Original Research

From the Fraser Health Authority, Surrey, BC, Canada (Drs. Yousefi and Paletta), and Catalyst Consulting Inc., Vancouver, BC, Canada (Elayne McIvor).

Objective: Despite the ongoing growth in the number of hospitalist programs in Canada, their impact on the quality of interprofessional communication, teamwork, and staff satisfaction is not well known. This study aimed to evaluate perceptions of frontline care providers and hospital managers about the impact of the implementation of 3 new hospitalist services on care quality, teamwork, and interprofessional communication.

Design: We used an online survey and semistructured interviews to evaluate respondents’ views on quality of interprofessional communication and collaboration, impact of the new services on quality of care, and overall staff satisfaction with the new inpatient care model.

Setting: Integrated Regional Health Authority in British Columbia, Canada.

Participants: Participants included hospital administrators, frontline care providers (across a range of professions), and hospital and community-based physicians.

Results: The majority of respondents reported high levels of satisfaction with their new hospital medicine services. They identified improvements in interprofessional collaboration and communication between hospitalists and other professionals, which were attributed to enhanced onsite presence of physicians. They also perceived improvements in quality of care and efficiency. On the other hand, they identified a number of challenges with the change process, and raised concerns about the impact of patient handoffs on care quality and efficiency.

Conclusion: Across 3 very different acute care settings, the implementation of a hospitalist service was widely perceived to have resulted in improved teamwork, quality of care, and interprofessional communication.

Keywords: hospital medicine; hospitalist; teamwork; interprofessional collaboration.

 

Over the past 2 decades, the hospitalist model has become prevalent in Canada and internationally.¹ Hospitalist care has been associated with improvements in efficiency and quality of care.^2-6 However, less is known about its impact on the quality of interprofessional communication, teamwork, and staff satisfaction. In a 2012 study of a specialized orthopedic facility in the Greater Toronto Area (GTA), Ontario, Webster et al found a pervasive perception among interviewees that the addition of a hospitalist resulted in improved patient safety, expedited transfers, enhanced communication with Primary Care Providers (PCPs), and better continuity of care.⁷ They also identified enhanced collaboration among providers since the addition of the hospitalist to the care team. In another study of 5 community hospitals in the GTA, Conn et al⁸ found that staff on General Internal Medicine wards where hospitalists worked described superior interprofessional collaboration, deeper interpersonal relationships between physicians and other care team members, and a higher sense of “team-based care.”

Fraser Health Authority (FH) is an integrated regional health system with one of the largest regional Hospital Medicine (HM) networks in Canada.⁹ Over the past 2 decades, FH has implemented a number of HM services in its acute care facilities across a range of small and large community and academic hospitals. More recently, 3 hospitalist services were implemented over a 2-year period: new HM services in a tertiary referral center (Site A, July 2016) and a small community hospital (Site B, December 2016), and reintroduction of a hospitalist service in a medium-sized community hospital (Site C, January 2017). This provided a unique opportunity to assess the impact of the implementation of the hospitalist model across a range of facilities. The main objectives of this evaluation were to understand the level of physician, nursing, allied staff, and hospital administration satisfaction with the new hospitalist model, as well as the perceived impact of the service on efficiency and quality of care. As such, FH engaged an external consultant (EM) to conduct a comprehensive evaluation of the introduction of its latest HM services.

Methods

Setting

Hospital medicine services are currently available in 10 of 12 acute care facilities within the FH system. The 3 sites described in this evaluation constitute the most recent sites where a hospitalist service was implemented.

Site A is a 272-bed tertiary referral center situated in a rapidly growing community. At the time of our evaluation, 21 Full Time Equivalent (FTE) hospitalists cared for an average of 126 patients, which constituted the majority of adult medical patients. Each day, 8 individuals rounded on admitted patients (average individual census: 16) with another person providing in-house, evening, and overnight coverage. An additional flexible shift during the early afternoon helped with Emergency Department (ED) admissions.

 

Site B is small, 45-bed community hospital in a semi-rural community. The hospitalist service began in December 2016, with 4 FTE hospitalists caring for an average of 28 patients daily. This constituted 2 hospitalists rounding daily on admitted patients, with on-call coverage provided from home.

Site C is a 188-bed community hospital with a hospitalist service initially introduced in 2005. In 2016, the program was disbanded and the site moved back to a primarily community-based model, in which family physicians in the community were invited to assume the care of hospitalized patients. However, the hospitalist program had to be reintroduced in January 2017 due to poor uptake among PCPs in the community. At the time of evaluation, 19 FTE hospitalists (with 7 hospitalists working daily) provided most responsible physician care to a daily census of 116 patients (average individual census: 16). The program also covered ED admissions in-house until midnight, with overnight call provided from home.

Approach

We adopted a utilization-focused evaluation approach to guide our investigation. In this approach, the assessment is deliberately planned and conducted in a way that it maximizes the likelihood that findings would be used by the organization to inform learning, adaptations, and decision-making.¹¹ To enable this, the evaluator identified the primary intended recipients and engaged them at the start of the evaluation process to understand the main intended uses of the project. Moreover, the evaluator ensured that these intended uses of the evaluation guided all other decisions made throughout the process.

We collected data using an online survey of the staff at the 3 facilities, complemented by a series of semistructured qualitative interviews with FH administrators and frontline providers.

Online survey

We conducted an open online survey of a broad range of stakeholders who worked in the 3 facilities. To develop the questionnaire, we searched our department’s archives for previous surveys conducted from 2001 to 2005. We also interviewed the regional HM program management team to identify priority areas and reached out to the local leadership of the 3 acute care facilities for their input and support of the project. We refined the survey through several iterations, seeking input from experts in the FH Department of Evaluation and Research. The final questionnaire contained 10 items, including a mix of closed- and open-ended questions (Appendix A).

 

To reach the target audience, we collaborated with each hospital’s local leadership as well as the Divisions of Family Practice (DFP) that support local community PCPs in each hospital community.¹⁰ Existing email lists were compiled to create a master electronic survey distribution list. The initial invitation and 3 subsequent reminders were disseminated to the following target groups: hospital physicians (both hospitalists and nonhospitalists), PCPs, nursing and other allied professionals, administrators, and DFP leadership.

The survey consent form, background information, questions, and online platform (SimpleSurvey, Montreal, QC) were approved by FH’s Privacy Department. All respondents were required to provide their consent and able to withdraw at any time. Survey responses were kept anonymous and confidential, with results captured automatically into a spreadsheet by the survey platform. As an incentive for participation, respondents had the opportunity to win 1 of 3 $100 Visa gift cards. Personal contact information provided for the prize draw was collected in a separate survey that could not link back to respondents’ answers. The survey was trialed several times by the evaluation team to address any technical challenges before dissemination to the targeted participants.

Qualitative interviews

We conducted semistructured interviews with a purposive sample of FH administrators and frontline providers (Appendix B). The interview questions broadly mirrored the survey but allowed for more in-depth exploration of constructs. Interviewees were recruited through email invitations to selected senior and mid-level local and regional administrators, asking interviewees to refer our team to other contacts, and inviting survey respondents to voluntarily participate in a follow-up interview. One of the authors (EM), a Credentialed Evaluator, conducted all the one-time interviews either in-person at the individual participant’s workplace or by telephone. She did not have pre-existing relationships with any of the interviewees. Interviews were recorded and transcribed for analysis. Interviewees were required to consent to participate and understood that they could withdraw at any point. They were not offered incentives to participate. Interviews were carried out until thematic saturation was reached.

Analysis

A content analysis approach was employed for all qualitative data, which included open-ended responses from the online survey and interview transcripts. One of the authors (EM) conducted the analysis. The following steps were followed in the inductive content analysis process: repeated reading of the raw data, generation of initial thematic codes, organizing and sorting codes into categories (ie, main vs subcategories), coding of all data, quantifying codes, and interpreting themes. When responding to open-ended questions, respondents often provided multiple answers per question. Each of the respondents’ answers were coded. In alignment with the inductive nature of the analysis process, themes emerged organically from the data rather than the researchers using preconceived theories and categories to code the text. This was achieved by postponing the review of relevant literature on the topic until after the analysis was complete and using an external evaluation consultant (with no prior relationship to FH and limited theoretical knowledge of the topic matter) to analyze the data. Descriptive statistics were run on quantitative data in SPSS (v.24, IBM, Armonk, NY). For survey responses to be included in the analysis, the respondents needed to indicate which site they worked at and were required to answer at least 1 other survey question. One interviewee was excluded from the analysis since they were not familiar with the hospitalist model at their site.

Ethics approval

The evaluation protocol was reviewed by FH Department of Evaluation and Research and was deemed exempt from formal research ethics review.

 

 

Results

A total of 377 individuals responded to the online survey between January 8 and February 28, 2018 (response rate 14%). The distribution of respondents generally reflected the size of the respective acute care facilities. Compared to the overall sampled population, fewer nurses participated in the survey (45% vs 64%) while the rate of participation for Unit Clerks (14% vs 16%) and allied professionals (12% vs 16%) were similar.

Out of the 45 people approached for an interview, a total of 38 were conducted from January 3 to March 5, 2018 (response rate 84%). The interviews lasted an average of 42 minutes. Interviewees represented a range of administrative and health professional roles (Figure 1). Some interviewees held multiple positions.

Satisfaction with HM service

Across all sites, survey respondents reported high levels of satisfaction with their respective HM services and identified positive impacts on their job satisfaction (Figure 2). Almost all interviewees similarly expressed high satisfaction levels with their HM services (95%; n = 36).

Perceptions of HM service performance

Survey respondents rated the strength of hospitalists’ interprofessional communication and collaboration with other physicians and with care teams. Roughly two-thirds reported that overall hospitalist communication was “good” or “very good.” We also asked participants to rate the frequency at which hospitalists met best practice expectations related to interprofessional teamwork. Across all sites, similar proportions of respondents (23% to 39%) reported that these best practices were met “most of the time” or “always” (Figure 3). Survey questions also assessed perceptions of respondents about the quality and safety of care provided by hospitalists (Figure 4).

Perceptions of the impact of the HM service postimplementation

The majority of survey respondents reported improvements in the quality of communication, professional relationships, and coordination of inpatient care at transition points after the implementation of the HM service (Figure 5). This was also reflected in interviews, where some indicated that it was easier to communicate with hospitalists due to their on-site presence, accessibility, and 24/7 availability (n = 21). They also described improved collaboration within the care teams (n = 7), and easier communication with hospitalists because they were approachable, willing, and receptive (n = 4).

 

We also asked the survey respondents to assess the impact of the new hospitalist model on different dimensions of care quality, including patient satisfaction, patient experience, efficiency, and overall quality of care (Figure 6). Findings were comparable across these dimensions, with roughly 50-60% of respondents noting positive changes compared to before the implementation of the programs. However, most interviewees identified both positive and negative effects in these areas. Positive impacts included hospitalist on-site presence leading to better accessibility and timeliness of care (n = 5), hospitalists providing continuity to patients/families by working for weeklong rotations (n = 6), hospitalists being particularly skilled at managing complex clinical presentations (n = 2), and hospitalists being able to spend more time with patients (n = 2). On the other hand, some interviewees noted that patients and families did not like seeing multiple doctors due to frequent handoffs between hospitalists (n = 12). They also raised concerns that hospitalists did not know patients’ histories or had relationships with them, potentially leading to longer length of stay and unnecessary investigations (n = 8).

Site-to-site ratings of satisfaction and performance

Survey respondents’ satisfaction and performance ratings varied substantially site-to-site. Across all areas assessed, ratings were consistently highest at Site B (the smallest institution in our evaluation and the most recent addition to the HM network in the health authority). These differences were statistically significant across all survey questions asked.

Discussion

Findings from this study provide insight into the experiences of frontline health care professionals and administrators with the implementation of new HM services across a range of small to large acute care facilities. They indicate that the majority of respondents reported high levels of satisfaction with their hospitalist services. Most also indicated that the service had resulted in improvements compared to prior inpatient care models.

Over half of the survey respondents, and the majority of interviewees, reported a positive impact on interprofessional communication and collaboration. This was largely attributed to enhanced accessibility and availability of hospitalists:

• "Being on-site lends itself to better communication because they’re accessible. Hospitalists always answer the phone, but the general practitioners (GP) don’t always since they may be with other patients." (Dietician, Site A)
• "A big strength is that we have physician presence on the unit all day during scheduled hours, which makes us more accessible to nurses and more able to follow up on patients that we have concerns about." (Physician Leader, Site B)

However, the ratings dropped substantially when they were asked to assess adherence to specific best practices of such communication and collaboration, such as participation in daily check-ins or attendance at team care rounds (Figure 3). Interdisciplinary clinical rounds have been identified as a tool to improve the effectiveness of care teams.¹² A number of elements have been identified as key components of effective rounds.¹³ Bedside rounds have also been found to enhance communication and teamwork.^14,15 In our study, the discrepancy between overall high levels of satisfaction with hospitalists’ communication/collaboration despite low scores on participation in more concrete activities may illustrate the importance of informal and ad hoc opportunities for interactions between hospitalists and other care providers that result from the enhanced presence of hospitalists on care units.⁸ Outside of formal rounds, hospitalists have the ability to interact with other care providers throughout their shifts. Prior studies have shown that hospitalists spend a significant portion of their time communicating with other care team members throughout their workdays.¹⁶ At the same time, the amount of time spent on communication should be balanced against the need for provision of direct care at the bedside. Future research should aim to identify the right balance between these competing priorities, and to understand the nature and quality of the communication between various care providers.

 

We also aimed to understand the perceptions of study participants about the impact of the HM service on quality of care. Survey participants not only expressed reasonable satisfaction with various aspects of hospitalists’ performance, but also described a positive impact on care quality after the implementation of their new services. This was also reflected in the interviews:

• "The clinical knowledge of the new hospitalists is far better. Some are internal medicine trained, so they bring better knowledge and skills. I feel comfortable that they can take patients and manage them. I wasn’t always comfortable with doing that in the past." (Emergency Physician, Site C)
• "Hospitalists are really familiar with acute care and how it works. They’ve become more familiar with the discharge planning system and thus know more about the resources available. And even something as simple as knowing which forms to use." (Dietician, Site A)

It must be noted that these observations should ideally be corroborated through a robust before-after analysis of various quality measures. While such an analysis was beyond the scope of our current project, we have previously demonstrated that across our network (including the 3 sites included in our evaluation) hospitalist care is associated with lower mortality and readmission rates.⁴ Our findings appear to confirm previous suggestions that hospitalists’ dedicated focus on inpatient care may allow them to develop enhanced skills in the management of common conditions in the acute care setting¹⁷ which can be perceived to be of value to other hospital-based care providers.

The issue of frequent handover among hospitalists was the most commonly identified challenge by both survey respondents and interviewees:

• "They’re very reluctant to discharge patients if it’s their first day with the patient. Even if the previous hospitalist said they were ready for discharge, the new doc wants to run all of their own tests before they feel comfortable. Maybe it’s a trust issue between hospitalists when they hand patients over. It’s also being personally liable for patients if you discharge them." (Patient Care Coordinator, Site A)
• "Communication is an issue. There’s lots of turnover in hospitalists. Relationships were closer with GPs because we had so much more interaction with particular individuals." (Hospitalist Physician Leader, Site A)

It must be noted that we conducted our evaluation in a relatively short time span (within 2 years) after the 3 services were implemented. Developing trust among a large number of hospitalists newly recruited to these programs can take time and may be a factor that can explain the reluctance of some to discharge patients after handoffs. However, concerns about discontinuity of care inherent in the hospitalist model are not new.^18,19 Better continuity has been associated with higher probability of patient discharges²⁰ and improved outcomes.²¹ To address this challenge, the hospitalist community has focused on defining the core competencies associated with high quality handovers,²² and deliberate efforts to improve the quality of handoffs through quality improvement methodologies.²³ Our study participants similarly identified these measures as potential solutions. Despite this, addressing hospitalist continuity of care remains a pressing challenge for the broader hospitalist community.²⁴

Our evaluation has a number of methodological limitations. First, the survey response rate was only 14%, which raises questions about nonresponse bias and the representativeness of the findings to the larger population of interest. While the distribution of respondents was largely similar to the overall sampled population, a number of factors may have impacted our response rate. For example, we were only able to distribute our survey to health care providers’ institutional email addresses. Moreover, while we provided incentives for participation and sent out a number of reminders, we solely relied on one communication modality (ie, electronic communication) and did not utilize other methods (such as posters, reminder at meetings, in-person invitations). Second, while the survey included a number of open-ended questions, many of these responses were at times brief and difficult to interpret and were not included in the analysis. Third, all data collected were self-reported. For example, we could not corroborate comments about participation in interdisciplinary rounds by objective measures such as attendance records or direct observation. Self-report data is subjective in nature and is vulnerable to a range of biases, such as social desirability bias.²⁵ Finally, patient satisfaction and experience with hospitalist care were not assessed by patients themselves. Ideally, standardized cross-site indicators should validate our patient-related results.

 

As mentioned above, hospitalist performance ratings varied substantially from site-to-site and were consistently higher at Site B (a small community hospital in a semi-rural area), followed by Site C (a medium-sized community hospital) and Site A (a tertiary referral center). The variability in program ratings and perceived hospitalist impacts between sites could be due to a variety of factors, such as the degree of change between the past and current models at each site, differences in hospitalist hiring processes, hospital size and culture, and differences in service design and operations. It may also be related to the timing of the introduction of the HM service, as Site B was the most recent site where the service was established. As such, there may be an element of recall bias behind the observed discrepancies. This highlights the importance of local context on respondent perceptions and suggests that our results may not be generalizable to other institutions with different attributes and characteristics.

Conclusion

Findings from this study have demonstrated that the recent hospitalist services in our health system have improved overall levels of interprofessional communication and teamwork, as well as perceptions of care quality among the majority of participants who reported high levels of satisfaction with their programs. Our findings further highlight the issue of frequent handovers among hospitalists as a pressing and ongoing challenge.

Corresponding Author: Vandad Yousefi, MD, CCFP, Past Regional Department Head – Hospital Medicine, Fraser Health Authority, Central City Tower, Suite 400, 13450 – 102nd Ave, Surrey, BC V3T 0H1; vandad.yousefi@fraserhealth.ca.

Financial disclosures: This project was funded by the Fraser Health Authority, which provided the funding for hiring of the external consultant to design, implement, and analyze the results of the evaluation program in collaboration with the Regional Hospitalist Program at Fraser Health.

From the Fraser Health Authority, Surrey, BC, Canada (Drs. Yousefi and Paletta), and Catalyst Consulting Inc., Vancouver, BC, Canada (Elayne McIvor).

Objective: Despite the ongoing growth in the number of hospitalist programs in Canada, their impact on the quality of interprofessional communication, teamwork, and staff satisfaction is not well known. This study aimed to evaluate perceptions of frontline care providers and hospital managers about the impact of the implementation of 3 new hospitalist services on care quality, teamwork, and interprofessional communication.

Design: We used an online survey and semistructured interviews to evaluate respondents’ views on quality of interprofessional communication and collaboration, impact of the new services on quality of care, and overall staff satisfaction with the new inpatient care model.

Setting: Integrated Regional Health Authority in British Columbia, Canada.

Participants: Participants included hospital administrators, frontline care providers (across a range of professions), and hospital and community-based physicians.

Results: The majority of respondents reported high levels of satisfaction with their new hospital medicine services. They identified improvements in interprofessional collaboration and communication between hospitalists and other professionals, which were attributed to enhanced onsite presence of physicians. They also perceived improvements in quality of care and efficiency. On the other hand, they identified a number of challenges with the change process, and raised concerns about the impact of patient handoffs on care quality and efficiency.

Conclusion: Across 3 very different acute care settings, the implementation of a hospitalist service was widely perceived to have resulted in improved teamwork, quality of care, and interprofessional communication.

Keywords: hospital medicine; hospitalist; teamwork; interprofessional collaboration.

 

Over the past 2 decades, the hospitalist model has become prevalent in Canada and internationally.¹ Hospitalist care has been associated with improvements in efficiency and quality of care.^2-6 However, less is known about its impact on the quality of interprofessional communication, teamwork, and staff satisfaction. In a 2012 study of a specialized orthopedic facility in the Greater Toronto Area (GTA), Ontario, Webster et al found a pervasive perception among interviewees that the addition of a hospitalist resulted in improved patient safety, expedited transfers, enhanced communication with Primary Care Providers (PCPs), and better continuity of care.⁷ They also identified enhanced collaboration among providers since the addition of the hospitalist to the care team. In another study of 5 community hospitals in the GTA, Conn et al⁸ found that staff on General Internal Medicine wards where hospitalists worked described superior interprofessional collaboration, deeper interpersonal relationships between physicians and other care team members, and a higher sense of “team-based care.”

Fraser Health Authority (FH) is an integrated regional health system with one of the largest regional Hospital Medicine (HM) networks in Canada.⁹ Over the past 2 decades, FH has implemented a number of HM services in its acute care facilities across a range of small and large community and academic hospitals. More recently, 3 hospitalist services were implemented over a 2-year period: new HM services in a tertiary referral center (Site A, July 2016) and a small community hospital (Site B, December 2016), and reintroduction of a hospitalist service in a medium-sized community hospital (Site C, January 2017). This provided a unique opportunity to assess the impact of the implementation of the hospitalist model across a range of facilities. The main objectives of this evaluation were to understand the level of physician, nursing, allied staff, and hospital administration satisfaction with the new hospitalist model, as well as the perceived impact of the service on efficiency and quality of care. As such, FH engaged an external consultant (EM) to conduct a comprehensive evaluation of the introduction of its latest HM services.

Methods

Setting

Hospital medicine services are currently available in 10 of 12 acute care facilities within the FH system. The 3 sites described in this evaluation constitute the most recent sites where a hospitalist service was implemented.

Site A is a 272-bed tertiary referral center situated in a rapidly growing community. At the time of our evaluation, 21 Full Time Equivalent (FTE) hospitalists cared for an average of 126 patients, which constituted the majority of adult medical patients. Each day, 8 individuals rounded on admitted patients (average individual census: 16) with another person providing in-house, evening, and overnight coverage. An additional flexible shift during the early afternoon helped with Emergency Department (ED) admissions.

 

Site B is small, 45-bed community hospital in a semi-rural community. The hospitalist service began in December 2016, with 4 FTE hospitalists caring for an average of 28 patients daily. This constituted 2 hospitalists rounding daily on admitted patients, with on-call coverage provided from home.

Site C is a 188-bed community hospital with a hospitalist service initially introduced in 2005. In 2016, the program was disbanded and the site moved back to a primarily community-based model, in which family physicians in the community were invited to assume the care of hospitalized patients. However, the hospitalist program had to be reintroduced in January 2017 due to poor uptake among PCPs in the community. At the time of evaluation, 19 FTE hospitalists (with 7 hospitalists working daily) provided most responsible physician care to a daily census of 116 patients (average individual census: 16). The program also covered ED admissions in-house until midnight, with overnight call provided from home.

Approach

We adopted a utilization-focused evaluation approach to guide our investigation. In this approach, the assessment is deliberately planned and conducted in a way that it maximizes the likelihood that findings would be used by the organization to inform learning, adaptations, and decision-making.¹¹ To enable this, the evaluator identified the primary intended recipients and engaged them at the start of the evaluation process to understand the main intended uses of the project. Moreover, the evaluator ensured that these intended uses of the evaluation guided all other decisions made throughout the process.

We collected data using an online survey of the staff at the 3 facilities, complemented by a series of semistructured qualitative interviews with FH administrators and frontline providers.

Online survey

We conducted an open online survey of a broad range of stakeholders who worked in the 3 facilities. To develop the questionnaire, we searched our department’s archives for previous surveys conducted from 2001 to 2005. We also interviewed the regional HM program management team to identify priority areas and reached out to the local leadership of the 3 acute care facilities for their input and support of the project. We refined the survey through several iterations, seeking input from experts in the FH Department of Evaluation and Research. The final questionnaire contained 10 items, including a mix of closed- and open-ended questions (Appendix A).

 

To reach the target audience, we collaborated with each hospital’s local leadership as well as the Divisions of Family Practice (DFP) that support local community PCPs in each hospital community.¹⁰ Existing email lists were compiled to create a master electronic survey distribution list. The initial invitation and 3 subsequent reminders were disseminated to the following target groups: hospital physicians (both hospitalists and nonhospitalists), PCPs, nursing and other allied professionals, administrators, and DFP leadership.

The survey consent form, background information, questions, and online platform (SimpleSurvey, Montreal, QC) were approved by FH’s Privacy Department. All respondents were required to provide their consent and able to withdraw at any time. Survey responses were kept anonymous and confidential, with results captured automatically into a spreadsheet by the survey platform. As an incentive for participation, respondents had the opportunity to win 1 of 3 $100 Visa gift cards. Personal contact information provided for the prize draw was collected in a separate survey that could not link back to respondents’ answers. The survey was trialed several times by the evaluation team to address any technical challenges before dissemination to the targeted participants.

Qualitative interviews

We conducted semistructured interviews with a purposive sample of FH administrators and frontline providers (Appendix B). The interview questions broadly mirrored the survey but allowed for more in-depth exploration of constructs. Interviewees were recruited through email invitations to selected senior and mid-level local and regional administrators, asking interviewees to refer our team to other contacts, and inviting survey respondents to voluntarily participate in a follow-up interview. One of the authors (EM), a Credentialed Evaluator, conducted all the one-time interviews either in-person at the individual participant’s workplace or by telephone. She did not have pre-existing relationships with any of the interviewees. Interviews were recorded and transcribed for analysis. Interviewees were required to consent to participate and understood that they could withdraw at any point. They were not offered incentives to participate. Interviews were carried out until thematic saturation was reached.

Analysis

A content analysis approach was employed for all qualitative data, which included open-ended responses from the online survey and interview transcripts. One of the authors (EM) conducted the analysis. The following steps were followed in the inductive content analysis process: repeated reading of the raw data, generation of initial thematic codes, organizing and sorting codes into categories (ie, main vs subcategories), coding of all data, quantifying codes, and interpreting themes. When responding to open-ended questions, respondents often provided multiple answers per question. Each of the respondents’ answers were coded. In alignment with the inductive nature of the analysis process, themes emerged organically from the data rather than the researchers using preconceived theories and categories to code the text. This was achieved by postponing the review of relevant literature on the topic until after the analysis was complete and using an external evaluation consultant (with no prior relationship to FH and limited theoretical knowledge of the topic matter) to analyze the data. Descriptive statistics were run on quantitative data in SPSS (v.24, IBM, Armonk, NY). For survey responses to be included in the analysis, the respondents needed to indicate which site they worked at and were required to answer at least 1 other survey question. One interviewee was excluded from the analysis since they were not familiar with the hospitalist model at their site.

Ethics approval

The evaluation protocol was reviewed by FH Department of Evaluation and Research and was deemed exempt from formal research ethics review.

 

 

Results

A total of 377 individuals responded to the online survey between January 8 and February 28, 2018 (response rate 14%). The distribution of respondents generally reflected the size of the respective acute care facilities. Compared to the overall sampled population, fewer nurses participated in the survey (45% vs 64%) while the rate of participation for Unit Clerks (14% vs 16%) and allied professionals (12% vs 16%) were similar.

Out of the 45 people approached for an interview, a total of 38 were conducted from January 3 to March 5, 2018 (response rate 84%). The interviews lasted an average of 42 minutes. Interviewees represented a range of administrative and health professional roles (Figure 1). Some interviewees held multiple positions.

Satisfaction with HM service

Across all sites, survey respondents reported high levels of satisfaction with their respective HM services and identified positive impacts on their job satisfaction (Figure 2). Almost all interviewees similarly expressed high satisfaction levels with their HM services (95%; n = 36).

Perceptions of HM service performance

Survey respondents rated the strength of hospitalists’ interprofessional communication and collaboration with other physicians and with care teams. Roughly two-thirds reported that overall hospitalist communication was “good” or “very good.” We also asked participants to rate the frequency at which hospitalists met best practice expectations related to interprofessional teamwork. Across all sites, similar proportions of respondents (23% to 39%) reported that these best practices were met “most of the time” or “always” (Figure 3). Survey questions also assessed perceptions of respondents about the quality and safety of care provided by hospitalists (Figure 4).

Perceptions of the impact of the HM service postimplementation

The majority of survey respondents reported improvements in the quality of communication, professional relationships, and coordination of inpatient care at transition points after the implementation of the HM service (Figure 5). This was also reflected in interviews, where some indicated that it was easier to communicate with hospitalists due to their on-site presence, accessibility, and 24/7 availability (n = 21). They also described improved collaboration within the care teams (n = 7), and easier communication with hospitalists because they were approachable, willing, and receptive (n = 4).

 

We also asked the survey respondents to assess the impact of the new hospitalist model on different dimensions of care quality, including patient satisfaction, patient experience, efficiency, and overall quality of care (Figure 6). Findings were comparable across these dimensions, with roughly 50-60% of respondents noting positive changes compared to before the implementation of the programs. However, most interviewees identified both positive and negative effects in these areas. Positive impacts included hospitalist on-site presence leading to better accessibility and timeliness of care (n = 5), hospitalists providing continuity to patients/families by working for weeklong rotations (n = 6), hospitalists being particularly skilled at managing complex clinical presentations (n = 2), and hospitalists being able to spend more time with patients (n = 2). On the other hand, some interviewees noted that patients and families did not like seeing multiple doctors due to frequent handoffs between hospitalists (n = 12). They also raised concerns that hospitalists did not know patients’ histories or had relationships with them, potentially leading to longer length of stay and unnecessary investigations (n = 8).

Site-to-site ratings of satisfaction and performance

Survey respondents’ satisfaction and performance ratings varied substantially site-to-site. Across all areas assessed, ratings were consistently highest at Site B (the smallest institution in our evaluation and the most recent addition to the HM network in the health authority). These differences were statistically significant across all survey questions asked.

Discussion

Findings from this study provide insight into the experiences of frontline health care professionals and administrators with the implementation of new HM services across a range of small to large acute care facilities. They indicate that the majority of respondents reported high levels of satisfaction with their hospitalist services. Most also indicated that the service had resulted in improvements compared to prior inpatient care models.

Over half of the survey respondents, and the majority of interviewees, reported a positive impact on interprofessional communication and collaboration. This was largely attributed to enhanced accessibility and availability of hospitalists:

• "Being on-site lends itself to better communication because they’re accessible. Hospitalists always answer the phone, but the general practitioners (GP) don’t always since they may be with other patients." (Dietician, Site A)
• "A big strength is that we have physician presence on the unit all day during scheduled hours, which makes us more accessible to nurses and more able to follow up on patients that we have concerns about." (Physician Leader, Site B)

However, the ratings dropped substantially when they were asked to assess adherence to specific best practices of such communication and collaboration, such as participation in daily check-ins or attendance at team care rounds (Figure 3). Interdisciplinary clinical rounds have been identified as a tool to improve the effectiveness of care teams.¹² A number of elements have been identified as key components of effective rounds.¹³ Bedside rounds have also been found to enhance communication and teamwork.^14,15 In our study, the discrepancy between overall high levels of satisfaction with hospitalists’ communication/collaboration despite low scores on participation in more concrete activities may illustrate the importance of informal and ad hoc opportunities for interactions between hospitalists and other care providers that result from the enhanced presence of hospitalists on care units.⁸ Outside of formal rounds, hospitalists have the ability to interact with other care providers throughout their shifts. Prior studies have shown that hospitalists spend a significant portion of their time communicating with other care team members throughout their workdays.¹⁶ At the same time, the amount of time spent on communication should be balanced against the need for provision of direct care at the bedside. Future research should aim to identify the right balance between these competing priorities, and to understand the nature and quality of the communication between various care providers.

 

We also aimed to understand the perceptions of study participants about the impact of the HM service on quality of care. Survey participants not only expressed reasonable satisfaction with various aspects of hospitalists’ performance, but also described a positive impact on care quality after the implementation of their new services. This was also reflected in the interviews:

• "The clinical knowledge of the new hospitalists is far better. Some are internal medicine trained, so they bring better knowledge and skills. I feel comfortable that they can take patients and manage them. I wasn’t always comfortable with doing that in the past." (Emergency Physician, Site C)
• "Hospitalists are really familiar with acute care and how it works. They’ve become more familiar with the discharge planning system and thus know more about the resources available. And even something as simple as knowing which forms to use." (Dietician, Site A)

It must be noted that these observations should ideally be corroborated through a robust before-after analysis of various quality measures. While such an analysis was beyond the scope of our current project, we have previously demonstrated that across our network (including the 3 sites included in our evaluation) hospitalist care is associated with lower mortality and readmission rates.⁴ Our findings appear to confirm previous suggestions that hospitalists’ dedicated focus on inpatient care may allow them to develop enhanced skills in the management of common conditions in the acute care setting¹⁷ which can be perceived to be of value to other hospital-based care providers.

The issue of frequent handover among hospitalists was the most commonly identified challenge by both survey respondents and interviewees:

• "They’re very reluctant to discharge patients if it’s their first day with the patient. Even if the previous hospitalist said they were ready for discharge, the new doc wants to run all of their own tests before they feel comfortable. Maybe it’s a trust issue between hospitalists when they hand patients over. It’s also being personally liable for patients if you discharge them." (Patient Care Coordinator, Site A)
• "Communication is an issue. There’s lots of turnover in hospitalists. Relationships were closer with GPs because we had so much more interaction with particular individuals." (Hospitalist Physician Leader, Site A)

It must be noted that we conducted our evaluation in a relatively short time span (within 2 years) after the 3 services were implemented. Developing trust among a large number of hospitalists newly recruited to these programs can take time and may be a factor that can explain the reluctance of some to discharge patients after handoffs. However, concerns about discontinuity of care inherent in the hospitalist model are not new.^18,19 Better continuity has been associated with higher probability of patient discharges²⁰ and improved outcomes.²¹ To address this challenge, the hospitalist community has focused on defining the core competencies associated with high quality handovers,²² and deliberate efforts to improve the quality of handoffs through quality improvement methodologies.²³ Our study participants similarly identified these measures as potential solutions. Despite this, addressing hospitalist continuity of care remains a pressing challenge for the broader hospitalist community.²⁴

Our evaluation has a number of methodological limitations. First, the survey response rate was only 14%, which raises questions about nonresponse bias and the representativeness of the findings to the larger population of interest. While the distribution of respondents was largely similar to the overall sampled population, a number of factors may have impacted our response rate. For example, we were only able to distribute our survey to health care providers’ institutional email addresses. Moreover, while we provided incentives for participation and sent out a number of reminders, we solely relied on one communication modality (ie, electronic communication) and did not utilize other methods (such as posters, reminder at meetings, in-person invitations). Second, while the survey included a number of open-ended questions, many of these responses were at times brief and difficult to interpret and were not included in the analysis. Third, all data collected were self-reported. For example, we could not corroborate comments about participation in interdisciplinary rounds by objective measures such as attendance records or direct observation. Self-report data is subjective in nature and is vulnerable to a range of biases, such as social desirability bias.²⁵ Finally, patient satisfaction and experience with hospitalist care were not assessed by patients themselves. Ideally, standardized cross-site indicators should validate our patient-related results.

 

As mentioned above, hospitalist performance ratings varied substantially from site-to-site and were consistently higher at Site B (a small community hospital in a semi-rural area), followed by Site C (a medium-sized community hospital) and Site A (a tertiary referral center). The variability in program ratings and perceived hospitalist impacts between sites could be due to a variety of factors, such as the degree of change between the past and current models at each site, differences in hospitalist hiring processes, hospital size and culture, and differences in service design and operations. It may also be related to the timing of the introduction of the HM service, as Site B was the most recent site where the service was established. As such, there may be an element of recall bias behind the observed discrepancies. This highlights the importance of local context on respondent perceptions and suggests that our results may not be generalizable to other institutions with different attributes and characteristics.

Conclusion

Findings from this study have demonstrated that the recent hospitalist services in our health system have improved overall levels of interprofessional communication and teamwork, as well as perceptions of care quality among the majority of participants who reported high levels of satisfaction with their programs. Our findings further highlight the issue of frequent handovers among hospitalists as a pressing and ongoing challenge.

Corresponding Author: Vandad Yousefi, MD, CCFP, Past Regional Department Head – Hospital Medicine, Fraser Health Authority, Central City Tower, Suite 400, 13450 – 102nd Ave, Surrey, BC V3T 0H1; vandad.yousefi@fraserhealth.ca.

Financial disclosures: This project was funded by the Fraser Health Authority, which provided the funding for hiring of the external consultant to design, implement, and analyze the results of the evaluation program in collaboration with the Regional Hospitalist Program at Fraser Health.

From the Fraser Health Authority, Surrey, BC, Canada (Drs. Yousefi and Paletta), and Catalyst Consulting Inc., Vancouver, BC, Canada (Elayne McIvor).

Objective: Despite the ongoing growth in the number of hospitalist programs in Canada, their impact on the quality of interprofessional communication, teamwork, and staff satisfaction is not well known. This study aimed to evaluate perceptions of frontline care providers and hospital managers about the impact of the implementation of 3 new hospitalist services on care quality, teamwork, and interprofessional communication.

Design: We used an online survey and semistructured interviews to evaluate respondents’ views on quality of interprofessional communication and collaboration, impact of the new services on quality of care, and overall staff satisfaction with the new inpatient care model.

Setting: Integrated Regional Health Authority in British Columbia, Canada.

Participants: Participants included hospital administrators, frontline care providers (across a range of professions), and hospital and community-based physicians.

Results: The majority of respondents reported high levels of satisfaction with their new hospital medicine services. They identified improvements in interprofessional collaboration and communication between hospitalists and other professionals, which were attributed to enhanced onsite presence of physicians. They also perceived improvements in quality of care and efficiency. On the other hand, they identified a number of challenges with the change process, and raised concerns about the impact of patient handoffs on care quality and efficiency.

Conclusion: Across 3 very different acute care settings, the implementation of a hospitalist service was widely perceived to have resulted in improved teamwork, quality of care, and interprofessional communication.

Keywords: hospital medicine; hospitalist; teamwork; interprofessional collaboration.

 

Over the past 2 decades, the hospitalist model has become prevalent in Canada and internationally.¹ Hospitalist care has been associated with improvements in efficiency and quality of care.^2-6 However, less is known about its impact on the quality of interprofessional communication, teamwork, and staff satisfaction. In a 2012 study of a specialized orthopedic facility in the Greater Toronto Area (GTA), Ontario, Webster et al found a pervasive perception among interviewees that the addition of a hospitalist resulted in improved patient safety, expedited transfers, enhanced communication with Primary Care Providers (PCPs), and better continuity of care.⁷ They also identified enhanced collaboration among providers since the addition of the hospitalist to the care team. In another study of 5 community hospitals in the GTA, Conn et al⁸ found that staff on General Internal Medicine wards where hospitalists worked described superior interprofessional collaboration, deeper interpersonal relationships between physicians and other care team members, and a higher sense of “team-based care.”

Fraser Health Authority (FH) is an integrated regional health system with one of the largest regional Hospital Medicine (HM) networks in Canada.⁹ Over the past 2 decades, FH has implemented a number of HM services in its acute care facilities across a range of small and large community and academic hospitals. More recently, 3 hospitalist services were implemented over a 2-year period: new HM services in a tertiary referral center (Site A, July 2016) and a small community hospital (Site B, December 2016), and reintroduction of a hospitalist service in a medium-sized community hospital (Site C, January 2017). This provided a unique opportunity to assess the impact of the implementation of the hospitalist model across a range of facilities. The main objectives of this evaluation were to understand the level of physician, nursing, allied staff, and hospital administration satisfaction with the new hospitalist model, as well as the perceived impact of the service on efficiency and quality of care. As such, FH engaged an external consultant (EM) to conduct a comprehensive evaluation of the introduction of its latest HM services.

Methods

Setting

Hospital medicine services are currently available in 10 of 12 acute care facilities within the FH system. The 3 sites described in this evaluation constitute the most recent sites where a hospitalist service was implemented.

Site A is a 272-bed tertiary referral center situated in a rapidly growing community. At the time of our evaluation, 21 Full Time Equivalent (FTE) hospitalists cared for an average of 126 patients, which constituted the majority of adult medical patients. Each day, 8 individuals rounded on admitted patients (average individual census: 16) with another person providing in-house, evening, and overnight coverage. An additional flexible shift during the early afternoon helped with Emergency Department (ED) admissions.

 

Site B is small, 45-bed community hospital in a semi-rural community. The hospitalist service began in December 2016, with 4 FTE hospitalists caring for an average of 28 patients daily. This constituted 2 hospitalists rounding daily on admitted patients, with on-call coverage provided from home.

Site C is a 188-bed community hospital with a hospitalist service initially introduced in 2005. In 2016, the program was disbanded and the site moved back to a primarily community-based model, in which family physicians in the community were invited to assume the care of hospitalized patients. However, the hospitalist program had to be reintroduced in January 2017 due to poor uptake among PCPs in the community. At the time of evaluation, 19 FTE hospitalists (with 7 hospitalists working daily) provided most responsible physician care to a daily census of 116 patients (average individual census: 16). The program also covered ED admissions in-house until midnight, with overnight call provided from home.

Approach

We adopted a utilization-focused evaluation approach to guide our investigation. In this approach, the assessment is deliberately planned and conducted in a way that it maximizes the likelihood that findings would be used by the organization to inform learning, adaptations, and decision-making.¹¹ To enable this, the evaluator identified the primary intended recipients and engaged them at the start of the evaluation process to understand the main intended uses of the project. Moreover, the evaluator ensured that these intended uses of the evaluation guided all other decisions made throughout the process.

We collected data using an online survey of the staff at the 3 facilities, complemented by a series of semistructured qualitative interviews with FH administrators and frontline providers.

Online survey

We conducted an open online survey of a broad range of stakeholders who worked in the 3 facilities. To develop the questionnaire, we searched our department’s archives for previous surveys conducted from 2001 to 2005. We also interviewed the regional HM program management team to identify priority areas and reached out to the local leadership of the 3 acute care facilities for their input and support of the project. We refined the survey through several iterations, seeking input from experts in the FH Department of Evaluation and Research. The final questionnaire contained 10 items, including a mix of closed- and open-ended questions (Appendix A).

 

To reach the target audience, we collaborated with each hospital’s local leadership as well as the Divisions of Family Practice (DFP) that support local community PCPs in each hospital community.¹⁰ Existing email lists were compiled to create a master electronic survey distribution list. The initial invitation and 3 subsequent reminders were disseminated to the following target groups: hospital physicians (both hospitalists and nonhospitalists), PCPs, nursing and other allied professionals, administrators, and DFP leadership.

The survey consent form, background information, questions, and online platform (SimpleSurvey, Montreal, QC) were approved by FH’s Privacy Department. All respondents were required to provide their consent and able to withdraw at any time. Survey responses were kept anonymous and confidential, with results captured automatically into a spreadsheet by the survey platform. As an incentive for participation, respondents had the opportunity to win 1 of 3 $100 Visa gift cards. Personal contact information provided for the prize draw was collected in a separate survey that could not link back to respondents’ answers. The survey was trialed several times by the evaluation team to address any technical challenges before dissemination to the targeted participants.

Qualitative interviews

We conducted semistructured interviews with a purposive sample of FH administrators and frontline providers (Appendix B). The interview questions broadly mirrored the survey but allowed for more in-depth exploration of constructs. Interviewees were recruited through email invitations to selected senior and mid-level local and regional administrators, asking interviewees to refer our team to other contacts, and inviting survey respondents to voluntarily participate in a follow-up interview. One of the authors (EM), a Credentialed Evaluator, conducted all the one-time interviews either in-person at the individual participant’s workplace or by telephone. She did not have pre-existing relationships with any of the interviewees. Interviews were recorded and transcribed for analysis. Interviewees were required to consent to participate and understood that they could withdraw at any point. They were not offered incentives to participate. Interviews were carried out until thematic saturation was reached.

Analysis

A content analysis approach was employed for all qualitative data, which included open-ended responses from the online survey and interview transcripts. One of the authors (EM) conducted the analysis. The following steps were followed in the inductive content analysis process: repeated reading of the raw data, generation of initial thematic codes, organizing and sorting codes into categories (ie, main vs subcategories), coding of all data, quantifying codes, and interpreting themes. When responding to open-ended questions, respondents often provided multiple answers per question. Each of the respondents’ answers were coded. In alignment with the inductive nature of the analysis process, themes emerged organically from the data rather than the researchers using preconceived theories and categories to code the text. This was achieved by postponing the review of relevant literature on the topic until after the analysis was complete and using an external evaluation consultant (with no prior relationship to FH and limited theoretical knowledge of the topic matter) to analyze the data. Descriptive statistics were run on quantitative data in SPSS (v.24, IBM, Armonk, NY). For survey responses to be included in the analysis, the respondents needed to indicate which site they worked at and were required to answer at least 1 other survey question. One interviewee was excluded from the analysis since they were not familiar with the hospitalist model at their site.

Ethics approval

The evaluation protocol was reviewed by FH Department of Evaluation and Research and was deemed exempt from formal research ethics review.

 

 

Results

A total of 377 individuals responded to the online survey between January 8 and February 28, 2018 (response rate 14%). The distribution of respondents generally reflected the size of the respective acute care facilities. Compared to the overall sampled population, fewer nurses participated in the survey (45% vs 64%) while the rate of participation for Unit Clerks (14% vs 16%) and allied professionals (12% vs 16%) were similar.

Out of the 45 people approached for an interview, a total of 38 were conducted from January 3 to March 5, 2018 (response rate 84%). The interviews lasted an average of 42 minutes. Interviewees represented a range of administrative and health professional roles (Figure 1). Some interviewees held multiple positions.

Satisfaction with HM service

Across all sites, survey respondents reported high levels of satisfaction with their respective HM services and identified positive impacts on their job satisfaction (Figure 2). Almost all interviewees similarly expressed high satisfaction levels with their HM services (95%; n = 36).

Perceptions of HM service performance

Survey respondents rated the strength of hospitalists’ interprofessional communication and collaboration with other physicians and with care teams. Roughly two-thirds reported that overall hospitalist communication was “good” or “very good.” We also asked participants to rate the frequency at which hospitalists met best practice expectations related to interprofessional teamwork. Across all sites, similar proportions of respondents (23% to 39%) reported that these best practices were met “most of the time” or “always” (Figure 3). Survey questions also assessed perceptions of respondents about the quality and safety of care provided by hospitalists (Figure 4).

Perceptions of the impact of the HM service postimplementation

The majority of survey respondents reported improvements in the quality of communication, professional relationships, and coordination of inpatient care at transition points after the implementation of the HM service (Figure 5). This was also reflected in interviews, where some indicated that it was easier to communicate with hospitalists due to their on-site presence, accessibility, and 24/7 availability (n = 21). They also described improved collaboration within the care teams (n = 7), and easier communication with hospitalists because they were approachable, willing, and receptive (n = 4).

 

We also asked the survey respondents to assess the impact of the new hospitalist model on different dimensions of care quality, including patient satisfaction, patient experience, efficiency, and overall quality of care (Figure 6). Findings were comparable across these dimensions, with roughly 50-60% of respondents noting positive changes compared to before the implementation of the programs. However, most interviewees identified both positive and negative effects in these areas. Positive impacts included hospitalist on-site presence leading to better accessibility and timeliness of care (n = 5), hospitalists providing continuity to patients/families by working for weeklong rotations (n = 6), hospitalists being particularly skilled at managing complex clinical presentations (n = 2), and hospitalists being able to spend more time with patients (n = 2). On the other hand, some interviewees noted that patients and families did not like seeing multiple doctors due to frequent handoffs between hospitalists (n = 12). They also raised concerns that hospitalists did not know patients’ histories or had relationships with them, potentially leading to longer length of stay and unnecessary investigations (n = 8).

Site-to-site ratings of satisfaction and performance

Survey respondents’ satisfaction and performance ratings varied substantially site-to-site. Across all areas assessed, ratings were consistently highest at Site B (the smallest institution in our evaluation and the most recent addition to the HM network in the health authority). These differences were statistically significant across all survey questions asked.

Discussion

Findings from this study provide insight into the experiences of frontline health care professionals and administrators with the implementation of new HM services across a range of small to large acute care facilities. They indicate that the majority of respondents reported high levels of satisfaction with their hospitalist services. Most also indicated that the service had resulted in improvements compared to prior inpatient care models.

Over half of the survey respondents, and the majority of interviewees, reported a positive impact on interprofessional communication and collaboration. This was largely attributed to enhanced accessibility and availability of hospitalists:

• "Being on-site lends itself to better communication because they’re accessible. Hospitalists always answer the phone, but the general practitioners (GP) don’t always since they may be with other patients." (Dietician, Site A)
• "A big strength is that we have physician presence on the unit all day during scheduled hours, which makes us more accessible to nurses and more able to follow up on patients that we have concerns about." (Physician Leader, Site B)

However, the ratings dropped substantially when they were asked to assess adherence to specific best practices of such communication and collaboration, such as participation in daily check-ins or attendance at team care rounds (Figure 3). Interdisciplinary clinical rounds have been identified as a tool to improve the effectiveness of care teams.¹² A number of elements have been identified as key components of effective rounds.¹³ Bedside rounds have also been found to enhance communication and teamwork.^14,15 In our study, the discrepancy between overall high levels of satisfaction with hospitalists’ communication/collaboration despite low scores on participation in more concrete activities may illustrate the importance of informal and ad hoc opportunities for interactions between hospitalists and other care providers that result from the enhanced presence of hospitalists on care units.⁸ Outside of formal rounds, hospitalists have the ability to interact with other care providers throughout their shifts. Prior studies have shown that hospitalists spend a significant portion of their time communicating with other care team members throughout their workdays.¹⁶ At the same time, the amount of time spent on communication should be balanced against the need for provision of direct care at the bedside. Future research should aim to identify the right balance between these competing priorities, and to understand the nature and quality of the communication between various care providers.

 

We also aimed to understand the perceptions of study participants about the impact of the HM service on quality of care. Survey participants not only expressed reasonable satisfaction with various aspects of hospitalists’ performance, but also described a positive impact on care quality after the implementation of their new services. This was also reflected in the interviews:

• "The clinical knowledge of the new hospitalists is far better. Some are internal medicine trained, so they bring better knowledge and skills. I feel comfortable that they can take patients and manage them. I wasn’t always comfortable with doing that in the past." (Emergency Physician, Site C)
• "Hospitalists are really familiar with acute care and how it works. They’ve become more familiar with the discharge planning system and thus know more about the resources available. And even something as simple as knowing which forms to use." (Dietician, Site A)

It must be noted that these observations should ideally be corroborated through a robust before-after analysis of various quality measures. While such an analysis was beyond the scope of our current project, we have previously demonstrated that across our network (including the 3 sites included in our evaluation) hospitalist care is associated with lower mortality and readmission rates.⁴ Our findings appear to confirm previous suggestions that hospitalists’ dedicated focus on inpatient care may allow them to develop enhanced skills in the management of common conditions in the acute care setting¹⁷ which can be perceived to be of value to other hospital-based care providers.

The issue of frequent handover among hospitalists was the most commonly identified challenge by both survey respondents and interviewees:

• "They’re very reluctant to discharge patients if it’s their first day with the patient. Even if the previous hospitalist said they were ready for discharge, the new doc wants to run all of their own tests before they feel comfortable. Maybe it’s a trust issue between hospitalists when they hand patients over. It’s also being personally liable for patients if you discharge them." (Patient Care Coordinator, Site A)
• "Communication is an issue. There’s lots of turnover in hospitalists. Relationships were closer with GPs because we had so much more interaction with particular individuals." (Hospitalist Physician Leader, Site A)

It must be noted that we conducted our evaluation in a relatively short time span (within 2 years) after the 3 services were implemented. Developing trust among a large number of hospitalists newly recruited to these programs can take time and may be a factor that can explain the reluctance of some to discharge patients after handoffs. However, concerns about discontinuity of care inherent in the hospitalist model are not new.^18,19 Better continuity has been associated with higher probability of patient discharges²⁰ and improved outcomes.²¹ To address this challenge, the hospitalist community has focused on defining the core competencies associated with high quality handovers,²² and deliberate efforts to improve the quality of handoffs through quality improvement methodologies.²³ Our study participants similarly identified these measures as potential solutions. Despite this, addressing hospitalist continuity of care remains a pressing challenge for the broader hospitalist community.²⁴

Our evaluation has a number of methodological limitations. First, the survey response rate was only 14%, which raises questions about nonresponse bias and the representativeness of the findings to the larger population of interest. While the distribution of respondents was largely similar to the overall sampled population, a number of factors may have impacted our response rate. For example, we were only able to distribute our survey to health care providers’ institutional email addresses. Moreover, while we provided incentives for participation and sent out a number of reminders, we solely relied on one communication modality (ie, electronic communication) and did not utilize other methods (such as posters, reminder at meetings, in-person invitations). Second, while the survey included a number of open-ended questions, many of these responses were at times brief and difficult to interpret and were not included in the analysis. Third, all data collected were self-reported. For example, we could not corroborate comments about participation in interdisciplinary rounds by objective measures such as attendance records or direct observation. Self-report data is subjective in nature and is vulnerable to a range of biases, such as social desirability bias.²⁵ Finally, patient satisfaction and experience with hospitalist care were not assessed by patients themselves. Ideally, standardized cross-site indicators should validate our patient-related results.

 

As mentioned above, hospitalist performance ratings varied substantially from site-to-site and were consistently higher at Site B (a small community hospital in a semi-rural area), followed by Site C (a medium-sized community hospital) and Site A (a tertiary referral center). The variability in program ratings and perceived hospitalist impacts between sites could be due to a variety of factors, such as the degree of change between the past and current models at each site, differences in hospitalist hiring processes, hospital size and culture, and differences in service design and operations. It may also be related to the timing of the introduction of the HM service, as Site B was the most recent site where the service was established. As such, there may be an element of recall bias behind the observed discrepancies. This highlights the importance of local context on respondent perceptions and suggests that our results may not be generalizable to other institutions with different attributes and characteristics.

Conclusion

Findings from this study have demonstrated that the recent hospitalist services in our health system have improved overall levels of interprofessional communication and teamwork, as well as perceptions of care quality among the majority of participants who reported high levels of satisfaction with their programs. Our findings further highlight the issue of frequent handovers among hospitalists as a pressing and ongoing challenge.

Corresponding Author: Vandad Yousefi, MD, CCFP, Past Regional Department Head – Hospital Medicine, Fraser Health Authority, Central City Tower, Suite 400, 13450 – 102nd Ave, Surrey, BC V3T 0H1; vandad.yousefi@fraserhealth.ca.

Financial disclosures: This project was funded by the Fraser Health Authority, which provided the funding for hiring of the external consultant to design, implement, and analyze the results of the evaluation program in collaboration with the Regional Hospitalist Program at Fraser Health.

From Stamford Hospital, Stamford, CT.

Objective: This single-center, quasi-experimental study of adult patients admitted or transferred to a medical step-down unit with alcohol withdrawal diagnoses sought to determine if symptom–triggered therapy (STT) is more effective than combined fixed-scheduled (FS) and STT in severe alcohol withdrawal.

Methods: In the preintervention group (72 episodes), patients were treated with FS and STT based on physician preference. In the postintervention group (69 episodes), providers were required to utilize only the STT protocol.

Results: Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001) and a decrease in average length of stay from 8.0 days to 5.1 days (P < .001). Secondary safety measures included a reduction in the proportion of patients who experienced delirium tremens from 47.5% to 22.5% (P < .001), and a reduction in intubation rates from 13.8% to 1.3% (P = .003).

Conclusion: The STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients requires frequent monitoring to assess withdrawal severity combined with appropriate and timely dosing of benzodiazepines.

Keywords: alcohol withdrawal delirium; alcohol withdrawal syndrome; treatment protocol; benzodiazepine; lorazepam.

Management of severe alcohol withdrawal and delirium tremens (DT) is challenging and requires significant resources, including close monitoring and intensive treatment, frequently in an intensive care unit (ICU).¹ Early diagnosis and therapeutic intervention are important to limit potential complications associated with DT.² Benzodiazepines are first-line therapeutic agents, but the definition of optimal use and dosing regimens has been limited, due to a lack of randomized controlled trials. In lower acuity patients admitted to a detoxification unit, systematic symptom–triggered benzodiazepine therapy (STT) has been established to be more effective than fixed-schedule (FS) dosing.^3-5 Patients treated using STT require lower total benzodiazepine dosing and achieve shorter treatment durations. However, in higher-acuity patients admitted to general medical services, analyses have not shown an advantage of STT over combined FS and STT.⁶

 

 

Methods

The purpose of this study was to determine whether implementation of STT is more effective than FS dosing combined with episodic STT in the management of hospitalized high-acuity alcohol withdrawal patients. We conducted a preintervention and postintervention quasi-experimental study in the step-down unit (SDU) of a 305-bed community teaching hospital. The study population consisted of adult inpatients 18 years or older admitted or transferred to the 12-bed SDU with alcohol withdrawal, as defined by primary or secondary International Classification of Diseases, Tenth Revision diagnoses. SDU admission criteria included patients with prior DT or those who had received multiple doses of benzodiazepines in the emergency department. In-hospital transfer to the SDU was at the physician’s discretion, if the patient required escalating doses of benzodiazepines or the use of increasing resources, such as those for behavioral emergencies. The majority of patients admitted or transferred to the SDU were assigned to medical house staff teams under hospitalist supervision, and, on occasion, under community physicians. The nurse-to-patient ratio in the SDU was 1:3.

Study groups

The preintervention group consisted of 80 successive treatment episodes involving patients admitted or transferred to the SDU from December 2, 2015, to July 1, 2017. Patients were treated based upon physician preference, consisting of a scheduled dosing regimen with additional doses as needed. The postintervention group included 80 successive treatment episodes involving patients admitted or transferred to the SDU from October 1, 2017, to March 23, 2019. The STT protocol was used in all patients in the postintervention group.

In the preintervention group, fixed, scheduled doses of lorazepam or chlordiazepoxide and as-needed lorazepam were prescribed and adjusted based upon physician judgment. Monitoring of symptom severity was scored using the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA-Ar). Benzodiazepine dosing occurred if the CIWA-Ar score had increased 2 or more points from the last score.

In the postintervention group, the STT protocol included the creation of a standardized physician order set for benzodiazepine “sliding scale” administration. The STT protocol allowed for escalating doses for higher withdrawal scores. Symptom severity was scored using MINDS (Minnesota Detoxification Scale) criteria.¹ Lorazepam as-needed dosing was based upon MINDS scores. A MINDS score less than 10 resulted in no medication, MINDS 10-12 required 2 mg, MINDS 13-16 required 4 mg, MINDS 17-19 required 6 mg, and MINDS 20 required 8 mg and a call to the physician. Transfer to the ICU was recommended if the MINDS score was ≥ 20 for 3 consecutive hours. Monitoring intervals occurred more frequently at 30 minutes unless the MINDS score was less than 10. After 7 days, the MINDS protocol was recommended to be discontinued, as the patient might have had iatrogenic delirium.

The STT protocol was introduced during a didactic session for the hospitalists and a separate session for internal medicine and family residents. Each registered nurse working in the SDU was trained in the use of the STT protocol and MINDS during nursing huddles.

 

Patients were excluded from evaluation if they were transferred to the SDU after 7 or more days in the hospital, if they had stayed in the hospital more than 30 days, were chronically on benzodiazepine therapy (to avoid confounding withdrawal symptoms), or if they left the hospital against medical advice (AMA). To avoid bias in the results, the patients with early discontinuation of treatment were included in analyses of secondary outcomes, thus resulting in all 80 episodes analyzed.

Measures and data

The primary outcome measure was benzodiazepine dose intensity, expressed in total lorazepam-equivalents. Secondary measures included average length of stay (including general medical, surgical, and ICU days), seizure incidence, DT incidence, sitter use, behavioral emergency responses, rates of leaving AMA, intubation, transfer to the ICU, and death.

Benzodiazepine dosing and length of stay were obtained from the data warehouse of the hospital’s electronic health record (EHR; Meditech). Benzodiazepine dosing was expressed in total lorazepam-equivalents, with conversion as follows: lorazepam orally and intravenously 1 mg = chlordiazepoxide 25 mg = diazepam 5 mg. All other measures were obtained from chart review of the patients’ EMR entries. The Stamford Hospital Institutional Review Board approved this study.

Analysis

Data analyses for this study were performed using SPSS version 25.0 (IBM). Categorical data were reported as frequency (count) and percent within category. Continuous data were reported as mean (SD). Categorical data were analyzed using χ² analysis; continuous data were analyzed using t-tests. A P value of .05 was considered significant for each analysis.

Results

During the preintervention period, 72 episodes (58 patients) met inclusion criteria, and 69 episodes (55 patients) met inclusion criteria during the postintervention period. Ten patients were represented in both groups. Eight preintervention episodes were excluded from the primary analysis because the patient left AMA. Eleven postintervention episodes were excluded: 9 due to patients leaving AMA, 1 due to chronic benzodiazepine usage, and 1 due to transfer to the SDU unit after 7 days. Baseline characteristics and medication use profiles of the preintervention and postintervention groups are summarized in Table 1.

 

Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001), as shown in Table 2. Average length of stay decreased from 8.0 days to 5.1 days (P < .001). Secondary safety measures were notable for a reduction in DT incidence, from 47.5% to 22.5% (P < .001), and lower rates of intubation, from 13.8% to 1.3% (P = .003). Seven-day readmission rates were 0% preintervention and 1.4% postintervention.

Discussion

We found that hospitalized patients with severe alcohol withdrawal treated with STT required fewer benzodiazepines and had a lower length of stay than patients treated with a conventional combined STT and FS regimen. Implementation of the change from the STT and FS approach to the STT approach in the SDU resulted in concerns that waiting for symptoms to appear could result in more severe withdrawal and prolonged treatment.³ To address this, the intervention included monitoring and dosing every 30 minutes, as compared to monitoring and dosing every 1 hour preintervention. In addition, a sliding-scale approach to match alcohol withdrawal score with dosage was employed in postintervention patients.

Employment of the STT protocol also resulted in decreased complications, including lower rates of DT and transfer to the ICU. This new intervention resulted in significantly decreased time required to control severe symptoms. In the preintervention phase, if a patient’s symptoms escalated despite administration of the as-needed dose of benzodiazepine, there was often a delay in administration of additional doses due to the time needed for nurses to reach a physician and subsequent placement of a new order. In the postintervention phase, the STT protocol allowed nursing staff to give benzodiazepines without delay when needed. We believe this reduced the number of calls by nursing staff to physicians requesting additional medications, and that this improved teamwork when managing these patients.

As part of the intervention, a decision was made to use the MINDS scale rather than the CIWA-Ar scale to assess withdrawal severity. This was because the CIWA-Ar has only been validated in patients with uncomplicated alcohol withdrawal syndrome and has not been researched extensively in patients requiring ICU-level care.¹ MINDS assessment has proven to be reliable and reflects severity of withdrawal. Furthermore, MINDS requires less time to administer—3 to 5 minutes vs 5 to 15 minutes for the CIWA-Ar scale. CIWA-Ar, unlike MINDS, requires subjective input from the patient, which is less reliable for higher acuity patients. Our study is unique in that it focused on high-acuity patients and it showed both a significant reduction in quantity of benzodiazepines prescribed and length of stay. Previous studies on lower acuity patients in detoxification units have confirmed that STT is more effective than a FS approach.^3-5 In patients of higher acuity, STT has not proven to be superior.

A key lesson learned was the need for proper education of nursing staff. Concurrent nursing audits were necessary to ensure that scoring was performed in an accurate and timely manner. In addition, it was challenging to predict which patients might develop DTs versus those requiring a brief inpatient stay. While there was initial concern that an STT protocol could result in underdosing, we found that patients had fewer DT episodes and fewer ICU transfers.

 

This study had several limitations. These include a relatively small sample size and the data being less recent. As there has been no intervening change to the therapeutic paradigm of DT treatment, the findings remain pertinent to the present time. The study employed a simple pre/post design and was conducted in a single setting. We are not aware of any temporal or local trends likely to influence these results. Admissions and transfers to the SDU for severe alcohol withdrawal were based on physician discretion. However, patient characteristics in both groups were similar (Table 1). We note that the postintervention STT protocol allowed for more frequent benzodiazepine dosing, though benzodiazepine use did decrease. Different alcohol withdrawal scores (MINDS vs. CIWA-Ar) were used for postintervention and preintervention, although previous research has shown that MINDS and CIWA-Ar scores correlate well.⁷ Finally, some patients of higher acuity and complexity were excluded, potentially limiting the generalizability of our results.

Conclusion

Our STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients also requires frequent monitoring using the MINDS scale, integrated with benzodiazepine sliding-scale dosing to match symptom severity. This bundled approach resulted in a significant reduction of benzodiazepine usage and reduced length of stay. Timely treatment of these patients also reduced the percent of patients developing DTs, and reduced intubation rates and transfers to the ICU. Further studies may be warranted at other sites to confirm the effectiveness of this STT protocol.

Corresponding author: Paul W. Huang, MD, Stamford Hospital, One Hospital Plaza, PO Box 9317, Stamford, CT 06904; phuang@stamhealth.org.

Financial disclosures: None.

From Stamford Hospital, Stamford, CT.

Objective: This single-center, quasi-experimental study of adult patients admitted or transferred to a medical step-down unit with alcohol withdrawal diagnoses sought to determine if symptom–triggered therapy (STT) is more effective than combined fixed-scheduled (FS) and STT in severe alcohol withdrawal.

Methods: In the preintervention group (72 episodes), patients were treated with FS and STT based on physician preference. In the postintervention group (69 episodes), providers were required to utilize only the STT protocol.

Results: Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001) and a decrease in average length of stay from 8.0 days to 5.1 days (P < .001). Secondary safety measures included a reduction in the proportion of patients who experienced delirium tremens from 47.5% to 22.5% (P < .001), and a reduction in intubation rates from 13.8% to 1.3% (P = .003).

Conclusion: The STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients requires frequent monitoring to assess withdrawal severity combined with appropriate and timely dosing of benzodiazepines.

Keywords: alcohol withdrawal delirium; alcohol withdrawal syndrome; treatment protocol; benzodiazepine; lorazepam.

Management of severe alcohol withdrawal and delirium tremens (DT) is challenging and requires significant resources, including close monitoring and intensive treatment, frequently in an intensive care unit (ICU).¹ Early diagnosis and therapeutic intervention are important to limit potential complications associated with DT.² Benzodiazepines are first-line therapeutic agents, but the definition of optimal use and dosing regimens has been limited, due to a lack of randomized controlled trials. In lower acuity patients admitted to a detoxification unit, systematic symptom–triggered benzodiazepine therapy (STT) has been established to be more effective than fixed-schedule (FS) dosing.^3-5 Patients treated using STT require lower total benzodiazepine dosing and achieve shorter treatment durations. However, in higher-acuity patients admitted to general medical services, analyses have not shown an advantage of STT over combined FS and STT.⁶

 

 

Methods

The purpose of this study was to determine whether implementation of STT is more effective than FS dosing combined with episodic STT in the management of hospitalized high-acuity alcohol withdrawal patients. We conducted a preintervention and postintervention quasi-experimental study in the step-down unit (SDU) of a 305-bed community teaching hospital. The study population consisted of adult inpatients 18 years or older admitted or transferred to the 12-bed SDU with alcohol withdrawal, as defined by primary or secondary International Classification of Diseases, Tenth Revision diagnoses. SDU admission criteria included patients with prior DT or those who had received multiple doses of benzodiazepines in the emergency department. In-hospital transfer to the SDU was at the physician’s discretion, if the patient required escalating doses of benzodiazepines or the use of increasing resources, such as those for behavioral emergencies. The majority of patients admitted or transferred to the SDU were assigned to medical house staff teams under hospitalist supervision, and, on occasion, under community physicians. The nurse-to-patient ratio in the SDU was 1:3.

Study groups

The preintervention group consisted of 80 successive treatment episodes involving patients admitted or transferred to the SDU from December 2, 2015, to July 1, 2017. Patients were treated based upon physician preference, consisting of a scheduled dosing regimen with additional doses as needed. The postintervention group included 80 successive treatment episodes involving patients admitted or transferred to the SDU from October 1, 2017, to March 23, 2019. The STT protocol was used in all patients in the postintervention group.

In the preintervention group, fixed, scheduled doses of lorazepam or chlordiazepoxide and as-needed lorazepam were prescribed and adjusted based upon physician judgment. Monitoring of symptom severity was scored using the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA-Ar). Benzodiazepine dosing occurred if the CIWA-Ar score had increased 2 or more points from the last score.

In the postintervention group, the STT protocol included the creation of a standardized physician order set for benzodiazepine “sliding scale” administration. The STT protocol allowed for escalating doses for higher withdrawal scores. Symptom severity was scored using MINDS (Minnesota Detoxification Scale) criteria.¹ Lorazepam as-needed dosing was based upon MINDS scores. A MINDS score less than 10 resulted in no medication, MINDS 10-12 required 2 mg, MINDS 13-16 required 4 mg, MINDS 17-19 required 6 mg, and MINDS 20 required 8 mg and a call to the physician. Transfer to the ICU was recommended if the MINDS score was ≥ 20 for 3 consecutive hours. Monitoring intervals occurred more frequently at 30 minutes unless the MINDS score was less than 10. After 7 days, the MINDS protocol was recommended to be discontinued, as the patient might have had iatrogenic delirium.

The STT protocol was introduced during a didactic session for the hospitalists and a separate session for internal medicine and family residents. Each registered nurse working in the SDU was trained in the use of the STT protocol and MINDS during nursing huddles.

 

Patients were excluded from evaluation if they were transferred to the SDU after 7 or more days in the hospital, if they had stayed in the hospital more than 30 days, were chronically on benzodiazepine therapy (to avoid confounding withdrawal symptoms), or if they left the hospital against medical advice (AMA). To avoid bias in the results, the patients with early discontinuation of treatment were included in analyses of secondary outcomes, thus resulting in all 80 episodes analyzed.

Measures and data

The primary outcome measure was benzodiazepine dose intensity, expressed in total lorazepam-equivalents. Secondary measures included average length of stay (including general medical, surgical, and ICU days), seizure incidence, DT incidence, sitter use, behavioral emergency responses, rates of leaving AMA, intubation, transfer to the ICU, and death.

Benzodiazepine dosing and length of stay were obtained from the data warehouse of the hospital’s electronic health record (EHR; Meditech). Benzodiazepine dosing was expressed in total lorazepam-equivalents, with conversion as follows: lorazepam orally and intravenously 1 mg = chlordiazepoxide 25 mg = diazepam 5 mg. All other measures were obtained from chart review of the patients’ EMR entries. The Stamford Hospital Institutional Review Board approved this study.

Analysis

Data analyses for this study were performed using SPSS version 25.0 (IBM). Categorical data were reported as frequency (count) and percent within category. Continuous data were reported as mean (SD). Categorical data were analyzed using χ² analysis; continuous data were analyzed using t-tests. A P value of .05 was considered significant for each analysis.

Results

During the preintervention period, 72 episodes (58 patients) met inclusion criteria, and 69 episodes (55 patients) met inclusion criteria during the postintervention period. Ten patients were represented in both groups. Eight preintervention episodes were excluded from the primary analysis because the patient left AMA. Eleven postintervention episodes were excluded: 9 due to patients leaving AMA, 1 due to chronic benzodiazepine usage, and 1 due to transfer to the SDU unit after 7 days. Baseline characteristics and medication use profiles of the preintervention and postintervention groups are summarized in Table 1.

 

Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001), as shown in Table 2. Average length of stay decreased from 8.0 days to 5.1 days (P < .001). Secondary safety measures were notable for a reduction in DT incidence, from 47.5% to 22.5% (P < .001), and lower rates of intubation, from 13.8% to 1.3% (P = .003). Seven-day readmission rates were 0% preintervention and 1.4% postintervention.

Discussion

We found that hospitalized patients with severe alcohol withdrawal treated with STT required fewer benzodiazepines and had a lower length of stay than patients treated with a conventional combined STT and FS regimen. Implementation of the change from the STT and FS approach to the STT approach in the SDU resulted in concerns that waiting for symptoms to appear could result in more severe withdrawal and prolonged treatment.³ To address this, the intervention included monitoring and dosing every 30 minutes, as compared to monitoring and dosing every 1 hour preintervention. In addition, a sliding-scale approach to match alcohol withdrawal score with dosage was employed in postintervention patients.

Employment of the STT protocol also resulted in decreased complications, including lower rates of DT and transfer to the ICU. This new intervention resulted in significantly decreased time required to control severe symptoms. In the preintervention phase, if a patient’s symptoms escalated despite administration of the as-needed dose of benzodiazepine, there was often a delay in administration of additional doses due to the time needed for nurses to reach a physician and subsequent placement of a new order. In the postintervention phase, the STT protocol allowed nursing staff to give benzodiazepines without delay when needed. We believe this reduced the number of calls by nursing staff to physicians requesting additional medications, and that this improved teamwork when managing these patients.

As part of the intervention, a decision was made to use the MINDS scale rather than the CIWA-Ar scale to assess withdrawal severity. This was because the CIWA-Ar has only been validated in patients with uncomplicated alcohol withdrawal syndrome and has not been researched extensively in patients requiring ICU-level care.¹ MINDS assessment has proven to be reliable and reflects severity of withdrawal. Furthermore, MINDS requires less time to administer—3 to 5 minutes vs 5 to 15 minutes for the CIWA-Ar scale. CIWA-Ar, unlike MINDS, requires subjective input from the patient, which is less reliable for higher acuity patients. Our study is unique in that it focused on high-acuity patients and it showed both a significant reduction in quantity of benzodiazepines prescribed and length of stay. Previous studies on lower acuity patients in detoxification units have confirmed that STT is more effective than a FS approach.^3-5 In patients of higher acuity, STT has not proven to be superior.

A key lesson learned was the need for proper education of nursing staff. Concurrent nursing audits were necessary to ensure that scoring was performed in an accurate and timely manner. In addition, it was challenging to predict which patients might develop DTs versus those requiring a brief inpatient stay. While there was initial concern that an STT protocol could result in underdosing, we found that patients had fewer DT episodes and fewer ICU transfers.

 

This study had several limitations. These include a relatively small sample size and the data being less recent. As there has been no intervening change to the therapeutic paradigm of DT treatment, the findings remain pertinent to the present time. The study employed a simple pre/post design and was conducted in a single setting. We are not aware of any temporal or local trends likely to influence these results. Admissions and transfers to the SDU for severe alcohol withdrawal were based on physician discretion. However, patient characteristics in both groups were similar (Table 1). We note that the postintervention STT protocol allowed for more frequent benzodiazepine dosing, though benzodiazepine use did decrease. Different alcohol withdrawal scores (MINDS vs. CIWA-Ar) were used for postintervention and preintervention, although previous research has shown that MINDS and CIWA-Ar scores correlate well.⁷ Finally, some patients of higher acuity and complexity were excluded, potentially limiting the generalizability of our results.

Conclusion

Our STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients also requires frequent monitoring using the MINDS scale, integrated with benzodiazepine sliding-scale dosing to match symptom severity. This bundled approach resulted in a significant reduction of benzodiazepine usage and reduced length of stay. Timely treatment of these patients also reduced the percent of patients developing DTs, and reduced intubation rates and transfers to the ICU. Further studies may be warranted at other sites to confirm the effectiveness of this STT protocol.

Corresponding author: Paul W. Huang, MD, Stamford Hospital, One Hospital Plaza, PO Box 9317, Stamford, CT 06904; phuang@stamhealth.org.

Financial disclosures: None.

From Stamford Hospital, Stamford, CT.

Objective: This single-center, quasi-experimental study of adult patients admitted or transferred to a medical step-down unit with alcohol withdrawal diagnoses sought to determine if symptom–triggered therapy (STT) is more effective than combined fixed-scheduled (FS) and STT in severe alcohol withdrawal.

Methods: In the preintervention group (72 episodes), patients were treated with FS and STT based on physician preference. In the postintervention group (69 episodes), providers were required to utilize only the STT protocol.

Results: Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001) and a decrease in average length of stay from 8.0 days to 5.1 days (P < .001). Secondary safety measures included a reduction in the proportion of patients who experienced delirium tremens from 47.5% to 22.5% (P < .001), and a reduction in intubation rates from 13.8% to 1.3% (P = .003).

Conclusion: The STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients requires frequent monitoring to assess withdrawal severity combined with appropriate and timely dosing of benzodiazepines.

Keywords: alcohol withdrawal delirium; alcohol withdrawal syndrome; treatment protocol; benzodiazepine; lorazepam.

Management of severe alcohol withdrawal and delirium tremens (DT) is challenging and requires significant resources, including close monitoring and intensive treatment, frequently in an intensive care unit (ICU).¹ Early diagnosis and therapeutic intervention are important to limit potential complications associated with DT.² Benzodiazepines are first-line therapeutic agents, but the definition of optimal use and dosing regimens has been limited, due to a lack of randomized controlled trials. In lower acuity patients admitted to a detoxification unit, systematic symptom–triggered benzodiazepine therapy (STT) has been established to be more effective than fixed-schedule (FS) dosing.^3-5 Patients treated using STT require lower total benzodiazepine dosing and achieve shorter treatment durations. However, in higher-acuity patients admitted to general medical services, analyses have not shown an advantage of STT over combined FS and STT.⁶

 

 

Methods

The purpose of this study was to determine whether implementation of STT is more effective than FS dosing combined with episodic STT in the management of hospitalized high-acuity alcohol withdrawal patients. We conducted a preintervention and postintervention quasi-experimental study in the step-down unit (SDU) of a 305-bed community teaching hospital. The study population consisted of adult inpatients 18 years or older admitted or transferred to the 12-bed SDU with alcohol withdrawal, as defined by primary or secondary International Classification of Diseases, Tenth Revision diagnoses. SDU admission criteria included patients with prior DT or those who had received multiple doses of benzodiazepines in the emergency department. In-hospital transfer to the SDU was at the physician’s discretion, if the patient required escalating doses of benzodiazepines or the use of increasing resources, such as those for behavioral emergencies. The majority of patients admitted or transferred to the SDU were assigned to medical house staff teams under hospitalist supervision, and, on occasion, under community physicians. The nurse-to-patient ratio in the SDU was 1:3.

Study groups

The preintervention group consisted of 80 successive treatment episodes involving patients admitted or transferred to the SDU from December 2, 2015, to July 1, 2017. Patients were treated based upon physician preference, consisting of a scheduled dosing regimen with additional doses as needed. The postintervention group included 80 successive treatment episodes involving patients admitted or transferred to the SDU from October 1, 2017, to March 23, 2019. The STT protocol was used in all patients in the postintervention group.

In the preintervention group, fixed, scheduled doses of lorazepam or chlordiazepoxide and as-needed lorazepam were prescribed and adjusted based upon physician judgment. Monitoring of symptom severity was scored using the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA-Ar). Benzodiazepine dosing occurred if the CIWA-Ar score had increased 2 or more points from the last score.

In the postintervention group, the STT protocol included the creation of a standardized physician order set for benzodiazepine “sliding scale” administration. The STT protocol allowed for escalating doses for higher withdrawal scores. Symptom severity was scored using MINDS (Minnesota Detoxification Scale) criteria.¹ Lorazepam as-needed dosing was based upon MINDS scores. A MINDS score less than 10 resulted in no medication, MINDS 10-12 required 2 mg, MINDS 13-16 required 4 mg, MINDS 17-19 required 6 mg, and MINDS 20 required 8 mg and a call to the physician. Transfer to the ICU was recommended if the MINDS score was ≥ 20 for 3 consecutive hours. Monitoring intervals occurred more frequently at 30 minutes unless the MINDS score was less than 10. After 7 days, the MINDS protocol was recommended to be discontinued, as the patient might have had iatrogenic delirium.

The STT protocol was introduced during a didactic session for the hospitalists and a separate session for internal medicine and family residents. Each registered nurse working in the SDU was trained in the use of the STT protocol and MINDS during nursing huddles.

 

Patients were excluded from evaluation if they were transferred to the SDU after 7 or more days in the hospital, if they had stayed in the hospital more than 30 days, were chronically on benzodiazepine therapy (to avoid confounding withdrawal symptoms), or if they left the hospital against medical advice (AMA). To avoid bias in the results, the patients with early discontinuation of treatment were included in analyses of secondary outcomes, thus resulting in all 80 episodes analyzed.

Measures and data

The primary outcome measure was benzodiazepine dose intensity, expressed in total lorazepam-equivalents. Secondary measures included average length of stay (including general medical, surgical, and ICU days), seizure incidence, DT incidence, sitter use, behavioral emergency responses, rates of leaving AMA, intubation, transfer to the ICU, and death.

Benzodiazepine dosing and length of stay were obtained from the data warehouse of the hospital’s electronic health record (EHR; Meditech). Benzodiazepine dosing was expressed in total lorazepam-equivalents, with conversion as follows: lorazepam orally and intravenously 1 mg = chlordiazepoxide 25 mg = diazepam 5 mg. All other measures were obtained from chart review of the patients’ EMR entries. The Stamford Hospital Institutional Review Board approved this study.

Analysis

Data analyses for this study were performed using SPSS version 25.0 (IBM). Categorical data were reported as frequency (count) and percent within category. Continuous data were reported as mean (SD). Categorical data were analyzed using χ² analysis; continuous data were analyzed using t-tests. A P value of .05 was considered significant for each analysis.

Results

During the preintervention period, 72 episodes (58 patients) met inclusion criteria, and 69 episodes (55 patients) met inclusion criteria during the postintervention period. Ten patients were represented in both groups. Eight preintervention episodes were excluded from the primary analysis because the patient left AMA. Eleven postintervention episodes were excluded: 9 due to patients leaving AMA, 1 due to chronic benzodiazepine usage, and 1 due to transfer to the SDU unit after 7 days. Baseline characteristics and medication use profiles of the preintervention and postintervention groups are summarized in Table 1.

 

Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001), as shown in Table 2. Average length of stay decreased from 8.0 days to 5.1 days (P < .001). Secondary safety measures were notable for a reduction in DT incidence, from 47.5% to 22.5% (P < .001), and lower rates of intubation, from 13.8% to 1.3% (P = .003). Seven-day readmission rates were 0% preintervention and 1.4% postintervention.

Discussion

We found that hospitalized patients with severe alcohol withdrawal treated with STT required fewer benzodiazepines and had a lower length of stay than patients treated with a conventional combined STT and FS regimen. Implementation of the change from the STT and FS approach to the STT approach in the SDU resulted in concerns that waiting for symptoms to appear could result in more severe withdrawal and prolonged treatment.³ To address this, the intervention included monitoring and dosing every 30 minutes, as compared to monitoring and dosing every 1 hour preintervention. In addition, a sliding-scale approach to match alcohol withdrawal score with dosage was employed in postintervention patients.

Employment of the STT protocol also resulted in decreased complications, including lower rates of DT and transfer to the ICU. This new intervention resulted in significantly decreased time required to control severe symptoms. In the preintervention phase, if a patient’s symptoms escalated despite administration of the as-needed dose of benzodiazepine, there was often a delay in administration of additional doses due to the time needed for nurses to reach a physician and subsequent placement of a new order. In the postintervention phase, the STT protocol allowed nursing staff to give benzodiazepines without delay when needed. We believe this reduced the number of calls by nursing staff to physicians requesting additional medications, and that this improved teamwork when managing these patients.

As part of the intervention, a decision was made to use the MINDS scale rather than the CIWA-Ar scale to assess withdrawal severity. This was because the CIWA-Ar has only been validated in patients with uncomplicated alcohol withdrawal syndrome and has not been researched extensively in patients requiring ICU-level care.¹ MINDS assessment has proven to be reliable and reflects severity of withdrawal. Furthermore, MINDS requires less time to administer—3 to 5 minutes vs 5 to 15 minutes for the CIWA-Ar scale. CIWA-Ar, unlike MINDS, requires subjective input from the patient, which is less reliable for higher acuity patients. Our study is unique in that it focused on high-acuity patients and it showed both a significant reduction in quantity of benzodiazepines prescribed and length of stay. Previous studies on lower acuity patients in detoxification units have confirmed that STT is more effective than a FS approach.^3-5 In patients of higher acuity, STT has not proven to be superior.

A key lesson learned was the need for proper education of nursing staff. Concurrent nursing audits were necessary to ensure that scoring was performed in an accurate and timely manner. In addition, it was challenging to predict which patients might develop DTs versus those requiring a brief inpatient stay. While there was initial concern that an STT protocol could result in underdosing, we found that patients had fewer DT episodes and fewer ICU transfers.

 

This study had several limitations. These include a relatively small sample size and the data being less recent. As there has been no intervening change to the therapeutic paradigm of DT treatment, the findings remain pertinent to the present time. The study employed a simple pre/post design and was conducted in a single setting. We are not aware of any temporal or local trends likely to influence these results. Admissions and transfers to the SDU for severe alcohol withdrawal were based on physician discretion. However, patient characteristics in both groups were similar (Table 1). We note that the postintervention STT protocol allowed for more frequent benzodiazepine dosing, though benzodiazepine use did decrease. Different alcohol withdrawal scores (MINDS vs. CIWA-Ar) were used for postintervention and preintervention, although previous research has shown that MINDS and CIWA-Ar scores correlate well.⁷ Finally, some patients of higher acuity and complexity were excluded, potentially limiting the generalizability of our results.

Conclusion

Our STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients also requires frequent monitoring using the MINDS scale, integrated with benzodiazepine sliding-scale dosing to match symptom severity. This bundled approach resulted in a significant reduction of benzodiazepine usage and reduced length of stay. Timely treatment of these patients also reduced the percent of patients developing DTs, and reduced intubation rates and transfers to the ICU. Further studies may be warranted at other sites to confirm the effectiveness of this STT protocol.

Corresponding author: Paul W. Huang, MD, Stamford Hospital, One Hospital Plaza, PO Box 9317, Stamford, CT 06904; phuang@stamhealth.org.

Financial disclosures: None.

There is substantial interest in improving patient flow and reducing hospital length of stay (LOS).^1-4 Impaired hospital flow may negatively impact both patient satisfaction and safety through, for example, emergency department (ED) overcrowding.^5,6 Impaired hospital flow is associated with downstream effects on patient care, hospital costs, and availability of beds.^7-9

A number of quality-improvement interventions aim to improve patient flow, including efforts to increase the number of discharges that occur before noon.^10,11 Morning discharges have been hypothesized to free hospital beds earlier, thus reducing ED wait times for incoming patients and increasing beds for elective surgeries.¹¹ Morning discharges may also be more predictable for staff and patients. However, it is unclear whether efforts to increase the number of morning discharges have a negative impact on inpatient LOS by incentivizing physicians to keep patients in the hospital for an extra night to facilitate discharge in the early morning rather than the late afternoon. Morning discharges have been associated with both increased¹² and decreased LOS.^10,11,13-15

The purpose of this study was to examine the associations between morning discharges and ED LOS and hospital LOS in general internal medicine (GIM) at seven hospitals. GIM patients represent nearly 40% of ED admissions to a hospital,¹⁶ and thus are an important determinant of patient flow through the ED and hospital. We hypothesized that patients who were admitted to GIM on days with more morning discharges would have shorter ED LOS and hospital LOS.

METHODS

Design, Setting, and Participants

This was a retrospective cohort study conducted using the General Medicine Inpatient Initiative (GEMINI) clinical dataset.¹⁶ The dataset includes all GIM admissions at seven large hospital sites in Toronto and Mississauga, Ontario, Canada. These include five academic hospitals and two community-based teaching hospitals. Each hospital is publicly funded and provides tertiary and/or quaternary care to diverse multiethnic populations. Research ethics board approval was obtained from all participating sites.

GIM care is delivered by several interdisciplinary clinical teams functioning in parallel. Attending physicians are predominantly internists who practice as hospitalists in discrete service blocks, typically lasting 2 weeks at a time. Although GIM patients are preferentially admitted to GIM wards, participating hospitals did not have strict policies regarding cohorting GIM patients to specific wards (ie, holding patients in ED until a specific bed becomes available) that would confound the association between morning discharge and ED wait times. Approximately 75% of GIM patients are cared for on dedicated GIM wards at participating hospitals, with the remainder cared for on other medical or surgical wards.

We included all hospitalized patients who were admitted to hospital and discharged from GIM between April 1, 2010, and October 31, 2017, from the seven GEMINI hospitals. We included only patients admitted through the ED. As such, we did not include elective admissions or interfacility transfers who would not experience ED wait times. We excluded patients who were discharged without a provincial health insurance number (N = 2,169; 1.1% of total sample) because they could not be linked across visits to measure readmissions.

Data Source

The GEMINI dataset has been rigorously validated and previously described in detail.¹⁶ GEMINI collects both administrative health data reported to the Canadian Institute for Health Information (including data about patient demographics, comorbidities, and discharge destination) as well as electronic clinical data extracted from hospital computer systems (including attending physicians, in-hospital patient room transfers, and laboratory test results). Data are collected for each individual hospital encounter, and the provincial health insurance number is used to link patients across encounters.

Exposures and Outcomes

The primary exposure was the number of GIM patients discharged alive between 8:00 am and 12:00 pm (ie, morning GIM discharges) on the day of admission for each hospital encounter. This time window to define morning discharges was selected based on previous literature.^10-13 To report admission characteristics and unadjusted outcomes, hospital days were categorized into quartiles within each hospital based on the number of morning GIM discharges. Hospital days with the lowest number of morning discharges were classified into Q1 and the highest number of morning discharges into Q4, and quartiles were pooled across hospitals.

The two primary outcomes were ED LOS and hospital LOS. ED LOS was calculated as the difference between the time from triage by nursing staff to a patient’s exit from the ED, measured in hours. We also examined 30-day readmission to GIM at any participating hospital as a balancing measure against premature discharges and inpatient mortality because it could modify hospital LOS.

Patient Characteristics

Baseline patient characteristics were measured, including age, sex, Charlson Comorbidity Index score,¹⁷ day of admission (categorized as weekend/holiday or weekday), time of admission to hospital (categorized as daytime, 8:00 am to 4:59 pm, or nighttime, 5:00 pm to 07:59 am), study month, hospital site, and whether patients had been admitted to GIM in the prior 30 days. We used laboratory data to calculate the baseline Laboratory-based Acute Physiology Score (LAPS), which is a validated predictor of inpatient mortality when combined with age and Charlson Comorbidity Index score.^18,19 GIM census on day of admission was calculated in order to include overall patient volumes as an important adjustment variable that could confound the association between morning discharges and patient flow.

Statistical Analysis

The study population and physician characteristics were summarized with descriptive statistics. The balance of baseline patient characteristics across morning discharge quartiles was assessed using standardized differences. A standardized difference of less than 0.1 reflects good balance.²⁰

Unadjusted estimates of patient outcomes were reported across morning discharge quartiles. To model the overall association between morning discharge and outcomes, the number of morning GIM discharges on the day of admission was subtracted from the mean number of morning discharges at each hospital and considered as a continuous exposure. We used generalized linear mixed models to estimate the effect of morning discharges on patient outcomes. We fit negative binomial regression models with log link to examine the association between the number of morning discharges (centered by subtracting the hospital mean) and the two main outcomes, ED LOS and hospital LOS. Given the overdispersion of the study population due to the unequal mean and variance, a negative binomial model was preferred over a Poisson regression, as the mean and variance were not equal.²¹ For our secondary outcomes of binary measures (30-day readmission and morality), we fit logistic regression models. Adjustment for multiple comparisons was not performed.

Multivariable analysis was conducted to adjust for the baseline characteristics described above as well as the total number of GIM discharges on the day of admission and GIM census on the day of admission. Hospital and study month (to account for secular time trends) were included as fixed effects, and patients and admitting physicians were included as crossed random effects to account for the nested structure of admissions within patients and admissions within physicians within hospitals.

A sensitivity analysis was performed to assess for nonlinear associations between morning discharges and the four outcomes (hospital LOS, ED LOS, in-hospital mortality, and readmission) by inputting the term as a restricted cubic spline, with up to five knots, in multivariable regression models. We compared the Akaike information criteria (AIC), computed using the log-likelihood, to determine the goodness-of-fit from the negative binomial models.²² We replicated models for each individual hospital to examine whether any hospital-specific associations existed. Two additional sensitivity analyses were performed to examine heterogeneity in hospital-specific effects. Regression models were fit, including interaction terms between morning discharge and hospital, as well as interaction terms between hospital and the total number of GIM discharges and GIM census. The findings of these models (data not presented) were qualitatively similar to the overall results.

RESULTS

Study Population and Patient Characteristics

The study population consisted of 189,781 hospitalizations involving 115,630 unique patients. The median patient age was 73 years (interquartile range [IQR], 57-84), 50.3% were female, 43.8% had a high Charlson Comorbidity Index score, and 11.1% were admitted to GIM in the prior 30 days (Table 1). The median ED LOS was 14.5 hours (IQR, 10.0-23.1), and the mean was 18.1 hours (SD, 12.2). The median hospital LOS was 4.6 days (IQR, 2.4-9.0), and the mean was 8.6 days (SD, 18.7).

In total, 36,043 (19.0%) discharges occurred between 8:00 am and 12:00 pm. The average number of total daily discharges per hospital was 8.4 (SD, 4.6), and the average number of morning discharges was 1.7 (SD, 1.4). Morning discharges varied across hospitals, ranging from 0.9 per day (SD, 1.1) to 2.1 (SD, 1.6) (Appendix Table 1). The average number of morning discharges in the lowest quartile (Q1) was 0.3 (SD, 0.5) and was 3.3 (SD, 1.2) in the highest quartile (Q4). Baseline patient characteristics were well balanced across the quartiles, with standardized differences less than 0.1 (Table 1), except day of admission and GIM census. Days with a greater number of morning discharges were more likely to be weekdays rather than weekends/holidays (89.2% weekday admissions in Q4 compared with 53.9% in Q1). The median GIM census was 93 patients (IQR, 79-109).

Outcomes

Unadjusted clinical outcomes by number of morning discharges are presented in Table 2. The median unadjusted ED LOS was 14.4 (SD, 14.1), 14.3 (SD, 13.2), 14.5 (SD, 13.0), and 14.8 (SD, 13.0) hours for the first to fourth quartiles (fewest to largest number of morning discharges), respectively. The median unadjusted hospital LOS was 4.6 (SD, 6.5), 4.6 (SD, 6.9), 4.7 (SD, 6.4), and 4.6 (SD, 6.4) days for the first to fourth quartiles, respectively.

Unadjusted inpatient mortality was 6.1%, 5.5%, 5.5%, and 5.2% across the first to fourth quartiles, respectively. Unadjusted 30-day readmission to GIM was 12.2%, 12.6%, 12.6%, and 12.5% across the first to fourth quartiles, respectively.

After multivariable adjustment, there was no significant association between morning discharge and hospital LOS (aRR, 1.000; 95% CI, 0.996-1.000; P = .997), ED LOS (aRR, 0.999; 95% CI, 0.997-1.000; P = .307), in-hospital mortality (aRR, 0.967; 95% CI, 0.920-1.020; P =.183), or 30-day readmission (aRR, 1.010; 95% CI, 0.991-1.020; P = .471) (Table 3, Appendix Table 2, Appendix Table 3, Appendix Table 4, Appendix Table 5). When examining each hospital separately, we found that morning discharge was significantly associated with hospital LOS at only one hospital (Hospital D; aRR, 0.981; 95% CI, 0.966-0.996; P = .013). Morning discharge was statistically significantly associated with ED LOS at three hospitals (A, B, and C), but the aRR was at least 0.99 in all three cases (Table 4).

In sensitivity analyses, we found no improvements in model fit when adding spline terms to the model, suggesting no significant nonlinear associations between morning discharges and the outcomes of interest.

DISCUSSION

This large multicenter cohort study found no significant overall association between the number of morning discharges and ED or hospital LOS in GIM. At one hospital, there was a 1.9% reduction in adjusted ED LOS for every additional morning discharge, but no difference in hospital LOS. We also did not observe differences in readmission or inpatient mortality associated with the number of morning discharges. Our observational findings suggest that there is unlikely to be a strong association between morning discharge and patient throughput in GIM. Given that there may be other downstream benefits of morning discharge, such as freeing beds for daytime surgeries,²³ further research is needed to determine the effectiveness of specific interventions.

Several studies have posited morning discharge as a method of improving both patient care and hospital flow metrics.^{10,11,13-15,23} Quality improvement initiatives targeting morning discharges have included stakeholder meetings, incentives programs, discharge-centered breakfast programs, and creating deadlines for discharge orders.^24-29 Although these initiatives have gained support, critics have suggested that their supporting evidence is not robust. Werthemier et al¹⁰ found a 9.0% reduction of observed to expected LOS associated with increasing the number of early discharges. However, a response article suggested that their findings were confounded by other hospital initiatives, such as allocation of medical and social services to weekends.³⁰ Other observational studies have concluded that hospital LOS is not affected by the number of morning discharges, but this research has been limited by single-center analysis and relatively smaller sample sizes.¹² Our study further calls into question the association between morning discharge and patient throughput.

An additional reason for the controversy is that physicians may actively work to discharge patients late in the day to avoid an additional night in hospital. A qualitative study by Minichiello et al³¹ evaluated staff perceptions regarding afternoon discharges. Physicians and medical students believed that afternoon discharges were a result of waiting for test results and procedures, with staff aiming to discharge patients immediately after obtaining results or finishing necessary procedures. As such, there are concerns that incentivizing morning discharge may lead physicians in the opposite direction, to consciously or unconsciously keep patients overnight in order to facilitate an early morning discharge.³⁰

Our study’s greatest strength was the large sample size over 7 years at seven hospitals in two cities, including both academic and community hospitals with different models of care. To our knowledge, this is the first cohort study that has analyzed the association between early discharge and LOS using multiple centers. To avoid the confounding and reverse causality that may exist when examining the relationship between LOS and morning discharge at the patient level (eg, patients who stay in hospital longer may have more “planned” discharges and leave in the morning), we examined the association based on variation across different days within the GIM service of each hospital. Further, we included robust risk adjustment using clinical and laboratory data. Finally, since our study included a diverse patient population served by participating centers in a system with universal insurance for hospital care, our findings are likely generalizable to other urban and suburban hospitals.

There are several important limitations of our analysis. First, we could only include GIM patients, who represent nearly 40% of ED admissions to hospital at participating centers. A more holistic analysis across all hospital services could be justified; however, given that many quality improvement initiatives occur at the level of a single hospital service, we felt our approach would be informative for future research and improvement efforts. Approximately 75% of GIM patients at participating hospitals were cared for on a GIM ward, with 25% cared for on off-service units. We were unable to include the total hospital census in our models, and this could affect LOS and waiting times for GIM patients, particularly those admitted to off-service units. GIM census is likely highly correlated with hospital census, and we were able to adjust for this. Nevertheless, this remains an important potential source of unmeasured confounding. Second, we did not model the effects of morning discharges from GIM on patient-flow measures for non-GIM patients. Given the lack of effects for GIM patients, who would be more likely to be directly affected, it is unlikely that large effects would be seen for other hospital patients, but we did not measure effects on surgical delays or cancellations, for example.²³ Third, we report 30-day readmission to GIM at participating hospitals only, rather than all readmissions. However, prior research in our region demonstrated that 82% of hospital readmissions occur to the same site.³² Thus, our measure, which includes admission to any participating hospital, likely captures more than 80% of all readmissions, and this was a secondary outcome in our analysis. Finally, qualitative metrics, such as patient or provider satisfaction, were not measured in our study. Earlier discharge may impact patient care in other ways by being more predictable for staff, improving bed allocation for daytime procedures, making medication pick-ups easier to arrange, or making consultations with allied health services more convenient.^11,28,33 Conversely, if pressured to discharge before noon, providers may feel rushed to complete tasks and may face disruptions to typical workflow.²⁴ As such, future research is needed to provide a more complete understanding of the impact of early-morning discharge beyond hospital flow. Given the lack of a strong association observed between morning discharge and patient throughput in our study, further research should also consider the opportunity costs of interventions designed to improve morning discharge.

CONCLUSION

The number of morning discharges was not significantly associated with shorter ED LOS or hospital LOS for GIM patients. Our observational findings suggest that increasing morning discharges alone may not substantially improve patient flow in GIM. Further research is needed to evaluate specific morning discharge interventions and assess hospital-wide effects.

There is substantial interest in improving patient flow and reducing hospital length of stay (LOS).^1-4 Impaired hospital flow may negatively impact both patient satisfaction and safety through, for example, emergency department (ED) overcrowding.^5,6 Impaired hospital flow is associated with downstream effects on patient care, hospital costs, and availability of beds.^7-9

A number of quality-improvement interventions aim to improve patient flow, including efforts to increase the number of discharges that occur before noon.^10,11 Morning discharges have been hypothesized to free hospital beds earlier, thus reducing ED wait times for incoming patients and increasing beds for elective surgeries.¹¹ Morning discharges may also be more predictable for staff and patients. However, it is unclear whether efforts to increase the number of morning discharges have a negative impact on inpatient LOS by incentivizing physicians to keep patients in the hospital for an extra night to facilitate discharge in the early morning rather than the late afternoon. Morning discharges have been associated with both increased¹² and decreased LOS.^10,11,13-15

The purpose of this study was to examine the associations between morning discharges and ED LOS and hospital LOS in general internal medicine (GIM) at seven hospitals. GIM patients represent nearly 40% of ED admissions to a hospital,¹⁶ and thus are an important determinant of patient flow through the ED and hospital. We hypothesized that patients who were admitted to GIM on days with more morning discharges would have shorter ED LOS and hospital LOS.

METHODS

Design, Setting, and Participants

This was a retrospective cohort study conducted using the General Medicine Inpatient Initiative (GEMINI) clinical dataset.¹⁶ The dataset includes all GIM admissions at seven large hospital sites in Toronto and Mississauga, Ontario, Canada. These include five academic hospitals and two community-based teaching hospitals. Each hospital is publicly funded and provides tertiary and/or quaternary care to diverse multiethnic populations. Research ethics board approval was obtained from all participating sites.

GIM care is delivered by several interdisciplinary clinical teams functioning in parallel. Attending physicians are predominantly internists who practice as hospitalists in discrete service blocks, typically lasting 2 weeks at a time. Although GIM patients are preferentially admitted to GIM wards, participating hospitals did not have strict policies regarding cohorting GIM patients to specific wards (ie, holding patients in ED until a specific bed becomes available) that would confound the association between morning discharge and ED wait times. Approximately 75% of GIM patients are cared for on dedicated GIM wards at participating hospitals, with the remainder cared for on other medical or surgical wards.

We included all hospitalized patients who were admitted to hospital and discharged from GIM between April 1, 2010, and October 31, 2017, from the seven GEMINI hospitals. We included only patients admitted through the ED. As such, we did not include elective admissions or interfacility transfers who would not experience ED wait times. We excluded patients who were discharged without a provincial health insurance number (N = 2,169; 1.1% of total sample) because they could not be linked across visits to measure readmissions.

Data Source

The GEMINI dataset has been rigorously validated and previously described in detail.¹⁶ GEMINI collects both administrative health data reported to the Canadian Institute for Health Information (including data about patient demographics, comorbidities, and discharge destination) as well as electronic clinical data extracted from hospital computer systems (including attending physicians, in-hospital patient room transfers, and laboratory test results). Data are collected for each individual hospital encounter, and the provincial health insurance number is used to link patients across encounters.

Exposures and Outcomes

The primary exposure was the number of GIM patients discharged alive between 8:00 am and 12:00 pm (ie, morning GIM discharges) on the day of admission for each hospital encounter. This time window to define morning discharges was selected based on previous literature.^10-13 To report admission characteristics and unadjusted outcomes, hospital days were categorized into quartiles within each hospital based on the number of morning GIM discharges. Hospital days with the lowest number of morning discharges were classified into Q1 and the highest number of morning discharges into Q4, and quartiles were pooled across hospitals.

The two primary outcomes were ED LOS and hospital LOS. ED LOS was calculated as the difference between the time from triage by nursing staff to a patient’s exit from the ED, measured in hours. We also examined 30-day readmission to GIM at any participating hospital as a balancing measure against premature discharges and inpatient mortality because it could modify hospital LOS.

Patient Characteristics

Baseline patient characteristics were measured, including age, sex, Charlson Comorbidity Index score,¹⁷ day of admission (categorized as weekend/holiday or weekday), time of admission to hospital (categorized as daytime, 8:00 am to 4:59 pm, or nighttime, 5:00 pm to 07:59 am), study month, hospital site, and whether patients had been admitted to GIM in the prior 30 days. We used laboratory data to calculate the baseline Laboratory-based Acute Physiology Score (LAPS), which is a validated predictor of inpatient mortality when combined with age and Charlson Comorbidity Index score.^18,19 GIM census on day of admission was calculated in order to include overall patient volumes as an important adjustment variable that could confound the association between morning discharges and patient flow.

Statistical Analysis

The study population and physician characteristics were summarized with descriptive statistics. The balance of baseline patient characteristics across morning discharge quartiles was assessed using standardized differences. A standardized difference of less than 0.1 reflects good balance.²⁰

Unadjusted estimates of patient outcomes were reported across morning discharge quartiles. To model the overall association between morning discharge and outcomes, the number of morning GIM discharges on the day of admission was subtracted from the mean number of morning discharges at each hospital and considered as a continuous exposure. We used generalized linear mixed models to estimate the effect of morning discharges on patient outcomes. We fit negative binomial regression models with log link to examine the association between the number of morning discharges (centered by subtracting the hospital mean) and the two main outcomes, ED LOS and hospital LOS. Given the overdispersion of the study population due to the unequal mean and variance, a negative binomial model was preferred over a Poisson regression, as the mean and variance were not equal.²¹ For our secondary outcomes of binary measures (30-day readmission and morality), we fit logistic regression models. Adjustment for multiple comparisons was not performed.

Multivariable analysis was conducted to adjust for the baseline characteristics described above as well as the total number of GIM discharges on the day of admission and GIM census on the day of admission. Hospital and study month (to account for secular time trends) were included as fixed effects, and patients and admitting physicians were included as crossed random effects to account for the nested structure of admissions within patients and admissions within physicians within hospitals.

A sensitivity analysis was performed to assess for nonlinear associations between morning discharges and the four outcomes (hospital LOS, ED LOS, in-hospital mortality, and readmission) by inputting the term as a restricted cubic spline, with up to five knots, in multivariable regression models. We compared the Akaike information criteria (AIC), computed using the log-likelihood, to determine the goodness-of-fit from the negative binomial models.²² We replicated models for each individual hospital to examine whether any hospital-specific associations existed. Two additional sensitivity analyses were performed to examine heterogeneity in hospital-specific effects. Regression models were fit, including interaction terms between morning discharge and hospital, as well as interaction terms between hospital and the total number of GIM discharges and GIM census. The findings of these models (data not presented) were qualitatively similar to the overall results.

RESULTS

Study Population and Patient Characteristics

The study population consisted of 189,781 hospitalizations involving 115,630 unique patients. The median patient age was 73 years (interquartile range [IQR], 57-84), 50.3% were female, 43.8% had a high Charlson Comorbidity Index score, and 11.1% were admitted to GIM in the prior 30 days (Table 1). The median ED LOS was 14.5 hours (IQR, 10.0-23.1), and the mean was 18.1 hours (SD, 12.2). The median hospital LOS was 4.6 days (IQR, 2.4-9.0), and the mean was 8.6 days (SD, 18.7).

In total, 36,043 (19.0%) discharges occurred between 8:00 am and 12:00 pm. The average number of total daily discharges per hospital was 8.4 (SD, 4.6), and the average number of morning discharges was 1.7 (SD, 1.4). Morning discharges varied across hospitals, ranging from 0.9 per day (SD, 1.1) to 2.1 (SD, 1.6) (Appendix Table 1). The average number of morning discharges in the lowest quartile (Q1) was 0.3 (SD, 0.5) and was 3.3 (SD, 1.2) in the highest quartile (Q4). Baseline patient characteristics were well balanced across the quartiles, with standardized differences less than 0.1 (Table 1), except day of admission and GIM census. Days with a greater number of morning discharges were more likely to be weekdays rather than weekends/holidays (89.2% weekday admissions in Q4 compared with 53.9% in Q1). The median GIM census was 93 patients (IQR, 79-109).

Outcomes

Unadjusted clinical outcomes by number of morning discharges are presented in Table 2. The median unadjusted ED LOS was 14.4 (SD, 14.1), 14.3 (SD, 13.2), 14.5 (SD, 13.0), and 14.8 (SD, 13.0) hours for the first to fourth quartiles (fewest to largest number of morning discharges), respectively. The median unadjusted hospital LOS was 4.6 (SD, 6.5), 4.6 (SD, 6.9), 4.7 (SD, 6.4), and 4.6 (SD, 6.4) days for the first to fourth quartiles, respectively.

Unadjusted inpatient mortality was 6.1%, 5.5%, 5.5%, and 5.2% across the first to fourth quartiles, respectively. Unadjusted 30-day readmission to GIM was 12.2%, 12.6%, 12.6%, and 12.5% across the first to fourth quartiles, respectively.

After multivariable adjustment, there was no significant association between morning discharge and hospital LOS (aRR, 1.000; 95% CI, 0.996-1.000; P = .997), ED LOS (aRR, 0.999; 95% CI, 0.997-1.000; P = .307), in-hospital mortality (aRR, 0.967; 95% CI, 0.920-1.020; P =.183), or 30-day readmission (aRR, 1.010; 95% CI, 0.991-1.020; P = .471) (Table 3, Appendix Table 2, Appendix Table 3, Appendix Table 4, Appendix Table 5). When examining each hospital separately, we found that morning discharge was significantly associated with hospital LOS at only one hospital (Hospital D; aRR, 0.981; 95% CI, 0.966-0.996; P = .013). Morning discharge was statistically significantly associated with ED LOS at three hospitals (A, B, and C), but the aRR was at least 0.99 in all three cases (Table 4).

In sensitivity analyses, we found no improvements in model fit when adding spline terms to the model, suggesting no significant nonlinear associations between morning discharges and the outcomes of interest.

DISCUSSION

This large multicenter cohort study found no significant overall association between the number of morning discharges and ED or hospital LOS in GIM. At one hospital, there was a 1.9% reduction in adjusted ED LOS for every additional morning discharge, but no difference in hospital LOS. We also did not observe differences in readmission or inpatient mortality associated with the number of morning discharges. Our observational findings suggest that there is unlikely to be a strong association between morning discharge and patient throughput in GIM. Given that there may be other downstream benefits of morning discharge, such as freeing beds for daytime surgeries,²³ further research is needed to determine the effectiveness of specific interventions.

Several studies have posited morning discharge as a method of improving both patient care and hospital flow metrics.^{10,11,13-15,23} Quality improvement initiatives targeting morning discharges have included stakeholder meetings, incentives programs, discharge-centered breakfast programs, and creating deadlines for discharge orders.^24-29 Although these initiatives have gained support, critics have suggested that their supporting evidence is not robust. Werthemier et al¹⁰ found a 9.0% reduction of observed to expected LOS associated with increasing the number of early discharges. However, a response article suggested that their findings were confounded by other hospital initiatives, such as allocation of medical and social services to weekends.³⁰ Other observational studies have concluded that hospital LOS is not affected by the number of morning discharges, but this research has been limited by single-center analysis and relatively smaller sample sizes.¹² Our study further calls into question the association between morning discharge and patient throughput.

An additional reason for the controversy is that physicians may actively work to discharge patients late in the day to avoid an additional night in hospital. A qualitative study by Minichiello et al³¹ evaluated staff perceptions regarding afternoon discharges. Physicians and medical students believed that afternoon discharges were a result of waiting for test results and procedures, with staff aiming to discharge patients immediately after obtaining results or finishing necessary procedures. As such, there are concerns that incentivizing morning discharge may lead physicians in the opposite direction, to consciously or unconsciously keep patients overnight in order to facilitate an early morning discharge.³⁰

Our study’s greatest strength was the large sample size over 7 years at seven hospitals in two cities, including both academic and community hospitals with different models of care. To our knowledge, this is the first cohort study that has analyzed the association between early discharge and LOS using multiple centers. To avoid the confounding and reverse causality that may exist when examining the relationship between LOS and morning discharge at the patient level (eg, patients who stay in hospital longer may have more “planned” discharges and leave in the morning), we examined the association based on variation across different days within the GIM service of each hospital. Further, we included robust risk adjustment using clinical and laboratory data. Finally, since our study included a diverse patient population served by participating centers in a system with universal insurance for hospital care, our findings are likely generalizable to other urban and suburban hospitals.

There are several important limitations of our analysis. First, we could only include GIM patients, who represent nearly 40% of ED admissions to hospital at participating centers. A more holistic analysis across all hospital services could be justified; however, given that many quality improvement initiatives occur at the level of a single hospital service, we felt our approach would be informative for future research and improvement efforts. Approximately 75% of GIM patients at participating hospitals were cared for on a GIM ward, with 25% cared for on off-service units. We were unable to include the total hospital census in our models, and this could affect LOS and waiting times for GIM patients, particularly those admitted to off-service units. GIM census is likely highly correlated with hospital census, and we were able to adjust for this. Nevertheless, this remains an important potential source of unmeasured confounding. Second, we did not model the effects of morning discharges from GIM on patient-flow measures for non-GIM patients. Given the lack of effects for GIM patients, who would be more likely to be directly affected, it is unlikely that large effects would be seen for other hospital patients, but we did not measure effects on surgical delays or cancellations, for example.²³ Third, we report 30-day readmission to GIM at participating hospitals only, rather than all readmissions. However, prior research in our region demonstrated that 82% of hospital readmissions occur to the same site.³² Thus, our measure, which includes admission to any participating hospital, likely captures more than 80% of all readmissions, and this was a secondary outcome in our analysis. Finally, qualitative metrics, such as patient or provider satisfaction, were not measured in our study. Earlier discharge may impact patient care in other ways by being more predictable for staff, improving bed allocation for daytime procedures, making medication pick-ups easier to arrange, or making consultations with allied health services more convenient.^11,28,33 Conversely, if pressured to discharge before noon, providers may feel rushed to complete tasks and may face disruptions to typical workflow.²⁴ As such, future research is needed to provide a more complete understanding of the impact of early-morning discharge beyond hospital flow. Given the lack of a strong association observed between morning discharge and patient throughput in our study, further research should also consider the opportunity costs of interventions designed to improve morning discharge.

CONCLUSION

The number of morning discharges was not significantly associated with shorter ED LOS or hospital LOS for GIM patients. Our observational findings suggest that increasing morning discharges alone may not substantially improve patient flow in GIM. Further research is needed to evaluate specific morning discharge interventions and assess hospital-wide effects.

There is substantial interest in improving patient flow and reducing hospital length of stay (LOS).^1-4 Impaired hospital flow may negatively impact both patient satisfaction and safety through, for example, emergency department (ED) overcrowding.^5,6 Impaired hospital flow is associated with downstream effects on patient care, hospital costs, and availability of beds.^7-9

A number of quality-improvement interventions aim to improve patient flow, including efforts to increase the number of discharges that occur before noon.^10,11 Morning discharges have been hypothesized to free hospital beds earlier, thus reducing ED wait times for incoming patients and increasing beds for elective surgeries.¹¹ Morning discharges may also be more predictable for staff and patients. However, it is unclear whether efforts to increase the number of morning discharges have a negative impact on inpatient LOS by incentivizing physicians to keep patients in the hospital for an extra night to facilitate discharge in the early morning rather than the late afternoon. Morning discharges have been associated with both increased¹² and decreased LOS.^10,11,13-15

The purpose of this study was to examine the associations between morning discharges and ED LOS and hospital LOS in general internal medicine (GIM) at seven hospitals. GIM patients represent nearly 40% of ED admissions to a hospital,¹⁶ and thus are an important determinant of patient flow through the ED and hospital. We hypothesized that patients who were admitted to GIM on days with more morning discharges would have shorter ED LOS and hospital LOS.

METHODS

Design, Setting, and Participants

This was a retrospective cohort study conducted using the General Medicine Inpatient Initiative (GEMINI) clinical dataset.¹⁶ The dataset includes all GIM admissions at seven large hospital sites in Toronto and Mississauga, Ontario, Canada. These include five academic hospitals and two community-based teaching hospitals. Each hospital is publicly funded and provides tertiary and/or quaternary care to diverse multiethnic populations. Research ethics board approval was obtained from all participating sites.

GIM care is delivered by several interdisciplinary clinical teams functioning in parallel. Attending physicians are predominantly internists who practice as hospitalists in discrete service blocks, typically lasting 2 weeks at a time. Although GIM patients are preferentially admitted to GIM wards, participating hospitals did not have strict policies regarding cohorting GIM patients to specific wards (ie, holding patients in ED until a specific bed becomes available) that would confound the association between morning discharge and ED wait times. Approximately 75% of GIM patients are cared for on dedicated GIM wards at participating hospitals, with the remainder cared for on other medical or surgical wards.

We included all hospitalized patients who were admitted to hospital and discharged from GIM between April 1, 2010, and October 31, 2017, from the seven GEMINI hospitals. We included only patients admitted through the ED. As such, we did not include elective admissions or interfacility transfers who would not experience ED wait times. We excluded patients who were discharged without a provincial health insurance number (N = 2,169; 1.1% of total sample) because they could not be linked across visits to measure readmissions.

Data Source

The GEMINI dataset has been rigorously validated and previously described in detail.¹⁶ GEMINI collects both administrative health data reported to the Canadian Institute for Health Information (including data about patient demographics, comorbidities, and discharge destination) as well as electronic clinical data extracted from hospital computer systems (including attending physicians, in-hospital patient room transfers, and laboratory test results). Data are collected for each individual hospital encounter, and the provincial health insurance number is used to link patients across encounters.

Exposures and Outcomes

The primary exposure was the number of GIM patients discharged alive between 8:00 am and 12:00 pm (ie, morning GIM discharges) on the day of admission for each hospital encounter. This time window to define morning discharges was selected based on previous literature.^10-13 To report admission characteristics and unadjusted outcomes, hospital days were categorized into quartiles within each hospital based on the number of morning GIM discharges. Hospital days with the lowest number of morning discharges were classified into Q1 and the highest number of morning discharges into Q4, and quartiles were pooled across hospitals.

The two primary outcomes were ED LOS and hospital LOS. ED LOS was calculated as the difference between the time from triage by nursing staff to a patient’s exit from the ED, measured in hours. We also examined 30-day readmission to GIM at any participating hospital as a balancing measure against premature discharges and inpatient mortality because it could modify hospital LOS.

Patient Characteristics

Baseline patient characteristics were measured, including age, sex, Charlson Comorbidity Index score,¹⁷ day of admission (categorized as weekend/holiday or weekday), time of admission to hospital (categorized as daytime, 8:00 am to 4:59 pm, or nighttime, 5:00 pm to 07:59 am), study month, hospital site, and whether patients had been admitted to GIM in the prior 30 days. We used laboratory data to calculate the baseline Laboratory-based Acute Physiology Score (LAPS), which is a validated predictor of inpatient mortality when combined with age and Charlson Comorbidity Index score.^18,19 GIM census on day of admission was calculated in order to include overall patient volumes as an important adjustment variable that could confound the association between morning discharges and patient flow.

Statistical Analysis

The study population and physician characteristics were summarized with descriptive statistics. The balance of baseline patient characteristics across morning discharge quartiles was assessed using standardized differences. A standardized difference of less than 0.1 reflects good balance.²⁰

Unadjusted estimates of patient outcomes were reported across morning discharge quartiles. To model the overall association between morning discharge and outcomes, the number of morning GIM discharges on the day of admission was subtracted from the mean number of morning discharges at each hospital and considered as a continuous exposure. We used generalized linear mixed models to estimate the effect of morning discharges on patient outcomes. We fit negative binomial regression models with log link to examine the association between the number of morning discharges (centered by subtracting the hospital mean) and the two main outcomes, ED LOS and hospital LOS. Given the overdispersion of the study population due to the unequal mean and variance, a negative binomial model was preferred over a Poisson regression, as the mean and variance were not equal.²¹ For our secondary outcomes of binary measures (30-day readmission and morality), we fit logistic regression models. Adjustment for multiple comparisons was not performed.

Multivariable analysis was conducted to adjust for the baseline characteristics described above as well as the total number of GIM discharges on the day of admission and GIM census on the day of admission. Hospital and study month (to account for secular time trends) were included as fixed effects, and patients and admitting physicians were included as crossed random effects to account for the nested structure of admissions within patients and admissions within physicians within hospitals.

A sensitivity analysis was performed to assess for nonlinear associations between morning discharges and the four outcomes (hospital LOS, ED LOS, in-hospital mortality, and readmission) by inputting the term as a restricted cubic spline, with up to five knots, in multivariable regression models. We compared the Akaike information criteria (AIC), computed using the log-likelihood, to determine the goodness-of-fit from the negative binomial models.²² We replicated models for each individual hospital to examine whether any hospital-specific associations existed. Two additional sensitivity analyses were performed to examine heterogeneity in hospital-specific effects. Regression models were fit, including interaction terms between morning discharge and hospital, as well as interaction terms between hospital and the total number of GIM discharges and GIM census. The findings of these models (data not presented) were qualitatively similar to the overall results.

RESULTS

Study Population and Patient Characteristics

The study population consisted of 189,781 hospitalizations involving 115,630 unique patients. The median patient age was 73 years (interquartile range [IQR], 57-84), 50.3% were female, 43.8% had a high Charlson Comorbidity Index score, and 11.1% were admitted to GIM in the prior 30 days (Table 1). The median ED LOS was 14.5 hours (IQR, 10.0-23.1), and the mean was 18.1 hours (SD, 12.2). The median hospital LOS was 4.6 days (IQR, 2.4-9.0), and the mean was 8.6 days (SD, 18.7).

In total, 36,043 (19.0%) discharges occurred between 8:00 am and 12:00 pm. The average number of total daily discharges per hospital was 8.4 (SD, 4.6), and the average number of morning discharges was 1.7 (SD, 1.4). Morning discharges varied across hospitals, ranging from 0.9 per day (SD, 1.1) to 2.1 (SD, 1.6) (Appendix Table 1). The average number of morning discharges in the lowest quartile (Q1) was 0.3 (SD, 0.5) and was 3.3 (SD, 1.2) in the highest quartile (Q4). Baseline patient characteristics were well balanced across the quartiles, with standardized differences less than 0.1 (Table 1), except day of admission and GIM census. Days with a greater number of morning discharges were more likely to be weekdays rather than weekends/holidays (89.2% weekday admissions in Q4 compared with 53.9% in Q1). The median GIM census was 93 patients (IQR, 79-109).

Outcomes

Unadjusted clinical outcomes by number of morning discharges are presented in Table 2. The median unadjusted ED LOS was 14.4 (SD, 14.1), 14.3 (SD, 13.2), 14.5 (SD, 13.0), and 14.8 (SD, 13.0) hours for the first to fourth quartiles (fewest to largest number of morning discharges), respectively. The median unadjusted hospital LOS was 4.6 (SD, 6.5), 4.6 (SD, 6.9), 4.7 (SD, 6.4), and 4.6 (SD, 6.4) days for the first to fourth quartiles, respectively.

Unadjusted inpatient mortality was 6.1%, 5.5%, 5.5%, and 5.2% across the first to fourth quartiles, respectively. Unadjusted 30-day readmission to GIM was 12.2%, 12.6%, 12.6%, and 12.5% across the first to fourth quartiles, respectively.

After multivariable adjustment, there was no significant association between morning discharge and hospital LOS (aRR, 1.000; 95% CI, 0.996-1.000; P = .997), ED LOS (aRR, 0.999; 95% CI, 0.997-1.000; P = .307), in-hospital mortality (aRR, 0.967; 95% CI, 0.920-1.020; P =.183), or 30-day readmission (aRR, 1.010; 95% CI, 0.991-1.020; P = .471) (Table 3, Appendix Table 2, Appendix Table 3, Appendix Table 4, Appendix Table 5). When examining each hospital separately, we found that morning discharge was significantly associated with hospital LOS at only one hospital (Hospital D; aRR, 0.981; 95% CI, 0.966-0.996; P = .013). Morning discharge was statistically significantly associated with ED LOS at three hospitals (A, B, and C), but the aRR was at least 0.99 in all three cases (Table 4).

In sensitivity analyses, we found no improvements in model fit when adding spline terms to the model, suggesting no significant nonlinear associations between morning discharges and the outcomes of interest.

DISCUSSION

This large multicenter cohort study found no significant overall association between the number of morning discharges and ED or hospital LOS in GIM. At one hospital, there was a 1.9% reduction in adjusted ED LOS for every additional morning discharge, but no difference in hospital LOS. We also did not observe differences in readmission or inpatient mortality associated with the number of morning discharges. Our observational findings suggest that there is unlikely to be a strong association between morning discharge and patient throughput in GIM. Given that there may be other downstream benefits of morning discharge, such as freeing beds for daytime surgeries,²³ further research is needed to determine the effectiveness of specific interventions.

Several studies have posited morning discharge as a method of improving both patient care and hospital flow metrics.^{10,11,13-15,23} Quality improvement initiatives targeting morning discharges have included stakeholder meetings, incentives programs, discharge-centered breakfast programs, and creating deadlines for discharge orders.^24-29 Although these initiatives have gained support, critics have suggested that their supporting evidence is not robust. Werthemier et al¹⁰ found a 9.0% reduction of observed to expected LOS associated with increasing the number of early discharges. However, a response article suggested that their findings were confounded by other hospital initiatives, such as allocation of medical and social services to weekends.³⁰ Other observational studies have concluded that hospital LOS is not affected by the number of morning discharges, but this research has been limited by single-center analysis and relatively smaller sample sizes.¹² Our study further calls into question the association between morning discharge and patient throughput.

An additional reason for the controversy is that physicians may actively work to discharge patients late in the day to avoid an additional night in hospital. A qualitative study by Minichiello et al³¹ evaluated staff perceptions regarding afternoon discharges. Physicians and medical students believed that afternoon discharges were a result of waiting for test results and procedures, with staff aiming to discharge patients immediately after obtaining results or finishing necessary procedures. As such, there are concerns that incentivizing morning discharge may lead physicians in the opposite direction, to consciously or unconsciously keep patients overnight in order to facilitate an early morning discharge.³⁰

Our study’s greatest strength was the large sample size over 7 years at seven hospitals in two cities, including both academic and community hospitals with different models of care. To our knowledge, this is the first cohort study that has analyzed the association between early discharge and LOS using multiple centers. To avoid the confounding and reverse causality that may exist when examining the relationship between LOS and morning discharge at the patient level (eg, patients who stay in hospital longer may have more “planned” discharges and leave in the morning), we examined the association based on variation across different days within the GIM service of each hospital. Further, we included robust risk adjustment using clinical and laboratory data. Finally, since our study included a diverse patient population served by participating centers in a system with universal insurance for hospital care, our findings are likely generalizable to other urban and suburban hospitals.

There are several important limitations of our analysis. First, we could only include GIM patients, who represent nearly 40% of ED admissions to hospital at participating centers. A more holistic analysis across all hospital services could be justified; however, given that many quality improvement initiatives occur at the level of a single hospital service, we felt our approach would be informative for future research and improvement efforts. Approximately 75% of GIM patients at participating hospitals were cared for on a GIM ward, with 25% cared for on off-service units. We were unable to include the total hospital census in our models, and this could affect LOS and waiting times for GIM patients, particularly those admitted to off-service units. GIM census is likely highly correlated with hospital census, and we were able to adjust for this. Nevertheless, this remains an important potential source of unmeasured confounding. Second, we did not model the effects of morning discharges from GIM on patient-flow measures for non-GIM patients. Given the lack of effects for GIM patients, who would be more likely to be directly affected, it is unlikely that large effects would be seen for other hospital patients, but we did not measure effects on surgical delays or cancellations, for example.²³ Third, we report 30-day readmission to GIM at participating hospitals only, rather than all readmissions. However, prior research in our region demonstrated that 82% of hospital readmissions occur to the same site.³² Thus, our measure, which includes admission to any participating hospital, likely captures more than 80% of all readmissions, and this was a secondary outcome in our analysis. Finally, qualitative metrics, such as patient or provider satisfaction, were not measured in our study. Earlier discharge may impact patient care in other ways by being more predictable for staff, improving bed allocation for daytime procedures, making medication pick-ups easier to arrange, or making consultations with allied health services more convenient.^11,28,33 Conversely, if pressured to discharge before noon, providers may feel rushed to complete tasks and may face disruptions to typical workflow.²⁴ As such, future research is needed to provide a more complete understanding of the impact of early-morning discharge beyond hospital flow. Given the lack of a strong association observed between morning discharge and patient throughput in our study, further research should also consider the opportunity costs of interventions designed to improve morning discharge.

CONCLUSION

The number of morning discharges was not significantly associated with shorter ED LOS or hospital LOS for GIM patients. Our observational findings suggest that increasing morning discharges alone may not substantially improve patient flow in GIM. Further research is needed to evaluate specific morning discharge interventions and assess hospital-wide effects.

Nearly 48,000 Americans died from overdoses involving opioids in 2018, continuing a national crisis that has led to 446,000 deaths since 1999.¹ Annually, opioids are responsible for more than 500,000 admissions, approximately 1% of all hospitalizations, costing the United States nearly $15 billion.^2,3 Among hospitalized patients, chronic opioid use is associated with increased mortality, severe infectious complications, and higher rates of readmission.⁴ Opioid use disorder (OUD) is a chronic, relapsing medical condition with biopsychosocial origins and significant morbidity and mortality.⁵ Opioid agonist therapy (OAT) with buprenorphine or methadone maintenance, the evidence-based standard of treatment, reduces the mortality rate by half, decreases overdoses and hospital readmissions, and improves retention in care.^6-10

OAT maintenance refers to using buprenorphine or methadone for long-term treatment of OUD rather than for acute treatment of opioid withdrawal. Despite evidence supporting OAT maintenance, clinicians start medications for only 11% to 15% of hospitalized patients with OUD, depending on practice contexts.^11,12 Three significant barriers—stigma, insufficient clinician education, and restrictive regulations—prevent clinicians from starting OAT.¹³ Clinicians who do not have the Drug Enforcement Administration (DEA)–issued DATA-2000 waiver (X-waiver) for outpatient prescribing can order buprenorphine for admitted patients but cannot prescribe it at discharge.¹⁴ In hospitals where they exist, addiction medicine consult services offer primary teams guidance on pharmacotherapy, leading to reduced hospital readmissions and increased engagement in outpatient addiction treatment.^15-17 However, in most hospitals around the country, such specialty services do not exist.¹⁸ In some hospitals without addiction medicine consult services, hospitalists with expertise in OUD have started assisting primary teams in starting OAT, but to our knowledge, no prior studies have described the impact of these interventions on patients or clinician experience with OAT.¹⁹

This quality improvement project aimed to increase the rate at which internal medicine resident teams at Johns Hopkins Hospital (JHH) in Baltimore, Maryland, started hospitalized patients with OUD on buprenorphine maintenance. We hypothesized that resident education and measures to increase the availability of X-waivered physicians would increase the rate of initiating buprenorphine maintenance. We additionally hypothesized that these interventions would increase knowledge about and comfort with buprenorphine across the residency. This represents the first study to examine the effects of clinician education and a team of X-waivered residents and hospitalists who assist in starting buprenorphine maintenance in a hospital without an addiction medicine consult service.

METHODS

Setting

This study took place from July 2018 to June 2019 at JHH, a large, academic, urban hospital in Baltimore. Prior to the intervention, internal medicine residents at JHH commonly used short courses of buprenorphine to treat withdrawal, but they did not have access to hospital-specific resources to assist with starting maintenance OAT. During the study period, JHH had a Substance Use Disorders team staffed by peer recovery specialists that could be consulted by hospitalists and residents to provide psychosocial support and link admitted patients to treatment after discharge. There were no providers on the team to guide pharmacotherapy or to write discharge buprenorphine prescriptions. The Osler Medical Residency Training Program at JHH has 140 internal medicine residents and 16 combined medicine-pediatrics residents. All residents receive 1 hour of formal education about opioid use disorder annually. In addition, 28 of those 156 residents, those in the Urban Health Primary Care track, spend 1 month on an Addiction Medicine rotation in which they complete the 8-hour training required to receive the X-waiver. Those residents are encouraged to apply for the X-waiver once they obtain a medical license subsidized by a Health Resources & Services Administration (HRSA) grant. Four internal medicine attending physicians on teaching services and one resident had X-waivers prior to the intervention.

Intervention

In November 2018, we administered a survey to residents to identify barriers to starting buprenorphine maintenance and to measure knowledge and confidence with using buprenorphine for OUD (Appendix Figure 1 and Figure 2). We focused on buprenorphine because providers at JHH were familiar with this medication and because Baltimore has widespread access to buprenorphine, with more than 490 local buprenorphine providers.²⁰ Five residents piloted the survey and provided feedback. We then administered the survey to all internal medicine and medicine-pediatrics residents. Based on the results, we developed a targeted educational conference and also created the Buprenorphine Bridge Team (BBT).

In January 2019, we presented the educational conference for residents devoted to the use of buprenorphine for OUD and introduced the BBT. The conference started with a patient testimonial and included peer recovery specialists, pharmacists, nurses, and social workers. We summarized the evidence for buprenorphine and offered a practical guide to start treatment in a one-page protocol. This protocol included guidance on selecting patients, shared decision-making around OUD treatment, avoiding precipitated withdrawal, dosing buprenorphine, and establishing follow-up (Appendix Figure 3). We asked for input on this protocol from nursing leadership, social work teams, and peer recovery specialists. Dosing was adapted from the Guidelines from the American Society of Addiction Medicine, with expert input from physicians from the Addiction Medicine Consult service at Johns Hopkins Bayview Medical Center, also in Baltimore.⁵ We instructed residents to obtain discharge buprenorphine prescriptions from an X-waivered physician on their team or from the newly established BBT. We asked resident teams to set up a postdischarge appointment for patients with an X-waivered provider, either in a community practice or at the JHH After Care Clinic, a transitional care clinic for discharged patients.²¹

The BBT is a resident-led group of X-waivered JHH residents and hospitalists who volunteer to write discharge buprenorphine prescriptions for patients. The BBT serves to ensure primary teams have access to an X-waivered prescriber. It is not a consult service. We asked primary teams to contact the BBT after initiating buprenorphine and after securing a follow-up appointment. In response to each request, a member of the BBT reviews the patient chart, confirms the follow-up plan, writes a prescription for buprenorphine along with intranasal naloxone, and leaves a brief note. During the 6-month postintervention period, the team consisted of three residents and three hospitalist attendings. Each week, two members (residents or attendings) staffed the team Monday to Friday, 8 am to 5 pm. Most weeks were staffed by two residents. One resident provided services after hours and during weekends. Resident team members ensured that the buprenorphine plan was discussed with the primary team’s attending. For dosing questions beyond the BBT’s scope of knowledge, a member of the BBT relayed questions to physicians from the Addiction Medicine Consult team at Johns Hopkins Bayview Medical Center.

In May 2019, 5 months after the education session and implementation of the BBT, we administered a follow-up survey.

Outcomes

The primary outcome was the percent of inpatients eligible to start OAT who were discharged on buprenorphine maintenance. We obtained data from the electronic medical record. The denominator consisted of patients with OUD not on buprenorphine or methadone maintenance on admission. We identified patients with OUD by an opioid-related International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code or by a standing or as-needed order for buprenorphine or methadone during hospitalization.²² We reviewed admission and discharge documentation to identify patients with OUD who were not in active treatment with buprenorphine or methadone maintenance.

As a secondary outcome, we measured engagement in OUD treatment after discharge by calculating the proportion of patients started on buprenorphine who filled a buprenorphine prescription within 30 days after discharge. We chose 30 days based on the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measure for engagement of treatment for alcohol and other drugs.²³ We obtained the data from the Chesapeake Regional Information System for our Patients (CRISP) Prescription Drug Monitoring Program, which monitors all prescriptions for controlled substances dispensed in Maryland and five neighboring states. As a balancing measure, we counted patients newly started on methadone maintenance for OUD before and after the intervention. Additional secondary process outcomes included frequency of BBT requests, the volume of buprenorphine prescriptions written by the team, and time required to complete a BBT request.

Clinician-level outcomes, measured with electronically administered pre- and postintervention surveys to residents, included knowledge about and comfort with buprenorphine. Of the 16 questions in the pre- and postimplementation surveys, we analyzed the 6 questions concerning knowledge and comfort that remained identical in the pre- and postintervention surveys and used 5-point Likert scale responses. As an incentive, we randomly distributed three $50 gift cards to survey completers.

Analysis

We used an interrupted time series analysis to evaluate the association between the intervention bundle and a change in the rate that medical teams started patients with OUD on buprenorphine maintenance. This approach allowed us to control for preintervention trends. To evaluate the impact of our interventions, our pre- and postintervention periods include the same residents during the 2018-2019 academic year. Both periods consisted of twelve 2-week intervals (preintervention: July 26, 2019, to January 9, 2019; postintervention: January 10, 2019, to June 26, 2019).

To evaluate for changes in engagement in OUD treatment after discharge, we used two-sample t tests. To evaluate for changes in resident-reported comfort and knowledge with initiating buprenorphine maintenance, we used Wilcoxon rank sum tests for survey data and Wilcoxon signed rank tests for paired data. All analyses employed two-sided P values with statistical significance evaluated at the .05 alpha level. We analyzed data using R version 3.6.3 (Foundation for Statistical Computing). The Institutional Review Board at JHH reviewed and approved the study protocol as a quality improvement project (IRB00193365).

RESULTS

During the 24-week preintervention period, internal medicine resident teams started 30 out of 305 eligible patients (10%) on buprenorphine maintenance vs 64 out of 270 eligible patients (24%) during the 24-week postintervention period. Our interrupted time series analysis showed a significant increase in the percent of eligible patients started on buprenorphine maintenance (expected number of patients started postintervention, 27; actual, 64; absolute increase in percent, 14.4%; 95% CI, 3.6%-25.3%; P = .017) (Figure). There was no significant trend during the preintervention period and no significant trend during the postintervention period.

Before the intervention, 13 of the 30 patients (40%) newly started on buprenorphine maintenance during their admission filled a follow-up buprenorphine prescription within 30 days of discharge. After the intervention, 31 of 64 patients (46%) filled a buprenorphine prescription within 30 days (P = .612). Two patients were started on methadone maintenance, one prior to and one after the intervention.

During the 6-month postintervention period, the BBT received 75 requests and wrote 70 prescriptions for buprenorphine. The median time required to complete a BBT request was 15 minutes (minimum, 5 minutes; maximum, 60 minutes).

Of 156 internal medicine and medicine-pediatrics residents, 89 residents (57%) completed the baseline survey and 66 residents (42%) completed the follow-up survey. Forty residents completed both surveys. After the intervention, residents were significantly more likely to feel comfortable dosing buprenorphine (P < .0001) and counseling patients about its use (P = .0237) and were more likely to report ease of establishing follow-up (P < .0001). Self-reported knowledge about preventing precipitated withdrawal increased significantly (P = .0191), as did knowledge about the effectiveness of buprenorphine (P = .0003) independent of formal drug counseling (P = .0066) (Table). Paired survey data also found statistically significant results for all questions except those about preventing precipitated withdrawal and efficacy. For the latter, respondents who completed both surveys were more knowledgeable before the intervention than the overall group that completed the baseline survey (Appendix Table).

DISCUSSION

This study shows how a resident-led quality improvement project comprising clinician education and implementation of a novel BBT was associated with an increased rate of starting buprenorphine maintenance in hospitalized patients with OUD and improved resident knowledge about and comfort with buprenorphine. To our knowledge, this is the first study demonstrating how education and a team of X-waivered generalists can help primary teams initiate and discharge patients on buprenorphine maintenance in a hospital without an addiction medicine consult service.

Prior to the intervention, resident internal medicine teams at JHH started 10% of hospitalized patients with OUD on buprenorphine maintenance, consistent with prior studies showing rates of 11% to 15% for initiating OAT for hospitalized patients.^11,12 After the intervention, the rate of initiating buprenorphine maintenance more than doubled, rising to 24% of eligible patients. Resident internal medicine teams at JHH started buprenorphine maintenance for 37 more patients over the 24-week postintervention period than would have been predicted prior to the intervention, or an additional three patients every 2 weeks.

Between 40% and 46% of hospitalized patients newly started on buprenorphine maintenance filled an outpatient buprenorphine prescription within 30 days of discharge. We are not aware of comparative data for 30-day follow-up for hospitalized patients newly started on buprenorphine maintenance. Data from other contexts show 5% to 10% of veterans were engaged in addiction treatment 30 days after initiation from inpatient or outpatient encounters. An analysis of an academic medical center in Oregon found engagement with an addiction medicine consult service increased after hospital engagement for patients with any substance use disorder from 23% to 39% using the 34-day HEDIS measure for engagement.^17,24,25

The BBT required approximately 15 minutes per request and wrote an average of three prescriptions per week, demonstrating the feasibility of this approach and the high demand for this service. One strength of our approach is that residents gained experience starting buprenorphine independently using the aforementioned protocol instead of deferring to a full consult service. It is likely that this resident engagement in initiating longitudinal OUD care contributed to the success of this initiative, as did existing resident familiarity with using buprenorphine for opioid withdrawal.

This approach to resident education—promoting direct, first-person experience with medications in a clinical context—aligns with recommendations from a recent review about substance use disorder education for health professionals.²⁶ Our interventions increased resident knowledge and comfort with buprenorphine, consistent with prior studies showing increased resident confidence in management of substance use disorders after curricular innovations.^24,25

A few contextual features were essential for this project’s viability. Maryland allows American medical graduates to obtain a medical license after 1 year of postgraduate training. This allowed three residents to obtain X-waivers. These residents had access to HRSA funding to subsidize the expenses of applying for state licensure and DEA registration. BBT members volunteered their time while working on other services. Last, we were able to take advantage of buprenorphine-providing clinics in Baltimore, including the JHH After Care Clinic, to accept patients for follow-up appointments after discharge.

Limitations

The BBT required motivated clinicians willing to volunteer for additional clinical responsibilities during inpatient rotations and supportive faculty and residency leadership. Attending physicians, nurse practitioners, or physician assistants could staff a similar BBT in hospitals without residents or in hospitals where residents cannot obtain DEA registration. Crucially, other hospitals may not have access to practices with X-waivered physicians for outpatient follow-up. A recent study found X-waivered primary care physicians were less likely to be affiliated with hospital health systems. Other studies have shown limitations in access to buprenorphine at the county level based on geography and racial/ethnic segregation.^27-29

Most patients hospitalized with OUD did not have ICD-10 codes associated with OUD. We addressed this by assuming patients had OUD if buprenorphine or methadone was ordered during their hospitalization, even if the medication was never administered. This may have overcounted patients prescribed these medications for indications other than OUD, and it may have undercounted patients with OUD for whom buprenorphine or methadone were never considered. The opioid withdrawal order set at JHH automatically offers an option to use buprenorphine to treat withdrawal. Patients with OUD for whom buprenorphine or methadone were never ordered likely did not experience withdrawal or were in withdrawal so mild that it escaped the attention of the team, which limits the generalizability of our intervention.

We identified several limitations to the internal validity of our study. First, we used a before-and-after study design without a control group. We could not ethically withhold access to evidence-based, mortality-reducing medications from patients. Without a control group, we cannot rule out the possibility that underlying temporal trends made residents more likely to start buprenorphine maintenance independent of our intervention. We attempted to control for unmeasured confounders by using an interrupted time series analysis to control for preintervention trends, comparing the same group of residents before and after our interventions, and selecting an intervention period during which residents were given only educational sessions and materials provided by our team. Our results may be biased by clustered data because certain residents may have been more likely to initiate buprenorphine, but these effects are likely marginal because resident schedules are balanced between outpatient and inpatient rotations during each 6-month period.

Finally, this project focused on buprenorphine, not on other medications for OUD, including methadone or naltrexone, or nonpharmacologic treatments for OUD.

Sustainability and Next Steps

Since the start of the BBT in January 2019, five additional PGY-2 residents obtained their medical licenses and X-waivers. These residents, with the support of two attending hospitalists, led the BBT and coordinated education sessions that were incorporated into the curriculum during the 2019-2020 academic year. These educational sessions will continue indefinitely. In 2020, JHH started an Addiction Medicine Consult Service staffed by physicians, NPs, and a pharmacist. The BBT continues to operate in conjunction with this service.

We found substantial variability in the rate of buprenorphine maintenance initiation despite our interventions. This is an area for future improvement. In a free-response prompt in our follow-up survey, residents requested additional education sessions and an order set to assist with initiation of buprenorphine. To address these gaps, three educational sessions were added, one of which included education on starting methadone maintenance therapy. We also added a new order set for starting buprenorphine maintenance. We hypothesize that these interventions will improve consistency.

In order for a similar program to be disseminated to other institutions, educational initiatives and a team of dedicated X-waivered prescribers are key. Materials to assist with this process are available in the Appendix.

CONCLUSION

This study shows how a resident-led intervention comprising clinician education and a team of X-waivered generalists was associated with improved treatment of OUD for hospitalized patients. We encourage residents and all clinicians at other hospitals without addiction medicine consult services to design, implement, and study similar interventions that directly increase the use of buprenorphine or methadone maintenance to treat OUD.

Preliminary results from this project were presented at the AMERSA National Conference on November 7, 2019.

Nearly 48,000 Americans died from overdoses involving opioids in 2018, continuing a national crisis that has led to 446,000 deaths since 1999.¹ Annually, opioids are responsible for more than 500,000 admissions, approximately 1% of all hospitalizations, costing the United States nearly $15 billion.^2,3 Among hospitalized patients, chronic opioid use is associated with increased mortality, severe infectious complications, and higher rates of readmission.⁴ Opioid use disorder (OUD) is a chronic, relapsing medical condition with biopsychosocial origins and significant morbidity and mortality.⁵ Opioid agonist therapy (OAT) with buprenorphine or methadone maintenance, the evidence-based standard of treatment, reduces the mortality rate by half, decreases overdoses and hospital readmissions, and improves retention in care.^6-10

OAT maintenance refers to using buprenorphine or methadone for long-term treatment of OUD rather than for acute treatment of opioid withdrawal. Despite evidence supporting OAT maintenance, clinicians start medications for only 11% to 15% of hospitalized patients with OUD, depending on practice contexts.^11,12 Three significant barriers—stigma, insufficient clinician education, and restrictive regulations—prevent clinicians from starting OAT.¹³ Clinicians who do not have the Drug Enforcement Administration (DEA)–issued DATA-2000 waiver (X-waiver) for outpatient prescribing can order buprenorphine for admitted patients but cannot prescribe it at discharge.¹⁴ In hospitals where they exist, addiction medicine consult services offer primary teams guidance on pharmacotherapy, leading to reduced hospital readmissions and increased engagement in outpatient addiction treatment.^15-17 However, in most hospitals around the country, such specialty services do not exist.¹⁸ In some hospitals without addiction medicine consult services, hospitalists with expertise in OUD have started assisting primary teams in starting OAT, but to our knowledge, no prior studies have described the impact of these interventions on patients or clinician experience with OAT.¹⁹

This quality improvement project aimed to increase the rate at which internal medicine resident teams at Johns Hopkins Hospital (JHH) in Baltimore, Maryland, started hospitalized patients with OUD on buprenorphine maintenance. We hypothesized that resident education and measures to increase the availability of X-waivered physicians would increase the rate of initiating buprenorphine maintenance. We additionally hypothesized that these interventions would increase knowledge about and comfort with buprenorphine across the residency. This represents the first study to examine the effects of clinician education and a team of X-waivered residents and hospitalists who assist in starting buprenorphine maintenance in a hospital without an addiction medicine consult service.

METHODS

Setting

This study took place from July 2018 to June 2019 at JHH, a large, academic, urban hospital in Baltimore. Prior to the intervention, internal medicine residents at JHH commonly used short courses of buprenorphine to treat withdrawal, but they did not have access to hospital-specific resources to assist with starting maintenance OAT. During the study period, JHH had a Substance Use Disorders team staffed by peer recovery specialists that could be consulted by hospitalists and residents to provide psychosocial support and link admitted patients to treatment after discharge. There were no providers on the team to guide pharmacotherapy or to write discharge buprenorphine prescriptions. The Osler Medical Residency Training Program at JHH has 140 internal medicine residents and 16 combined medicine-pediatrics residents. All residents receive 1 hour of formal education about opioid use disorder annually. In addition, 28 of those 156 residents, those in the Urban Health Primary Care track, spend 1 month on an Addiction Medicine rotation in which they complete the 8-hour training required to receive the X-waiver. Those residents are encouraged to apply for the X-waiver once they obtain a medical license subsidized by a Health Resources & Services Administration (HRSA) grant. Four internal medicine attending physicians on teaching services and one resident had X-waivers prior to the intervention.

Intervention

In November 2018, we administered a survey to residents to identify barriers to starting buprenorphine maintenance and to measure knowledge and confidence with using buprenorphine for OUD (Appendix Figure 1 and Figure 2). We focused on buprenorphine because providers at JHH were familiar with this medication and because Baltimore has widespread access to buprenorphine, with more than 490 local buprenorphine providers.²⁰ Five residents piloted the survey and provided feedback. We then administered the survey to all internal medicine and medicine-pediatrics residents. Based on the results, we developed a targeted educational conference and also created the Buprenorphine Bridge Team (BBT).

In January 2019, we presented the educational conference for residents devoted to the use of buprenorphine for OUD and introduced the BBT. The conference started with a patient testimonial and included peer recovery specialists, pharmacists, nurses, and social workers. We summarized the evidence for buprenorphine and offered a practical guide to start treatment in a one-page protocol. This protocol included guidance on selecting patients, shared decision-making around OUD treatment, avoiding precipitated withdrawal, dosing buprenorphine, and establishing follow-up (Appendix Figure 3). We asked for input on this protocol from nursing leadership, social work teams, and peer recovery specialists. Dosing was adapted from the Guidelines from the American Society of Addiction Medicine, with expert input from physicians from the Addiction Medicine Consult service at Johns Hopkins Bayview Medical Center, also in Baltimore.⁵ We instructed residents to obtain discharge buprenorphine prescriptions from an X-waivered physician on their team or from the newly established BBT. We asked resident teams to set up a postdischarge appointment for patients with an X-waivered provider, either in a community practice or at the JHH After Care Clinic, a transitional care clinic for discharged patients.²¹

The BBT is a resident-led group of X-waivered JHH residents and hospitalists who volunteer to write discharge buprenorphine prescriptions for patients. The BBT serves to ensure primary teams have access to an X-waivered prescriber. It is not a consult service. We asked primary teams to contact the BBT after initiating buprenorphine and after securing a follow-up appointment. In response to each request, a member of the BBT reviews the patient chart, confirms the follow-up plan, writes a prescription for buprenorphine along with intranasal naloxone, and leaves a brief note. During the 6-month postintervention period, the team consisted of three residents and three hospitalist attendings. Each week, two members (residents or attendings) staffed the team Monday to Friday, 8 am to 5 pm. Most weeks were staffed by two residents. One resident provided services after hours and during weekends. Resident team members ensured that the buprenorphine plan was discussed with the primary team’s attending. For dosing questions beyond the BBT’s scope of knowledge, a member of the BBT relayed questions to physicians from the Addiction Medicine Consult team at Johns Hopkins Bayview Medical Center.

In May 2019, 5 months after the education session and implementation of the BBT, we administered a follow-up survey.

Outcomes

The primary outcome was the percent of inpatients eligible to start OAT who were discharged on buprenorphine maintenance. We obtained data from the electronic medical record. The denominator consisted of patients with OUD not on buprenorphine or methadone maintenance on admission. We identified patients with OUD by an opioid-related International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code or by a standing or as-needed order for buprenorphine or methadone during hospitalization.²² We reviewed admission and discharge documentation to identify patients with OUD who were not in active treatment with buprenorphine or methadone maintenance.

As a secondary outcome, we measured engagement in OUD treatment after discharge by calculating the proportion of patients started on buprenorphine who filled a buprenorphine prescription within 30 days after discharge. We chose 30 days based on the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measure for engagement of treatment for alcohol and other drugs.²³ We obtained the data from the Chesapeake Regional Information System for our Patients (CRISP) Prescription Drug Monitoring Program, which monitors all prescriptions for controlled substances dispensed in Maryland and five neighboring states. As a balancing measure, we counted patients newly started on methadone maintenance for OUD before and after the intervention. Additional secondary process outcomes included frequency of BBT requests, the volume of buprenorphine prescriptions written by the team, and time required to complete a BBT request.

Clinician-level outcomes, measured with electronically administered pre- and postintervention surveys to residents, included knowledge about and comfort with buprenorphine. Of the 16 questions in the pre- and postimplementation surveys, we analyzed the 6 questions concerning knowledge and comfort that remained identical in the pre- and postintervention surveys and used 5-point Likert scale responses. As an incentive, we randomly distributed three $50 gift cards to survey completers.

Analysis

We used an interrupted time series analysis to evaluate the association between the intervention bundle and a change in the rate that medical teams started patients with OUD on buprenorphine maintenance. This approach allowed us to control for preintervention trends. To evaluate the impact of our interventions, our pre- and postintervention periods include the same residents during the 2018-2019 academic year. Both periods consisted of twelve 2-week intervals (preintervention: July 26, 2019, to January 9, 2019; postintervention: January 10, 2019, to June 26, 2019).

To evaluate for changes in engagement in OUD treatment after discharge, we used two-sample t tests. To evaluate for changes in resident-reported comfort and knowledge with initiating buprenorphine maintenance, we used Wilcoxon rank sum tests for survey data and Wilcoxon signed rank tests for paired data. All analyses employed two-sided P values with statistical significance evaluated at the .05 alpha level. We analyzed data using R version 3.6.3 (Foundation for Statistical Computing). The Institutional Review Board at JHH reviewed and approved the study protocol as a quality improvement project (IRB00193365).

RESULTS

During the 24-week preintervention period, internal medicine resident teams started 30 out of 305 eligible patients (10%) on buprenorphine maintenance vs 64 out of 270 eligible patients (24%) during the 24-week postintervention period. Our interrupted time series analysis showed a significant increase in the percent of eligible patients started on buprenorphine maintenance (expected number of patients started postintervention, 27; actual, 64; absolute increase in percent, 14.4%; 95% CI, 3.6%-25.3%; P = .017) (Figure). There was no significant trend during the preintervention period and no significant trend during the postintervention period.

Before the intervention, 13 of the 30 patients (40%) newly started on buprenorphine maintenance during their admission filled a follow-up buprenorphine prescription within 30 days of discharge. After the intervention, 31 of 64 patients (46%) filled a buprenorphine prescription within 30 days (P = .612). Two patients were started on methadone maintenance, one prior to and one after the intervention.

During the 6-month postintervention period, the BBT received 75 requests and wrote 70 prescriptions for buprenorphine. The median time required to complete a BBT request was 15 minutes (minimum, 5 minutes; maximum, 60 minutes).

Of 156 internal medicine and medicine-pediatrics residents, 89 residents (57%) completed the baseline survey and 66 residents (42%) completed the follow-up survey. Forty residents completed both surveys. After the intervention, residents were significantly more likely to feel comfortable dosing buprenorphine (P < .0001) and counseling patients about its use (P = .0237) and were more likely to report ease of establishing follow-up (P < .0001). Self-reported knowledge about preventing precipitated withdrawal increased significantly (P = .0191), as did knowledge about the effectiveness of buprenorphine (P = .0003) independent of formal drug counseling (P = .0066) (Table). Paired survey data also found statistically significant results for all questions except those about preventing precipitated withdrawal and efficacy. For the latter, respondents who completed both surveys were more knowledgeable before the intervention than the overall group that completed the baseline survey (Appendix Table).

DISCUSSION

This study shows how a resident-led quality improvement project comprising clinician education and implementation of a novel BBT was associated with an increased rate of starting buprenorphine maintenance in hospitalized patients with OUD and improved resident knowledge about and comfort with buprenorphine. To our knowledge, this is the first study demonstrating how education and a team of X-waivered generalists can help primary teams initiate and discharge patients on buprenorphine maintenance in a hospital without an addiction medicine consult service.

Prior to the intervention, resident internal medicine teams at JHH started 10% of hospitalized patients with OUD on buprenorphine maintenance, consistent with prior studies showing rates of 11% to 15% for initiating OAT for hospitalized patients.^11,12 After the intervention, the rate of initiating buprenorphine maintenance more than doubled, rising to 24% of eligible patients. Resident internal medicine teams at JHH started buprenorphine maintenance for 37 more patients over the 24-week postintervention period than would have been predicted prior to the intervention, or an additional three patients every 2 weeks.

Between 40% and 46% of hospitalized patients newly started on buprenorphine maintenance filled an outpatient buprenorphine prescription within 30 days of discharge. We are not aware of comparative data for 30-day follow-up for hospitalized patients newly started on buprenorphine maintenance. Data from other contexts show 5% to 10% of veterans were engaged in addiction treatment 30 days after initiation from inpatient or outpatient encounters. An analysis of an academic medical center in Oregon found engagement with an addiction medicine consult service increased after hospital engagement for patients with any substance use disorder from 23% to 39% using the 34-day HEDIS measure for engagement.^17,24,25

The BBT required approximately 15 minutes per request and wrote an average of three prescriptions per week, demonstrating the feasibility of this approach and the high demand for this service. One strength of our approach is that residents gained experience starting buprenorphine independently using the aforementioned protocol instead of deferring to a full consult service. It is likely that this resident engagement in initiating longitudinal OUD care contributed to the success of this initiative, as did existing resident familiarity with using buprenorphine for opioid withdrawal.

This approach to resident education—promoting direct, first-person experience with medications in a clinical context—aligns with recommendations from a recent review about substance use disorder education for health professionals.²⁶ Our interventions increased resident knowledge and comfort with buprenorphine, consistent with prior studies showing increased resident confidence in management of substance use disorders after curricular innovations.^24,25

A few contextual features were essential for this project’s viability. Maryland allows American medical graduates to obtain a medical license after 1 year of postgraduate training. This allowed three residents to obtain X-waivers. These residents had access to HRSA funding to subsidize the expenses of applying for state licensure and DEA registration. BBT members volunteered their time while working on other services. Last, we were able to take advantage of buprenorphine-providing clinics in Baltimore, including the JHH After Care Clinic, to accept patients for follow-up appointments after discharge.

Limitations

The BBT required motivated clinicians willing to volunteer for additional clinical responsibilities during inpatient rotations and supportive faculty and residency leadership. Attending physicians, nurse practitioners, or physician assistants could staff a similar BBT in hospitals without residents or in hospitals where residents cannot obtain DEA registration. Crucially, other hospitals may not have access to practices with X-waivered physicians for outpatient follow-up. A recent study found X-waivered primary care physicians were less likely to be affiliated with hospital health systems. Other studies have shown limitations in access to buprenorphine at the county level based on geography and racial/ethnic segregation.^27-29

Most patients hospitalized with OUD did not have ICD-10 codes associated with OUD. We addressed this by assuming patients had OUD if buprenorphine or methadone was ordered during their hospitalization, even if the medication was never administered. This may have overcounted patients prescribed these medications for indications other than OUD, and it may have undercounted patients with OUD for whom buprenorphine or methadone were never considered. The opioid withdrawal order set at JHH automatically offers an option to use buprenorphine to treat withdrawal. Patients with OUD for whom buprenorphine or methadone were never ordered likely did not experience withdrawal or were in withdrawal so mild that it escaped the attention of the team, which limits the generalizability of our intervention.

We identified several limitations to the internal validity of our study. First, we used a before-and-after study design without a control group. We could not ethically withhold access to evidence-based, mortality-reducing medications from patients. Without a control group, we cannot rule out the possibility that underlying temporal trends made residents more likely to start buprenorphine maintenance independent of our intervention. We attempted to control for unmeasured confounders by using an interrupted time series analysis to control for preintervention trends, comparing the same group of residents before and after our interventions, and selecting an intervention period during which residents were given only educational sessions and materials provided by our team. Our results may be biased by clustered data because certain residents may have been more likely to initiate buprenorphine, but these effects are likely marginal because resident schedules are balanced between outpatient and inpatient rotations during each 6-month period.

Finally, this project focused on buprenorphine, not on other medications for OUD, including methadone or naltrexone, or nonpharmacologic treatments for OUD.

Sustainability and Next Steps

Since the start of the BBT in January 2019, five additional PGY-2 residents obtained their medical licenses and X-waivers. These residents, with the support of two attending hospitalists, led the BBT and coordinated education sessions that were incorporated into the curriculum during the 2019-2020 academic year. These educational sessions will continue indefinitely. In 2020, JHH started an Addiction Medicine Consult Service staffed by physicians, NPs, and a pharmacist. The BBT continues to operate in conjunction with this service.

We found substantial variability in the rate of buprenorphine maintenance initiation despite our interventions. This is an area for future improvement. In a free-response prompt in our follow-up survey, residents requested additional education sessions and an order set to assist with initiation of buprenorphine. To address these gaps, three educational sessions were added, one of which included education on starting methadone maintenance therapy. We also added a new order set for starting buprenorphine maintenance. We hypothesize that these interventions will improve consistency.

In order for a similar program to be disseminated to other institutions, educational initiatives and a team of dedicated X-waivered prescribers are key. Materials to assist with this process are available in the Appendix.

CONCLUSION

This study shows how a resident-led intervention comprising clinician education and a team of X-waivered generalists was associated with improved treatment of OUD for hospitalized patients. We encourage residents and all clinicians at other hospitals without addiction medicine consult services to design, implement, and study similar interventions that directly increase the use of buprenorphine or methadone maintenance to treat OUD.

Preliminary results from this project were presented at the AMERSA National Conference on November 7, 2019.

Nearly 48,000 Americans died from overdoses involving opioids in 2018, continuing a national crisis that has led to 446,000 deaths since 1999.¹ Annually, opioids are responsible for more than 500,000 admissions, approximately 1% of all hospitalizations, costing the United States nearly $15 billion.^2,3 Among hospitalized patients, chronic opioid use is associated with increased mortality, severe infectious complications, and higher rates of readmission.⁴ Opioid use disorder (OUD) is a chronic, relapsing medical condition with biopsychosocial origins and significant morbidity and mortality.⁵ Opioid agonist therapy (OAT) with buprenorphine or methadone maintenance, the evidence-based standard of treatment, reduces the mortality rate by half, decreases overdoses and hospital readmissions, and improves retention in care.^6-10

OAT maintenance refers to using buprenorphine or methadone for long-term treatment of OUD rather than for acute treatment of opioid withdrawal. Despite evidence supporting OAT maintenance, clinicians start medications for only 11% to 15% of hospitalized patients with OUD, depending on practice contexts.^11,12 Three significant barriers—stigma, insufficient clinician education, and restrictive regulations—prevent clinicians from starting OAT.¹³ Clinicians who do not have the Drug Enforcement Administration (DEA)–issued DATA-2000 waiver (X-waiver) for outpatient prescribing can order buprenorphine for admitted patients but cannot prescribe it at discharge.¹⁴ In hospitals where they exist, addiction medicine consult services offer primary teams guidance on pharmacotherapy, leading to reduced hospital readmissions and increased engagement in outpatient addiction treatment.^15-17 However, in most hospitals around the country, such specialty services do not exist.¹⁸ In some hospitals without addiction medicine consult services, hospitalists with expertise in OUD have started assisting primary teams in starting OAT, but to our knowledge, no prior studies have described the impact of these interventions on patients or clinician experience with OAT.¹⁹

This quality improvement project aimed to increase the rate at which internal medicine resident teams at Johns Hopkins Hospital (JHH) in Baltimore, Maryland, started hospitalized patients with OUD on buprenorphine maintenance. We hypothesized that resident education and measures to increase the availability of X-waivered physicians would increase the rate of initiating buprenorphine maintenance. We additionally hypothesized that these interventions would increase knowledge about and comfort with buprenorphine across the residency. This represents the first study to examine the effects of clinician education and a team of X-waivered residents and hospitalists who assist in starting buprenorphine maintenance in a hospital without an addiction medicine consult service.

METHODS

Setting

This study took place from July 2018 to June 2019 at JHH, a large, academic, urban hospital in Baltimore. Prior to the intervention, internal medicine residents at JHH commonly used short courses of buprenorphine to treat withdrawal, but they did not have access to hospital-specific resources to assist with starting maintenance OAT. During the study period, JHH had a Substance Use Disorders team staffed by peer recovery specialists that could be consulted by hospitalists and residents to provide psychosocial support and link admitted patients to treatment after discharge. There were no providers on the team to guide pharmacotherapy or to write discharge buprenorphine prescriptions. The Osler Medical Residency Training Program at JHH has 140 internal medicine residents and 16 combined medicine-pediatrics residents. All residents receive 1 hour of formal education about opioid use disorder annually. In addition, 28 of those 156 residents, those in the Urban Health Primary Care track, spend 1 month on an Addiction Medicine rotation in which they complete the 8-hour training required to receive the X-waiver. Those residents are encouraged to apply for the X-waiver once they obtain a medical license subsidized by a Health Resources & Services Administration (HRSA) grant. Four internal medicine attending physicians on teaching services and one resident had X-waivers prior to the intervention.

Intervention

In November 2018, we administered a survey to residents to identify barriers to starting buprenorphine maintenance and to measure knowledge and confidence with using buprenorphine for OUD (Appendix Figure 1 and Figure 2). We focused on buprenorphine because providers at JHH were familiar with this medication and because Baltimore has widespread access to buprenorphine, with more than 490 local buprenorphine providers.²⁰ Five residents piloted the survey and provided feedback. We then administered the survey to all internal medicine and medicine-pediatrics residents. Based on the results, we developed a targeted educational conference and also created the Buprenorphine Bridge Team (BBT).

In January 2019, we presented the educational conference for residents devoted to the use of buprenorphine for OUD and introduced the BBT. The conference started with a patient testimonial and included peer recovery specialists, pharmacists, nurses, and social workers. We summarized the evidence for buprenorphine and offered a practical guide to start treatment in a one-page protocol. This protocol included guidance on selecting patients, shared decision-making around OUD treatment, avoiding precipitated withdrawal, dosing buprenorphine, and establishing follow-up (Appendix Figure 3). We asked for input on this protocol from nursing leadership, social work teams, and peer recovery specialists. Dosing was adapted from the Guidelines from the American Society of Addiction Medicine, with expert input from physicians from the Addiction Medicine Consult service at Johns Hopkins Bayview Medical Center, also in Baltimore.⁵ We instructed residents to obtain discharge buprenorphine prescriptions from an X-waivered physician on their team or from the newly established BBT. We asked resident teams to set up a postdischarge appointment for patients with an X-waivered provider, either in a community practice or at the JHH After Care Clinic, a transitional care clinic for discharged patients.²¹

The BBT is a resident-led group of X-waivered JHH residents and hospitalists who volunteer to write discharge buprenorphine prescriptions for patients. The BBT serves to ensure primary teams have access to an X-waivered prescriber. It is not a consult service. We asked primary teams to contact the BBT after initiating buprenorphine and after securing a follow-up appointment. In response to each request, a member of the BBT reviews the patient chart, confirms the follow-up plan, writes a prescription for buprenorphine along with intranasal naloxone, and leaves a brief note. During the 6-month postintervention period, the team consisted of three residents and three hospitalist attendings. Each week, two members (residents or attendings) staffed the team Monday to Friday, 8 am to 5 pm. Most weeks were staffed by two residents. One resident provided services after hours and during weekends. Resident team members ensured that the buprenorphine plan was discussed with the primary team’s attending. For dosing questions beyond the BBT’s scope of knowledge, a member of the BBT relayed questions to physicians from the Addiction Medicine Consult team at Johns Hopkins Bayview Medical Center.

In May 2019, 5 months after the education session and implementation of the BBT, we administered a follow-up survey.

Outcomes

The primary outcome was the percent of inpatients eligible to start OAT who were discharged on buprenorphine maintenance. We obtained data from the electronic medical record. The denominator consisted of patients with OUD not on buprenorphine or methadone maintenance on admission. We identified patients with OUD by an opioid-related International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code or by a standing or as-needed order for buprenorphine or methadone during hospitalization.²² We reviewed admission and discharge documentation to identify patients with OUD who were not in active treatment with buprenorphine or methadone maintenance.

As a secondary outcome, we measured engagement in OUD treatment after discharge by calculating the proportion of patients started on buprenorphine who filled a buprenorphine prescription within 30 days after discharge. We chose 30 days based on the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measure for engagement of treatment for alcohol and other drugs.²³ We obtained the data from the Chesapeake Regional Information System for our Patients (CRISP) Prescription Drug Monitoring Program, which monitors all prescriptions for controlled substances dispensed in Maryland and five neighboring states. As a balancing measure, we counted patients newly started on methadone maintenance for OUD before and after the intervention. Additional secondary process outcomes included frequency of BBT requests, the volume of buprenorphine prescriptions written by the team, and time required to complete a BBT request.

Clinician-level outcomes, measured with electronically administered pre- and postintervention surveys to residents, included knowledge about and comfort with buprenorphine. Of the 16 questions in the pre- and postimplementation surveys, we analyzed the 6 questions concerning knowledge and comfort that remained identical in the pre- and postintervention surveys and used 5-point Likert scale responses. As an incentive, we randomly distributed three $50 gift cards to survey completers.

Analysis

We used an interrupted time series analysis to evaluate the association between the intervention bundle and a change in the rate that medical teams started patients with OUD on buprenorphine maintenance. This approach allowed us to control for preintervention trends. To evaluate the impact of our interventions, our pre- and postintervention periods include the same residents during the 2018-2019 academic year. Both periods consisted of twelve 2-week intervals (preintervention: July 26, 2019, to January 9, 2019; postintervention: January 10, 2019, to June 26, 2019).

To evaluate for changes in engagement in OUD treatment after discharge, we used two-sample t tests. To evaluate for changes in resident-reported comfort and knowledge with initiating buprenorphine maintenance, we used Wilcoxon rank sum tests for survey data and Wilcoxon signed rank tests for paired data. All analyses employed two-sided P values with statistical significance evaluated at the .05 alpha level. We analyzed data using R version 3.6.3 (Foundation for Statistical Computing). The Institutional Review Board at JHH reviewed and approved the study protocol as a quality improvement project (IRB00193365).

RESULTS

During the 24-week preintervention period, internal medicine resident teams started 30 out of 305 eligible patients (10%) on buprenorphine maintenance vs 64 out of 270 eligible patients (24%) during the 24-week postintervention period. Our interrupted time series analysis showed a significant increase in the percent of eligible patients started on buprenorphine maintenance (expected number of patients started postintervention, 27; actual, 64; absolute increase in percent, 14.4%; 95% CI, 3.6%-25.3%; P = .017) (Figure). There was no significant trend during the preintervention period and no significant trend during the postintervention period.

Before the intervention, 13 of the 30 patients (40%) newly started on buprenorphine maintenance during their admission filled a follow-up buprenorphine prescription within 30 days of discharge. After the intervention, 31 of 64 patients (46%) filled a buprenorphine prescription within 30 days (P = .612). Two patients were started on methadone maintenance, one prior to and one after the intervention.

During the 6-month postintervention period, the BBT received 75 requests and wrote 70 prescriptions for buprenorphine. The median time required to complete a BBT request was 15 minutes (minimum, 5 minutes; maximum, 60 minutes).

Of 156 internal medicine and medicine-pediatrics residents, 89 residents (57%) completed the baseline survey and 66 residents (42%) completed the follow-up survey. Forty residents completed both surveys. After the intervention, residents were significantly more likely to feel comfortable dosing buprenorphine (P < .0001) and counseling patients about its use (P = .0237) and were more likely to report ease of establishing follow-up (P < .0001). Self-reported knowledge about preventing precipitated withdrawal increased significantly (P = .0191), as did knowledge about the effectiveness of buprenorphine (P = .0003) independent of formal drug counseling (P = .0066) (Table). Paired survey data also found statistically significant results for all questions except those about preventing precipitated withdrawal and efficacy. For the latter, respondents who completed both surveys were more knowledgeable before the intervention than the overall group that completed the baseline survey (Appendix Table).

DISCUSSION

This study shows how a resident-led quality improvement project comprising clinician education and implementation of a novel BBT was associated with an increased rate of starting buprenorphine maintenance in hospitalized patients with OUD and improved resident knowledge about and comfort with buprenorphine. To our knowledge, this is the first study demonstrating how education and a team of X-waivered generalists can help primary teams initiate and discharge patients on buprenorphine maintenance in a hospital without an addiction medicine consult service.

Prior to the intervention, resident internal medicine teams at JHH started 10% of hospitalized patients with OUD on buprenorphine maintenance, consistent with prior studies showing rates of 11% to 15% for initiating OAT for hospitalized patients.^11,12 After the intervention, the rate of initiating buprenorphine maintenance more than doubled, rising to 24% of eligible patients. Resident internal medicine teams at JHH started buprenorphine maintenance for 37 more patients over the 24-week postintervention period than would have been predicted prior to the intervention, or an additional three patients every 2 weeks.

Between 40% and 46% of hospitalized patients newly started on buprenorphine maintenance filled an outpatient buprenorphine prescription within 30 days of discharge. We are not aware of comparative data for 30-day follow-up for hospitalized patients newly started on buprenorphine maintenance. Data from other contexts show 5% to 10% of veterans were engaged in addiction treatment 30 days after initiation from inpatient or outpatient encounters. An analysis of an academic medical center in Oregon found engagement with an addiction medicine consult service increased after hospital engagement for patients with any substance use disorder from 23% to 39% using the 34-day HEDIS measure for engagement.^17,24,25

The BBT required approximately 15 minutes per request and wrote an average of three prescriptions per week, demonstrating the feasibility of this approach and the high demand for this service. One strength of our approach is that residents gained experience starting buprenorphine independently using the aforementioned protocol instead of deferring to a full consult service. It is likely that this resident engagement in initiating longitudinal OUD care contributed to the success of this initiative, as did existing resident familiarity with using buprenorphine for opioid withdrawal.

This approach to resident education—promoting direct, first-person experience with medications in a clinical context—aligns with recommendations from a recent review about substance use disorder education for health professionals.²⁶ Our interventions increased resident knowledge and comfort with buprenorphine, consistent with prior studies showing increased resident confidence in management of substance use disorders after curricular innovations.^24,25

A few contextual features were essential for this project’s viability. Maryland allows American medical graduates to obtain a medical license after 1 year of postgraduate training. This allowed three residents to obtain X-waivers. These residents had access to HRSA funding to subsidize the expenses of applying for state licensure and DEA registration. BBT members volunteered their time while working on other services. Last, we were able to take advantage of buprenorphine-providing clinics in Baltimore, including the JHH After Care Clinic, to accept patients for follow-up appointments after discharge.

Limitations

The BBT required motivated clinicians willing to volunteer for additional clinical responsibilities during inpatient rotations and supportive faculty and residency leadership. Attending physicians, nurse practitioners, or physician assistants could staff a similar BBT in hospitals without residents or in hospitals where residents cannot obtain DEA registration. Crucially, other hospitals may not have access to practices with X-waivered physicians for outpatient follow-up. A recent study found X-waivered primary care physicians were less likely to be affiliated with hospital health systems. Other studies have shown limitations in access to buprenorphine at the county level based on geography and racial/ethnic segregation.^27-29

Most patients hospitalized with OUD did not have ICD-10 codes associated with OUD. We addressed this by assuming patients had OUD if buprenorphine or methadone was ordered during their hospitalization, even if the medication was never administered. This may have overcounted patients prescribed these medications for indications other than OUD, and it may have undercounted patients with OUD for whom buprenorphine or methadone were never considered. The opioid withdrawal order set at JHH automatically offers an option to use buprenorphine to treat withdrawal. Patients with OUD for whom buprenorphine or methadone were never ordered likely did not experience withdrawal or were in withdrawal so mild that it escaped the attention of the team, which limits the generalizability of our intervention.

We identified several limitations to the internal validity of our study. First, we used a before-and-after study design without a control group. We could not ethically withhold access to evidence-based, mortality-reducing medications from patients. Without a control group, we cannot rule out the possibility that underlying temporal trends made residents more likely to start buprenorphine maintenance independent of our intervention. We attempted to control for unmeasured confounders by using an interrupted time series analysis to control for preintervention trends, comparing the same group of residents before and after our interventions, and selecting an intervention period during which residents were given only educational sessions and materials provided by our team. Our results may be biased by clustered data because certain residents may have been more likely to initiate buprenorphine, but these effects are likely marginal because resident schedules are balanced between outpatient and inpatient rotations during each 6-month period.

Finally, this project focused on buprenorphine, not on other medications for OUD, including methadone or naltrexone, or nonpharmacologic treatments for OUD.

Sustainability and Next Steps

Since the start of the BBT in January 2019, five additional PGY-2 residents obtained their medical licenses and X-waivers. These residents, with the support of two attending hospitalists, led the BBT and coordinated education sessions that were incorporated into the curriculum during the 2019-2020 academic year. These educational sessions will continue indefinitely. In 2020, JHH started an Addiction Medicine Consult Service staffed by physicians, NPs, and a pharmacist. The BBT continues to operate in conjunction with this service.

We found substantial variability in the rate of buprenorphine maintenance initiation despite our interventions. This is an area for future improvement. In a free-response prompt in our follow-up survey, residents requested additional education sessions and an order set to assist with initiation of buprenorphine. To address these gaps, three educational sessions were added, one of which included education on starting methadone maintenance therapy. We also added a new order set for starting buprenorphine maintenance. We hypothesize that these interventions will improve consistency.

In order for a similar program to be disseminated to other institutions, educational initiatives and a team of dedicated X-waivered prescribers are key. Materials to assist with this process are available in the Appendix.

CONCLUSION

This study shows how a resident-led intervention comprising clinician education and a team of X-waivered generalists was associated with improved treatment of OUD for hospitalized patients. We encourage residents and all clinicians at other hospitals without addiction medicine consult services to design, implement, and study similar interventions that directly increase the use of buprenorphine or methadone maintenance to treat OUD.

Preliminary results from this project were presented at the AMERSA National Conference on November 7, 2019.

ABSTRACT

Purpose Medical scribes are known to increase revenue by increasing visits to a medical practice. We examined whether medical scribes are associated with markers of financial benefit independent of increased visits.

Methods We conducted a pre- and post-­observational study with a control group, examining changes in the percentage of visits (1) coded as level of service 4 or 5, (2) with at least 1 hierarchical condition category code billed, and (3) at which orders for 3 pay-for-performance quality measures (screening for breast, cervical, and colon cancer) were placed, if due. We looked at changes in outcomes among scribed providers and compared them to nonscribed providers. We used generalized estimating equations with robust standard errors to account for repeated measures and the hierarchical nature of the data, controlling for patient demographics.

Results We examined 41,371 visits to 17 scribed providers and 230,297 visits to 78 nonscribed providers. In adjusted analyses, and compared to nonscribed providers, scribes were associated with an increase of:

• Frutiger LT Std9.2 percentage points in level-of-­service 4 or 5 billing (Frutiger LT StdP < .001)
• 3.6 percentage points in hierarchical condition category coding (Frutiger LT StdP < .001)
• 4.0 percentage points in breast cancer screening orders (Frutiger LT StdP = .01)
• 4.9 percentage points in colon cancer screening orders (Frutiger LT StdPFrutiger LT Std = .04).

Conclusions This study suggests that scribes are associated with financial benefit in addition to increased visit volume. Primary care practices should consider the financial benefit of scribes independent of their ability to add patient volume.

Increasingly, medical scribes are used in ambulatory care settings across the United States.¹ Scribes are trained personnel who accompany providers during visits to provide documentation support and assist with other administrative tasks. They are associated with reduced documentation time for providers^2-6 and improved provider satisfaction,^7-11 without detriment to^4-16 (or with possible improvement in^17-20) patient satisfaction in ambulatory care settings. At the same time, concerns remain that using scribes might inhibit patient communication, harm clinical reasoning, reduce the effectiveness of clinical decision-support tools, and simply serve as a work-around to fixing inefficiencies in the electronic medical record (EMR).^21-23

A driving force for the increased use of medical scribes is the expectation that they reduce the cost of providing care. Cost-­efficiency is typically described as resulting from a reduction in physician time per patient seen, which allows increased patient volume and, in turn, drives increased physician productivity.^{2,8,10,18,24-27} Whether scribes result in cost savings remains unclear; some papers suggest that scribes are cost efficient in ambulatory care,^2,10,18 while others have been unable to identify cost savings, particularly in primary care.⁴

One reason why scribes might not be associated with cost savings is that their financial benefit might be undercounted. Studies that focus on increased volume miss the opportunity to capture financial benefits conferred through mechanisms that are independent of seeing more patients:

• Scribes might help providers address and document more, and more complex, medical problems, allowing higher level-of-service (LOS) billing. For example, a provider chooses a lower LOS because they have insufficiently documented a visit to support a higher LOS; by assisting with documentation, the scribe might allow the provider to choose a higher LOS.
• Scribes might prompt a provider to use decision-support tools for risk coding (using appropriate medical codes to capture the patient’s level of medical complexity), thereby increasing reimbursement.
• Scribes might extend the time available during the visit for the provider to address pay-for-performance quality measures, such as cancer screening.

Continue to: Making visits count, not counting visits

 

Making visits count, not counting visits. In this study, we examined whether medical scribes in primary care are associated with improved markers of revenue that are independent of seeing more patients. Specifically, we examined whether scribes are associated with increased LOS coding, risk coding, and orders for pay-for-performance measures for all primary care visits and for nonpreventive primary care visits.

Methods

Design

This observational study compared the change in outcomes before implementation of scribes and during implementation of scribes, between scribed providers and nonscribed providers. We compared visits during the year prior to the implementation of scribes (July 2017–June 2018) with the year during their implementation (July 2018–2019).

The Cambridge Health Alliance Institutional Review Board considered this study exempt from review.

Setting

This study was conducted at a safety-net community academic health system that uses an EMR developed by Epic Systems [Verona, WI]. This EMR includes decision-support tools that prompt providers when pay-for-performance quality measures are due and when hierarchical condition category (HCC) codes—ie, specific diagnoses used by Medicare and other payers to reimburse providers for the complexity of their patients—might apply to the visit.

Primary care practices should consider the financial benefit of scribes, independent of their ability to add patient volume.

These EMR decision-support tools use algorithms that draw on age, gender, diagnoses that were billed previously or are on the problem list, laboratory findings, and prior imaging. They alert physicians when a patient is due for pay-for-performance quality measures, such as cancer screenings, and when HCC codes might be applicable.

Continue to: During the study period...

 

During the study period, the EMR ­decision-support tool for HCC coding underwent several changes designed to improve HCC coding. In addition, systematic changes to primary care visits took place, leading to an increase in the number of patients seen and screenings required.

Outcomes

We examined 2 categories of outcomes that confer financial benefit to many institutions: billing measures and pay-for-performance measures.

Billing measures included the percentage of visits (1) coded as LOS 4 or 5 and (2) with at least 1 HCC code billed (among those for which the decision-support tool identified at least 1 potential HCC code).

Pay-for-performance measures. We examined whether any of 3 pay-for-­performance quality measures were addressed during the visit, selecting 3 that are commonly addressed by primary care providers (PCPs) and that require PCPs to sign an order for screening during a primary care visit: breast cancer (mammography order), cervical cancer (Papanicolaou smear order), and colon cancer (an order for fecal occult blood testing or colonoscopy).

Intervention

Scribes were employees of Cambridge Health Alliance who had recently graduated from college and were interested in a career as a health care professional. Scribes received 3 days of training on how to function effectively in their role; 1 day of training in EMR functionality; and 2 hours of training on decision-support tools for pay-for-performance quality measures and risk coding. Scribes continued learning on the job through feedback from supervising PCPs. Scribes documented patient encounters, recording histories and findings on the physical exam and transcribing discussion of treatment plans and the PCP’s instructions to patients.

Continue to: The 14 scribes worked with 17 physician...

 

The 14 scribes worked with 17 physician and nurse practitioner PCPs beginning in July 2018. Participation by PCPs was voluntary; they received no compensation for participating in the scribe program. PCPs were not required to see additional patients to participate. PCPs who chose to work with a scribe were similar to those who declined a scribe, as regards gender, race, type of provider (MD or NP), tenure at the institution, and percentage of time in clinical work (see Table W-1).

The control group comprised providers who elected not to work with a scribe but who worked in the same clinics as the intervention providers.

Scribes might extend the time available during the visit for the provider to address pay-forperformance quality measures, such as cancer screening.

Scribes were assigned to a PCP based on availability during the PCP’s scheduled hours and worked with 1 PCP throughout the intervention (except for 1 PCP who worked with 2 scribes). All PCPs worked with their scribe(s) part time; on average, 49% of intervention PCPs’ visits were scribed.

Inclusion and exclusion criteria

Because the first year at an institution is a learning period for PCPs, we excluded those who worked at the institution for < 1 year before the start of the scribe program (n = 12). Based on the extensive clinical experience of 1 PCP (WA) with scribes, we excluded the first 200 visits or 6 weeks (whichever occurred first) with a scribe among all scribed providers, to account for an initial learning period (n = 2202, of 15,372 scribed visits [14%]). We also excluded 2 providers who left during the pre-intervention period or were in the intervention period for < 1 month.

To ensure that we captured visits to providers with clinically significant exposure to scribes, we required scribed providers to have ≥ 20% of their visits scribed during the intervention period. To minimize the potential for contamination, we excluded nonscribed visits to scribed providers during the intervention period (n = 2211), because such nonscribed visits were largely due to visits outside the scribe’s scheduled time.

Continue to: Analysis

 

 

Analysis

We compared demographic characteristics for patients and providers using the chi-square test for categorical variables and the t test for continuous variables. We compared the change in outcomes from before implementation of scribes to during implementation of scribes among scribed providers, compared to nonscribed providers, using generalized estimating equations with robust standard errors to account for repeated measures (ie, multiple visits by the same patients) and the hierarchical nature of the data (ie, patients nested within providers). We then recalculated these estimates, controlling for patient demographics (age, gender, race, and ethnicity). We repeated these analyses for patients presenting for nonpreventive visits.

Results

Visit characteristics

We examined 271,768 visits, including 41,371 visits to 17 scribed providers and 230,397 visits to 78 nonscribed providers (Table 1). Patients were most likely to be female, > 21 years of age, have English as their language of care, and be non-White. Most visits were by established patients and were nonpreventive.

We noted no clinically significant differences in characteristics between visits with scribed providers and visits with nonscribed providers, and over time. Patient complexity measures, including care management enrollment and hospital admissions, were also similar between groups, and over time.

Billing measures

HCC coding. In 28.6% of visits, the decision-support tool identified at least 1 potential HCC code. Among these, the percentage of visits with at least 1 HCC code billed increased by 10.1 percentage points (from 3.9% before implementation of scribes to 14.0%) among scribed providers, compared to increasing by 6.5 percentage points (from 2.9% before implementation to 9.3%) among nonscribed providers (TABLE 2). Scribes were therefore associated with an additional 3.6 percentage-point increase in visits with at least 1 HCC code billed (P < .0001)—a difference that remained significant after adjusting for patient demographics (P < .0001).

LOS coding. Scribed providers increased the number of visits billed as LOS 4 or 5 by 9.6 percentage points (from 47.3% before implementation to 56.8%); during the same period, nonscribed providers increased the number of visits billed as LOS 4 or 5 by 1.3 percentage points (from 46.5% before implementation to 47.8%) (TABLE 2). Scribes were therefore associated with an additional 8.3 percentage points in LOS 4 or 5 billing (P < .001) (TABLE 2). This difference remained significant after adjusting for patient demographics (P < .001).

Continue to: Pay-for-performance quality measures

 

 

Pay-for-performance quality measures

Breast cancer screening. Scribed providers increased the number of visits at which breast cancer screening was ordered by 2.7 percentage points (from 17.3% before implementation of scribes to 20.0%); during the same period, the number of visits at which breast cancer screening was ordered by nonscribed providers decreased by 1.9 percentage points (from 19.5% to 17.6%). Scribes were therefore associated with an increase of 4.6 percentage points in breast cancer screening orders, compared to nonscribed providers (P < .003) (TABLE 2). That difference remained significant after adjusting for patient demographics (P = .01).

Colon cancer screening. Similarly, scribed providers increased the number of visits at which colon cancer screening was ordered by 1.2 percentage points (from 19.2% before implementation of scribes to 20.3%); during the same period, the number of visits at which colon cancer screening was ordered by nonscribed providers decreased by 2.7 percentage points (from 18.5% to 15.9%) (P = .112). After adjusting for patient demographics, scribes were associated with an increase of 4.9 percentage points in colon cancer screening orders, compared to nonscribed providers (P = .044) (TABLE 2).

Cervical cancer screening. The rate at which cervical cancer screening was ordered did not change among scribed providers and decreased (by 2.5 percentage points) among nonscribed providers—a difference that was not statistically significant (P = .26).

Nonpreventive visits. Our findings overall did not change in analyses focused on nonpreventive visits, in which scribes were associated with an increase of 8.2 percentage points in LOS 4 or 5 billing (P < .001); an increase of 3.1 percentage points in HCC coding (P < .001); and an increase of 3.2 percentage points in breast cancer screening orders (P = .03) (TABLE 3). Although scribes were associated with an increase of 1.5 percentage points in cervical cancer screening orders and an increase of 3.1 percentage points in colon cancer screening orders, these increases did not reach statistical significance.

Discussion

We found that implementation of scribes is associated with (1) an increase in LOS coding and risk coding and (2) a higher frequency of addressing 2 of 3 pay-for-performance quality measures in primary care. In adjusted analyses in our study, and compared to nonscribed providers, scribes were associated with an additional 9.2 percentage points in LOS 4 or 5 billing; 3.6 percentage points in HCC coding; 4.0 percentage points in breast cancer screening orders; and 4.9 percentage points in colon cancer screening orders. Cervical cancer screening orders followed a similar pattern, with an increase of 2.3 percentage points in the adjusted screening order rate among scribed providers, compared to nonscribed providers, during implementation of scribes—although the increase was not significant. These findings did not change in analyses focused on nonpreventive visits.

Continue to: Our findings are consistent

 

Our findings are consistent with those of earlier studies. Prior examinations in ambulatory specialties found that scribes increased HCC coding,⁴ LOS billing,²⁴ and pay-for-performance metrics.¹⁸ The only study to examine these areas in primary care found that scribes were associated with increased pay-for-performance measure documentation,²⁰ a change that is necessary but insufficient to realize increased pay-for-­performance revenue. Therefore, our study confirms, for the first time, that PCPs can better address pay-for-performance measures, LOS billing, and HCC coding when working with a scribe in primary care.

Providers who used a scribe during the intervention period of our study increased the number of visits billed as LOS 4 or 5 by 9.6 percentage points.

Demands on primary care visits are increasing.²⁸ Physicians are required to provide more documentation; there is greater emphasis on PCPs meeting pay-for-­performance measures; and there are more data in the EMR to review. In this context, addressing pay-for-performance measures and gaps in risk coding is likely to be increasingly challenging. Our study suggests that scribes might provide a mechanism to increase risk coding, LOS billing, and pay-for-performance measures, despite increased demands on primary care visits.

Increase in LOS billing. In the settings in which we work, a fee-for-service LOS 4 primary care visit generates, on average, $20 to $75 more in revenue than an LOS 3 visit. Using an average of $50 additional revenue for LOS 4 billing, we estimate that a full-time scribe is associated with roughly $7,000 in additional revenue annually. We arrived at this estimate using an average of 1500 visits at LOS ≤ 3 for every PCP full-time equivalent. A 9.2 percentage–point increase in LOS 4 billing would lead to roughly 140 additional LOS 4 visits, with each visit generating an additional $50 in revenue.

This analysis does not account for increased revenue associated with increased pay for HCC coding identified in our study.

Our study also suggests that scribes might provide a mechanism to increase risk coding and payfor-performance, despite increased demands on primary care visits.

Furthermore, in our conservative assumption, the entire increase in LOS billing was from level 3 to level 4; in fact, a small percentage of that increase would be from level 2 and another small percentage would be to level 5—both of which would generate additional revenue. Our assumption therefore underestimates the full financial value associated with scribes in the absence of increased patient volume. Nonetheless, the assumption suggests that increases in LOS billing offset a substantial percentage of a scribe’s salary.

Continue to: Limitations of this study

 

Limitations of this study. Our study should be interpreted in the context of several limitations:

• The study was conducted at 1 institution. Our findings might not be generalizable beyond this setting.
• The study measures the impact of scribes when providers work with scribes part time. Because providers who utilize a scribe for all, or nearly all, their visits are likely to use a scribe more efficiently, our study might underestimate the full impact of a scribe.
• In some settings, team members such as medical assistants are trained to assist with documentation and other responsibilities (such as closing care gaps) in addition to other patient care responsibilities.^29-32 The extent to which our findings transfer to other models is unclear; studies comparing the impact of other models (which might provide even stronger outcomes) to the impact of medical scribes would be an interesting area for further research.
• In addition to the variability of models, there is likely variability in the quality and interactions of medical scribes, which might impact outcomes. We did not examine the qualities of scribes that led to outcomes in this study.
• We examined the impact of scribes on quality measure–ordering behaviors of providers, not on whether quality measures actually improved. Because scribes are associated with more face-to-face time with patients,²⁷ they might allow for increased attention being paid by physicians to barriers to pay-for-performance measures (eg, patient education). This could increase the likelihood that patients complete a multitude of screenings, and thus improve adherence and follow-up. However, the impact of scribes on quality measures is a topic for future study.

Value beyond volume. Any limitations notwithstanding, our study suggests that scribes are associated with financial benefit in addition to the benefit of increased volume. Primary care practices should therefore consider the financial benefit of scribes independent of their ability to add patient volume. By recognizing this additive value, primary care practices might more fully capture the benefit of scribes, which might then allow practices to employ scribes with less demand to increase volume. This added support without increased volume would, in turn, likely reduce provider burnout (and the costly associated turnover) and increase patient satisfaction, leading to a synergistic financial benefit.

CORRESPONDENCE
Wayne Altman, MD, FAAFP, Tufts University School of Medicine, 200 Harrison Avenue, Boston, MA 02111; wayne. altman@tufts.edu

ABSTRACT

Purpose Medical scribes are known to increase revenue by increasing visits to a medical practice. We examined whether medical scribes are associated with markers of financial benefit independent of increased visits.

Methods We conducted a pre- and post-­observational study with a control group, examining changes in the percentage of visits (1) coded as level of service 4 or 5, (2) with at least 1 hierarchical condition category code billed, and (3) at which orders for 3 pay-for-performance quality measures (screening for breast, cervical, and colon cancer) were placed, if due. We looked at changes in outcomes among scribed providers and compared them to nonscribed providers. We used generalized estimating equations with robust standard errors to account for repeated measures and the hierarchical nature of the data, controlling for patient demographics.

Results We examined 41,371 visits to 17 scribed providers and 230,297 visits to 78 nonscribed providers. In adjusted analyses, and compared to nonscribed providers, scribes were associated with an increase of:

• Frutiger LT Std9.2 percentage points in level-of-­service 4 or 5 billing (Frutiger LT StdP < .001)
• 3.6 percentage points in hierarchical condition category coding (Frutiger LT StdP < .001)
• 4.0 percentage points in breast cancer screening orders (Frutiger LT StdP = .01)
• 4.9 percentage points in colon cancer screening orders (Frutiger LT StdPFrutiger LT Std = .04).

Conclusions This study suggests that scribes are associated with financial benefit in addition to increased visit volume. Primary care practices should consider the financial benefit of scribes independent of their ability to add patient volume.

Increasingly, medical scribes are used in ambulatory care settings across the United States.¹ Scribes are trained personnel who accompany providers during visits to provide documentation support and assist with other administrative tasks. They are associated with reduced documentation time for providers^2-6 and improved provider satisfaction,^7-11 without detriment to^4-16 (or with possible improvement in^17-20) patient satisfaction in ambulatory care settings. At the same time, concerns remain that using scribes might inhibit patient communication, harm clinical reasoning, reduce the effectiveness of clinical decision-support tools, and simply serve as a work-around to fixing inefficiencies in the electronic medical record (EMR).^21-23

A driving force for the increased use of medical scribes is the expectation that they reduce the cost of providing care. Cost-­efficiency is typically described as resulting from a reduction in physician time per patient seen, which allows increased patient volume and, in turn, drives increased physician productivity.^{2,8,10,18,24-27} Whether scribes result in cost savings remains unclear; some papers suggest that scribes are cost efficient in ambulatory care,^2,10,18 while others have been unable to identify cost savings, particularly in primary care.⁴

One reason why scribes might not be associated with cost savings is that their financial benefit might be undercounted. Studies that focus on increased volume miss the opportunity to capture financial benefits conferred through mechanisms that are independent of seeing more patients:

• Scribes might help providers address and document more, and more complex, medical problems, allowing higher level-of-service (LOS) billing. For example, a provider chooses a lower LOS because they have insufficiently documented a visit to support a higher LOS; by assisting with documentation, the scribe might allow the provider to choose a higher LOS.
• Scribes might prompt a provider to use decision-support tools for risk coding (using appropriate medical codes to capture the patient’s level of medical complexity), thereby increasing reimbursement.
• Scribes might extend the time available during the visit for the provider to address pay-for-performance quality measures, such as cancer screening.

Continue to: Making visits count, not counting visits

 

Making visits count, not counting visits. In this study, we examined whether medical scribes in primary care are associated with improved markers of revenue that are independent of seeing more patients. Specifically, we examined whether scribes are associated with increased LOS coding, risk coding, and orders for pay-for-performance measures for all primary care visits and for nonpreventive primary care visits.

Methods

Design

This observational study compared the change in outcomes before implementation of scribes and during implementation of scribes, between scribed providers and nonscribed providers. We compared visits during the year prior to the implementation of scribes (July 2017–June 2018) with the year during their implementation (July 2018–2019).

The Cambridge Health Alliance Institutional Review Board considered this study exempt from review.

Setting

This study was conducted at a safety-net community academic health system that uses an EMR developed by Epic Systems [Verona, WI]. This EMR includes decision-support tools that prompt providers when pay-for-performance quality measures are due and when hierarchical condition category (HCC) codes—ie, specific diagnoses used by Medicare and other payers to reimburse providers for the complexity of their patients—might apply to the visit.

Primary care practices should consider the financial benefit of scribes, independent of their ability to add patient volume.

These EMR decision-support tools use algorithms that draw on age, gender, diagnoses that were billed previously or are on the problem list, laboratory findings, and prior imaging. They alert physicians when a patient is due for pay-for-performance quality measures, such as cancer screenings, and when HCC codes might be applicable.

Continue to: During the study period...

 

During the study period, the EMR ­decision-support tool for HCC coding underwent several changes designed to improve HCC coding. In addition, systematic changes to primary care visits took place, leading to an increase in the number of patients seen and screenings required.

Outcomes

We examined 2 categories of outcomes that confer financial benefit to many institutions: billing measures and pay-for-performance measures.

Billing measures included the percentage of visits (1) coded as LOS 4 or 5 and (2) with at least 1 HCC code billed (among those for which the decision-support tool identified at least 1 potential HCC code).

Pay-for-performance measures. We examined whether any of 3 pay-for-­performance quality measures were addressed during the visit, selecting 3 that are commonly addressed by primary care providers (PCPs) and that require PCPs to sign an order for screening during a primary care visit: breast cancer (mammography order), cervical cancer (Papanicolaou smear order), and colon cancer (an order for fecal occult blood testing or colonoscopy).

Intervention

Scribes were employees of Cambridge Health Alliance who had recently graduated from college and were interested in a career as a health care professional. Scribes received 3 days of training on how to function effectively in their role; 1 day of training in EMR functionality; and 2 hours of training on decision-support tools for pay-for-performance quality measures and risk coding. Scribes continued learning on the job through feedback from supervising PCPs. Scribes documented patient encounters, recording histories and findings on the physical exam and transcribing discussion of treatment plans and the PCP’s instructions to patients.

Continue to: The 14 scribes worked with 17 physician...

 

The 14 scribes worked with 17 physician and nurse practitioner PCPs beginning in July 2018. Participation by PCPs was voluntary; they received no compensation for participating in the scribe program. PCPs were not required to see additional patients to participate. PCPs who chose to work with a scribe were similar to those who declined a scribe, as regards gender, race, type of provider (MD or NP), tenure at the institution, and percentage of time in clinical work (see Table W-1).

The control group comprised providers who elected not to work with a scribe but who worked in the same clinics as the intervention providers.

Scribes might extend the time available during the visit for the provider to address pay-forperformance quality measures, such as cancer screening.

Scribes were assigned to a PCP based on availability during the PCP’s scheduled hours and worked with 1 PCP throughout the intervention (except for 1 PCP who worked with 2 scribes). All PCPs worked with their scribe(s) part time; on average, 49% of intervention PCPs’ visits were scribed.

Inclusion and exclusion criteria

Because the first year at an institution is a learning period for PCPs, we excluded those who worked at the institution for < 1 year before the start of the scribe program (n = 12). Based on the extensive clinical experience of 1 PCP (WA) with scribes, we excluded the first 200 visits or 6 weeks (whichever occurred first) with a scribe among all scribed providers, to account for an initial learning period (n = 2202, of 15,372 scribed visits [14%]). We also excluded 2 providers who left during the pre-intervention period or were in the intervention period for < 1 month.

To ensure that we captured visits to providers with clinically significant exposure to scribes, we required scribed providers to have ≥ 20% of their visits scribed during the intervention period. To minimize the potential for contamination, we excluded nonscribed visits to scribed providers during the intervention period (n = 2211), because such nonscribed visits were largely due to visits outside the scribe’s scheduled time.

Continue to: Analysis

 

 

Analysis

We compared demographic characteristics for patients and providers using the chi-square test for categorical variables and the t test for continuous variables. We compared the change in outcomes from before implementation of scribes to during implementation of scribes among scribed providers, compared to nonscribed providers, using generalized estimating equations with robust standard errors to account for repeated measures (ie, multiple visits by the same patients) and the hierarchical nature of the data (ie, patients nested within providers). We then recalculated these estimates, controlling for patient demographics (age, gender, race, and ethnicity). We repeated these analyses for patients presenting for nonpreventive visits.

Results

Visit characteristics

We examined 271,768 visits, including 41,371 visits to 17 scribed providers and 230,397 visits to 78 nonscribed providers (Table 1). Patients were most likely to be female, > 21 years of age, have English as their language of care, and be non-White. Most visits were by established patients and were nonpreventive.

We noted no clinically significant differences in characteristics between visits with scribed providers and visits with nonscribed providers, and over time. Patient complexity measures, including care management enrollment and hospital admissions, were also similar between groups, and over time.

Billing measures

HCC coding. In 28.6% of visits, the decision-support tool identified at least 1 potential HCC code. Among these, the percentage of visits with at least 1 HCC code billed increased by 10.1 percentage points (from 3.9% before implementation of scribes to 14.0%) among scribed providers, compared to increasing by 6.5 percentage points (from 2.9% before implementation to 9.3%) among nonscribed providers (TABLE 2). Scribes were therefore associated with an additional 3.6 percentage-point increase in visits with at least 1 HCC code billed (P < .0001)—a difference that remained significant after adjusting for patient demographics (P < .0001).

LOS coding. Scribed providers increased the number of visits billed as LOS 4 or 5 by 9.6 percentage points (from 47.3% before implementation to 56.8%); during the same period, nonscribed providers increased the number of visits billed as LOS 4 or 5 by 1.3 percentage points (from 46.5% before implementation to 47.8%) (TABLE 2). Scribes were therefore associated with an additional 8.3 percentage points in LOS 4 or 5 billing (P < .001) (TABLE 2). This difference remained significant after adjusting for patient demographics (P < .001).

Continue to: Pay-for-performance quality measures

 

 

Pay-for-performance quality measures

Breast cancer screening. Scribed providers increased the number of visits at which breast cancer screening was ordered by 2.7 percentage points (from 17.3% before implementation of scribes to 20.0%); during the same period, the number of visits at which breast cancer screening was ordered by nonscribed providers decreased by 1.9 percentage points (from 19.5% to 17.6%). Scribes were therefore associated with an increase of 4.6 percentage points in breast cancer screening orders, compared to nonscribed providers (P < .003) (TABLE 2). That difference remained significant after adjusting for patient demographics (P = .01).

Colon cancer screening. Similarly, scribed providers increased the number of visits at which colon cancer screening was ordered by 1.2 percentage points (from 19.2% before implementation of scribes to 20.3%); during the same period, the number of visits at which colon cancer screening was ordered by nonscribed providers decreased by 2.7 percentage points (from 18.5% to 15.9%) (P = .112). After adjusting for patient demographics, scribes were associated with an increase of 4.9 percentage points in colon cancer screening orders, compared to nonscribed providers (P = .044) (TABLE 2).

Cervical cancer screening. The rate at which cervical cancer screening was ordered did not change among scribed providers and decreased (by 2.5 percentage points) among nonscribed providers—a difference that was not statistically significant (P = .26).

Nonpreventive visits. Our findings overall did not change in analyses focused on nonpreventive visits, in which scribes were associated with an increase of 8.2 percentage points in LOS 4 or 5 billing (P < .001); an increase of 3.1 percentage points in HCC coding (P < .001); and an increase of 3.2 percentage points in breast cancer screening orders (P = .03) (TABLE 3). Although scribes were associated with an increase of 1.5 percentage points in cervical cancer screening orders and an increase of 3.1 percentage points in colon cancer screening orders, these increases did not reach statistical significance.

Discussion

We found that implementation of scribes is associated with (1) an increase in LOS coding and risk coding and (2) a higher frequency of addressing 2 of 3 pay-for-performance quality measures in primary care. In adjusted analyses in our study, and compared to nonscribed providers, scribes were associated with an additional 9.2 percentage points in LOS 4 or 5 billing; 3.6 percentage points in HCC coding; 4.0 percentage points in breast cancer screening orders; and 4.9 percentage points in colon cancer screening orders. Cervical cancer screening orders followed a similar pattern, with an increase of 2.3 percentage points in the adjusted screening order rate among scribed providers, compared to nonscribed providers, during implementation of scribes—although the increase was not significant. These findings did not change in analyses focused on nonpreventive visits.

Continue to: Our findings are consistent

 

Our findings are consistent with those of earlier studies. Prior examinations in ambulatory specialties found that scribes increased HCC coding,⁴ LOS billing,²⁴ and pay-for-performance metrics.¹⁸ The only study to examine these areas in primary care found that scribes were associated with increased pay-for-performance measure documentation,²⁰ a change that is necessary but insufficient to realize increased pay-for-­performance revenue. Therefore, our study confirms, for the first time, that PCPs can better address pay-for-performance measures, LOS billing, and HCC coding when working with a scribe in primary care.

Providers who used a scribe during the intervention period of our study increased the number of visits billed as LOS 4 or 5 by 9.6 percentage points.

Demands on primary care visits are increasing.²⁸ Physicians are required to provide more documentation; there is greater emphasis on PCPs meeting pay-for-­performance measures; and there are more data in the EMR to review. In this context, addressing pay-for-performance measures and gaps in risk coding is likely to be increasingly challenging. Our study suggests that scribes might provide a mechanism to increase risk coding, LOS billing, and pay-for-performance measures, despite increased demands on primary care visits.

Increase in LOS billing. In the settings in which we work, a fee-for-service LOS 4 primary care visit generates, on average, $20 to $75 more in revenue than an LOS 3 visit. Using an average of $50 additional revenue for LOS 4 billing, we estimate that a full-time scribe is associated with roughly $7,000 in additional revenue annually. We arrived at this estimate using an average of 1500 visits at LOS ≤ 3 for every PCP full-time equivalent. A 9.2 percentage–point increase in LOS 4 billing would lead to roughly 140 additional LOS 4 visits, with each visit generating an additional $50 in revenue.

This analysis does not account for increased revenue associated with increased pay for HCC coding identified in our study.

Our study also suggests that scribes might provide a mechanism to increase risk coding and payfor-performance, despite increased demands on primary care visits.

Furthermore, in our conservative assumption, the entire increase in LOS billing was from level 3 to level 4; in fact, a small percentage of that increase would be from level 2 and another small percentage would be to level 5—both of which would generate additional revenue. Our assumption therefore underestimates the full financial value associated with scribes in the absence of increased patient volume. Nonetheless, the assumption suggests that increases in LOS billing offset a substantial percentage of a scribe’s salary.

Continue to: Limitations of this study

 

Limitations of this study. Our study should be interpreted in the context of several limitations:

• The study was conducted at 1 institution. Our findings might not be generalizable beyond this setting.
• The study measures the impact of scribes when providers work with scribes part time. Because providers who utilize a scribe for all, or nearly all, their visits are likely to use a scribe more efficiently, our study might underestimate the full impact of a scribe.
• In some settings, team members such as medical assistants are trained to assist with documentation and other responsibilities (such as closing care gaps) in addition to other patient care responsibilities.^29-32 The extent to which our findings transfer to other models is unclear; studies comparing the impact of other models (which might provide even stronger outcomes) to the impact of medical scribes would be an interesting area for further research.
• In addition to the variability of models, there is likely variability in the quality and interactions of medical scribes, which might impact outcomes. We did not examine the qualities of scribes that led to outcomes in this study.
• We examined the impact of scribes on quality measure–ordering behaviors of providers, not on whether quality measures actually improved. Because scribes are associated with more face-to-face time with patients,²⁷ they might allow for increased attention being paid by physicians to barriers to pay-for-performance measures (eg, patient education). This could increase the likelihood that patients complete a multitude of screenings, and thus improve adherence and follow-up. However, the impact of scribes on quality measures is a topic for future study.

Value beyond volume. Any limitations notwithstanding, our study suggests that scribes are associated with financial benefit in addition to the benefit of increased volume. Primary care practices should therefore consider the financial benefit of scribes independent of their ability to add patient volume. By recognizing this additive value, primary care practices might more fully capture the benefit of scribes, which might then allow practices to employ scribes with less demand to increase volume. This added support without increased volume would, in turn, likely reduce provider burnout (and the costly associated turnover) and increase patient satisfaction, leading to a synergistic financial benefit.

CORRESPONDENCE
Wayne Altman, MD, FAAFP, Tufts University School of Medicine, 200 Harrison Avenue, Boston, MA 02111; wayne. altman@tufts.edu

ABSTRACT

Purpose Medical scribes are known to increase revenue by increasing visits to a medical practice. We examined whether medical scribes are associated with markers of financial benefit independent of increased visits.

Methods We conducted a pre- and post-­observational study with a control group, examining changes in the percentage of visits (1) coded as level of service 4 or 5, (2) with at least 1 hierarchical condition category code billed, and (3) at which orders for 3 pay-for-performance quality measures (screening for breast, cervical, and colon cancer) were placed, if due. We looked at changes in outcomes among scribed providers and compared them to nonscribed providers. We used generalized estimating equations with robust standard errors to account for repeated measures and the hierarchical nature of the data, controlling for patient demographics.

Results We examined 41,371 visits to 17 scribed providers and 230,297 visits to 78 nonscribed providers. In adjusted analyses, and compared to nonscribed providers, scribes were associated with an increase of:

• Frutiger LT Std9.2 percentage points in level-of-­service 4 or 5 billing (Frutiger LT StdP < .001)
• 3.6 percentage points in hierarchical condition category coding (Frutiger LT StdP < .001)
• 4.0 percentage points in breast cancer screening orders (Frutiger LT StdP = .01)
• 4.9 percentage points in colon cancer screening orders (Frutiger LT StdPFrutiger LT Std = .04).

Conclusions This study suggests that scribes are associated with financial benefit in addition to increased visit volume. Primary care practices should consider the financial benefit of scribes independent of their ability to add patient volume.

Increasingly, medical scribes are used in ambulatory care settings across the United States.¹ Scribes are trained personnel who accompany providers during visits to provide documentation support and assist with other administrative tasks. They are associated with reduced documentation time for providers^2-6 and improved provider satisfaction,^7-11 without detriment to^4-16 (or with possible improvement in^17-20) patient satisfaction in ambulatory care settings. At the same time, concerns remain that using scribes might inhibit patient communication, harm clinical reasoning, reduce the effectiveness of clinical decision-support tools, and simply serve as a work-around to fixing inefficiencies in the electronic medical record (EMR).^21-23

A driving force for the increased use of medical scribes is the expectation that they reduce the cost of providing care. Cost-­efficiency is typically described as resulting from a reduction in physician time per patient seen, which allows increased patient volume and, in turn, drives increased physician productivity.^{2,8,10,18,24-27} Whether scribes result in cost savings remains unclear; some papers suggest that scribes are cost efficient in ambulatory care,^2,10,18 while others have been unable to identify cost savings, particularly in primary care.⁴

One reason why scribes might not be associated with cost savings is that their financial benefit might be undercounted. Studies that focus on increased volume miss the opportunity to capture financial benefits conferred through mechanisms that are independent of seeing more patients:

• Scribes might help providers address and document more, and more complex, medical problems, allowing higher level-of-service (LOS) billing. For example, a provider chooses a lower LOS because they have insufficiently documented a visit to support a higher LOS; by assisting with documentation, the scribe might allow the provider to choose a higher LOS.
• Scribes might prompt a provider to use decision-support tools for risk coding (using appropriate medical codes to capture the patient’s level of medical complexity), thereby increasing reimbursement.
• Scribes might extend the time available during the visit for the provider to address pay-for-performance quality measures, such as cancer screening.

Continue to: Making visits count, not counting visits

 

Making visits count, not counting visits. In this study, we examined whether medical scribes in primary care are associated with improved markers of revenue that are independent of seeing more patients. Specifically, we examined whether scribes are associated with increased LOS coding, risk coding, and orders for pay-for-performance measures for all primary care visits and for nonpreventive primary care visits.

Methods

Design

This observational study compared the change in outcomes before implementation of scribes and during implementation of scribes, between scribed providers and nonscribed providers. We compared visits during the year prior to the implementation of scribes (July 2017–June 2018) with the year during their implementation (July 2018–2019).

The Cambridge Health Alliance Institutional Review Board considered this study exempt from review.

Setting

This study was conducted at a safety-net community academic health system that uses an EMR developed by Epic Systems [Verona, WI]. This EMR includes decision-support tools that prompt providers when pay-for-performance quality measures are due and when hierarchical condition category (HCC) codes—ie, specific diagnoses used by Medicare and other payers to reimburse providers for the complexity of their patients—might apply to the visit.

Primary care practices should consider the financial benefit of scribes, independent of their ability to add patient volume.

These EMR decision-support tools use algorithms that draw on age, gender, diagnoses that were billed previously or are on the problem list, laboratory findings, and prior imaging. They alert physicians when a patient is due for pay-for-performance quality measures, such as cancer screenings, and when HCC codes might be applicable.

Continue to: During the study period...

 

During the study period, the EMR ­decision-support tool for HCC coding underwent several changes designed to improve HCC coding. In addition, systematic changes to primary care visits took place, leading to an increase in the number of patients seen and screenings required.

Outcomes

We examined 2 categories of outcomes that confer financial benefit to many institutions: billing measures and pay-for-performance measures.

Billing measures included the percentage of visits (1) coded as LOS 4 or 5 and (2) with at least 1 HCC code billed (among those for which the decision-support tool identified at least 1 potential HCC code).

Pay-for-performance measures. We examined whether any of 3 pay-for-­performance quality measures were addressed during the visit, selecting 3 that are commonly addressed by primary care providers (PCPs) and that require PCPs to sign an order for screening during a primary care visit: breast cancer (mammography order), cervical cancer (Papanicolaou smear order), and colon cancer (an order for fecal occult blood testing or colonoscopy).

Intervention

Scribes were employees of Cambridge Health Alliance who had recently graduated from college and were interested in a career as a health care professional. Scribes received 3 days of training on how to function effectively in their role; 1 day of training in EMR functionality; and 2 hours of training on decision-support tools for pay-for-performance quality measures and risk coding. Scribes continued learning on the job through feedback from supervising PCPs. Scribes documented patient encounters, recording histories and findings on the physical exam and transcribing discussion of treatment plans and the PCP’s instructions to patients.

Continue to: The 14 scribes worked with 17 physician...

 

The 14 scribes worked with 17 physician and nurse practitioner PCPs beginning in July 2018. Participation by PCPs was voluntary; they received no compensation for participating in the scribe program. PCPs were not required to see additional patients to participate. PCPs who chose to work with a scribe were similar to those who declined a scribe, as regards gender, race, type of provider (MD or NP), tenure at the institution, and percentage of time in clinical work (see Table W-1).

The control group comprised providers who elected not to work with a scribe but who worked in the same clinics as the intervention providers.

Scribes might extend the time available during the visit for the provider to address pay-forperformance quality measures, such as cancer screening.

Scribes were assigned to a PCP based on availability during the PCP’s scheduled hours and worked with 1 PCP throughout the intervention (except for 1 PCP who worked with 2 scribes). All PCPs worked with their scribe(s) part time; on average, 49% of intervention PCPs’ visits were scribed.

Inclusion and exclusion criteria

Because the first year at an institution is a learning period for PCPs, we excluded those who worked at the institution for < 1 year before the start of the scribe program (n = 12). Based on the extensive clinical experience of 1 PCP (WA) with scribes, we excluded the first 200 visits or 6 weeks (whichever occurred first) with a scribe among all scribed providers, to account for an initial learning period (n = 2202, of 15,372 scribed visits [14%]). We also excluded 2 providers who left during the pre-intervention period or were in the intervention period for < 1 month.

To ensure that we captured visits to providers with clinically significant exposure to scribes, we required scribed providers to have ≥ 20% of their visits scribed during the intervention period. To minimize the potential for contamination, we excluded nonscribed visits to scribed providers during the intervention period (n = 2211), because such nonscribed visits were largely due to visits outside the scribe’s scheduled time.

Continue to: Analysis

 

 

Analysis

We compared demographic characteristics for patients and providers using the chi-square test for categorical variables and the t test for continuous variables. We compared the change in outcomes from before implementation of scribes to during implementation of scribes among scribed providers, compared to nonscribed providers, using generalized estimating equations with robust standard errors to account for repeated measures (ie, multiple visits by the same patients) and the hierarchical nature of the data (ie, patients nested within providers). We then recalculated these estimates, controlling for patient demographics (age, gender, race, and ethnicity). We repeated these analyses for patients presenting for nonpreventive visits.

Results

Visit characteristics

We examined 271,768 visits, including 41,371 visits to 17 scribed providers and 230,397 visits to 78 nonscribed providers (Table 1). Patients were most likely to be female, > 21 years of age, have English as their language of care, and be non-White. Most visits were by established patients and were nonpreventive.

We noted no clinically significant differences in characteristics between visits with scribed providers and visits with nonscribed providers, and over time. Patient complexity measures, including care management enrollment and hospital admissions, were also similar between groups, and over time.

Billing measures

HCC coding. In 28.6% of visits, the decision-support tool identified at least 1 potential HCC code. Among these, the percentage of visits with at least 1 HCC code billed increased by 10.1 percentage points (from 3.9% before implementation of scribes to 14.0%) among scribed providers, compared to increasing by 6.5 percentage points (from 2.9% before implementation to 9.3%) among nonscribed providers (TABLE 2). Scribes were therefore associated with an additional 3.6 percentage-point increase in visits with at least 1 HCC code billed (P < .0001)—a difference that remained significant after adjusting for patient demographics (P < .0001).

LOS coding. Scribed providers increased the number of visits billed as LOS 4 or 5 by 9.6 percentage points (from 47.3% before implementation to 56.8%); during the same period, nonscribed providers increased the number of visits billed as LOS 4 or 5 by 1.3 percentage points (from 46.5% before implementation to 47.8%) (TABLE 2). Scribes were therefore associated with an additional 8.3 percentage points in LOS 4 or 5 billing (P < .001) (TABLE 2). This difference remained significant after adjusting for patient demographics (P < .001).

Continue to: Pay-for-performance quality measures

 

 

Pay-for-performance quality measures

Breast cancer screening. Scribed providers increased the number of visits at which breast cancer screening was ordered by 2.7 percentage points (from 17.3% before implementation of scribes to 20.0%); during the same period, the number of visits at which breast cancer screening was ordered by nonscribed providers decreased by 1.9 percentage points (from 19.5% to 17.6%). Scribes were therefore associated with an increase of 4.6 percentage points in breast cancer screening orders, compared to nonscribed providers (P < .003) (TABLE 2). That difference remained significant after adjusting for patient demographics (P = .01).

Colon cancer screening. Similarly, scribed providers increased the number of visits at which colon cancer screening was ordered by 1.2 percentage points (from 19.2% before implementation of scribes to 20.3%); during the same period, the number of visits at which colon cancer screening was ordered by nonscribed providers decreased by 2.7 percentage points (from 18.5% to 15.9%) (P = .112). After adjusting for patient demographics, scribes were associated with an increase of 4.9 percentage points in colon cancer screening orders, compared to nonscribed providers (P = .044) (TABLE 2).

Cervical cancer screening. The rate at which cervical cancer screening was ordered did not change among scribed providers and decreased (by 2.5 percentage points) among nonscribed providers—a difference that was not statistically significant (P = .26).

Nonpreventive visits. Our findings overall did not change in analyses focused on nonpreventive visits, in which scribes were associated with an increase of 8.2 percentage points in LOS 4 or 5 billing (P < .001); an increase of 3.1 percentage points in HCC coding (P < .001); and an increase of 3.2 percentage points in breast cancer screening orders (P = .03) (TABLE 3). Although scribes were associated with an increase of 1.5 percentage points in cervical cancer screening orders and an increase of 3.1 percentage points in colon cancer screening orders, these increases did not reach statistical significance.

Discussion

We found that implementation of scribes is associated with (1) an increase in LOS coding and risk coding and (2) a higher frequency of addressing 2 of 3 pay-for-performance quality measures in primary care. In adjusted analyses in our study, and compared to nonscribed providers, scribes were associated with an additional 9.2 percentage points in LOS 4 or 5 billing; 3.6 percentage points in HCC coding; 4.0 percentage points in breast cancer screening orders; and 4.9 percentage points in colon cancer screening orders. Cervical cancer screening orders followed a similar pattern, with an increase of 2.3 percentage points in the adjusted screening order rate among scribed providers, compared to nonscribed providers, during implementation of scribes—although the increase was not significant. These findings did not change in analyses focused on nonpreventive visits.

Continue to: Our findings are consistent

 

Our findings are consistent with those of earlier studies. Prior examinations in ambulatory specialties found that scribes increased HCC coding,⁴ LOS billing,²⁴ and pay-for-performance metrics.¹⁸ The only study to examine these areas in primary care found that scribes were associated with increased pay-for-performance measure documentation,²⁰ a change that is necessary but insufficient to realize increased pay-for-­performance revenue. Therefore, our study confirms, for the first time, that PCPs can better address pay-for-performance measures, LOS billing, and HCC coding when working with a scribe in primary care.

Providers who used a scribe during the intervention period of our study increased the number of visits billed as LOS 4 or 5 by 9.6 percentage points.

Demands on primary care visits are increasing.²⁸ Physicians are required to provide more documentation; there is greater emphasis on PCPs meeting pay-for-­performance measures; and there are more data in the EMR to review. In this context, addressing pay-for-performance measures and gaps in risk coding is likely to be increasingly challenging. Our study suggests that scribes might provide a mechanism to increase risk coding, LOS billing, and pay-for-performance measures, despite increased demands on primary care visits.

Increase in LOS billing. In the settings in which we work, a fee-for-service LOS 4 primary care visit generates, on average, $20 to $75 more in revenue than an LOS 3 visit. Using an average of $50 additional revenue for LOS 4 billing, we estimate that a full-time scribe is associated with roughly $7,000 in additional revenue annually. We arrived at this estimate using an average of 1500 visits at LOS ≤ 3 for every PCP full-time equivalent. A 9.2 percentage–point increase in LOS 4 billing would lead to roughly 140 additional LOS 4 visits, with each visit generating an additional $50 in revenue.

This analysis does not account for increased revenue associated with increased pay for HCC coding identified in our study.

Our study also suggests that scribes might provide a mechanism to increase risk coding and payfor-performance, despite increased demands on primary care visits.

Furthermore, in our conservative assumption, the entire increase in LOS billing was from level 3 to level 4; in fact, a small percentage of that increase would be from level 2 and another small percentage would be to level 5—both of which would generate additional revenue. Our assumption therefore underestimates the full financial value associated with scribes in the absence of increased patient volume. Nonetheless, the assumption suggests that increases in LOS billing offset a substantial percentage of a scribe’s salary.

Continue to: Limitations of this study

 

Limitations of this study. Our study should be interpreted in the context of several limitations:

• The study was conducted at 1 institution. Our findings might not be generalizable beyond this setting.
• The study measures the impact of scribes when providers work with scribes part time. Because providers who utilize a scribe for all, or nearly all, their visits are likely to use a scribe more efficiently, our study might underestimate the full impact of a scribe.
• In some settings, team members such as medical assistants are trained to assist with documentation and other responsibilities (such as closing care gaps) in addition to other patient care responsibilities.^29-32 The extent to which our findings transfer to other models is unclear; studies comparing the impact of other models (which might provide even stronger outcomes) to the impact of medical scribes would be an interesting area for further research.
• In addition to the variability of models, there is likely variability in the quality and interactions of medical scribes, which might impact outcomes. We did not examine the qualities of scribes that led to outcomes in this study.
• We examined the impact of scribes on quality measure–ordering behaviors of providers, not on whether quality measures actually improved. Because scribes are associated with more face-to-face time with patients,²⁷ they might allow for increased attention being paid by physicians to barriers to pay-for-performance measures (eg, patient education). This could increase the likelihood that patients complete a multitude of screenings, and thus improve adherence and follow-up. However, the impact of scribes on quality measures is a topic for future study.

Value beyond volume. Any limitations notwithstanding, our study suggests that scribes are associated with financial benefit in addition to the benefit of increased volume. Primary care practices should therefore consider the financial benefit of scribes independent of their ability to add patient volume. By recognizing this additive value, primary care practices might more fully capture the benefit of scribes, which might then allow practices to employ scribes with less demand to increase volume. This added support without increased volume would, in turn, likely reduce provider burnout (and the costly associated turnover) and increase patient satisfaction, leading to a synergistic financial benefit.

CORRESPONDENCE
Wayne Altman, MD, FAAFP, Tufts University School of Medicine, 200 Harrison Avenue, Boston, MA 02111; wayne. altman@tufts.edu