Practice Management

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; chase.d.hendrickson@vanderbilt.edu

Disclosures: None reported.

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; chase.d.hendrickson@vanderbilt.edu

Disclosures: None reported.

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; chase.d.hendrickson@vanderbilt.edu

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

AURORA, COLO. – Artificial Intelligence has arrived at medical offices, whether or not clinicians feel ready for it.

AI might result in more accurate, efficient, and cost-effective care. But it’s possible it could cause harm. That’s according to Benjamin Collins, MD, at Vanderbilt University Medical Center, Nashville, Tenn., who spoke on the subject at the annual meeting of the Society of General Internal Medicine.

Understanding the nuances of AI is even more important because of the quick development of the algorithms.

“When I submitted this workshop, there was no ChatGPT,” said Dr. Collins, referring to Chat Generative Pre-trained Transformer, a recently released natural language processing model. “A lot has already changed.”
 

Biased data

Biased data are perhaps the biggest pitfall of AI algorithms, Dr. Collins said. If garbage data go in, garbage predictions come out.

If the dataset that trains the algorithm underrepresents a particular gender or ethnic group, for example, the algorithm may not respond accurately to prompts. When an AI tool compounds existing inequalities related to socioeconomic status, ethnicity, or sexual orientation, the algorithm is biased, according to Harvard researchers.

“People often assume that artificial intelligence is free of bias due to the use of scientific processes and its development,” he said. “But whatever flaws exist in data collection and old data can lead to poor representation or underrepresentation in the data used to train the AI tool.”

Racial minorities are underrepresented in studies; therefore, data input into an AI tool might skew results for these patients.

The Framingham Heart Study, for example, which began in 1948, examined heart disease in mainly White participants. The findings from the study resulted in the creation of a sex-specific algorithm that was used to estimate the 10-year cardiovascular risk of a patient. While the cardiovascular risk score was accurate for White persons, it was less accurate for Black patients.

A study published in Science in 2019 revealed bias in an algorithm that used health care costs as a proxy for health needs. Because less money was spent on Black patients who had the same level of need as their White counterparts, the output inaccurately showed that Black patients were healthier and thus did not require extra care.

Developers can also be a source of bias, inasmuch as AI often reflects preexisting human biases, Dr. Collins said.

“Algorithmic bias presents a clear risk of harm that clinicians must play against the benefits of using AI,” Dr. Collins said. “That risk of harm is often disproportionately distributed to marginalized populations.”

As clinicians use AI algorithms to diagnose and detect disease, predict outcomes, and guide treatment, trouble comes when those algorithms perform well for some patients and poorly for others. This gap can exacerbate existing disparities in health care outcomes.

Dr. Collins advised clinicians to push to find out what data were used to train AI algorithms to determine how bias could have influenced the model and whether the developers risk-adjusted for bias. If the training data are not available, clinicians should ask their employers and AI developers to know more about the system.

Clinicians may face the so-called black box phenomenon, which occurs when developers cannot or will not explain what data went into an AI model, Dr. Collins said.

According to Stanford (Calif.) University, AI must be trained on large datasets of images that have been annotated by human experts. Those datasets can cost millions of dollars to create, meaning corporations often fund them and do not always share the data publicly.

Some groups, such as Stanford’s Center for Artificial Intelligence in Medicine and Imaging, are working to acquire annotated datasets so researchers who train AI models can know where the data came from.

Paul Haidet, MD, MPH, an internist at Penn State College of Medicine, Hershey, sees the technology as a tool that requires careful handling.

“It takes a while to learn how to use a stethoscope, and AI is like that,” Dr. Haidet said. “The thing about AI, though, is that it can be just dropped into a system and no one knows how it works.”

Dr. Haidet said he likes knowing how the sausage is made, something AI developers are often reticent to make known.

“If you’re just putting blind faith in a tool, that’s scary,” Dr. Haidet said.
 

Transparency and ‘explainability’

The ability to explain what goes into tools is essential to maintaining trust in the health care system, Dr. Collins said.

“Part of knowing how much trust to place in the system is the transparency of those systems and the ability to audit how well the algorithm is performing,” Dr. Collins said. “The system should also regularly report to users the level of certainty with which it is providing an output rather than providing a simple binary output.”

Dr. Collins recommends that providers develop an understanding of the limits of AI regulations as well, which might including learning how the system was approved and how it is monitored.

“The FDA has oversight over some applications of AI and health care for software as a medical device, but there’s currently no dedicated process to evaluate the systems for the presence of bias,” Dr. Collins said. “The gaps in regulation leave the door open for the use of AI in clinical care that contain significant biases.”

Dr. Haidet likened AI tools to the Global Positioning System: A good GPS system will let users see alternate routes, opt out of toll roads or highways, and will highlight why routes have changed. But users need to understand how to read the map so they can tell when something seems amiss.

Dr. Collins and Dr. Haidet report no relevant financial relationships

A version of this article first appeared on Medscape.com.

AURORA, COLO. – Artificial Intelligence has arrived at medical offices, whether or not clinicians feel ready for it.

AI might result in more accurate, efficient, and cost-effective care. But it’s possible it could cause harm. That’s according to Benjamin Collins, MD, at Vanderbilt University Medical Center, Nashville, Tenn., who spoke on the subject at the annual meeting of the Society of General Internal Medicine.

Understanding the nuances of AI is even more important because of the quick development of the algorithms.

“When I submitted this workshop, there was no ChatGPT,” said Dr. Collins, referring to Chat Generative Pre-trained Transformer, a recently released natural language processing model. “A lot has already changed.”
 

Biased data

Biased data are perhaps the biggest pitfall of AI algorithms, Dr. Collins said. If garbage data go in, garbage predictions come out.

If the dataset that trains the algorithm underrepresents a particular gender or ethnic group, for example, the algorithm may not respond accurately to prompts. When an AI tool compounds existing inequalities related to socioeconomic status, ethnicity, or sexual orientation, the algorithm is biased, according to Harvard researchers.

“People often assume that artificial intelligence is free of bias due to the use of scientific processes and its development,” he said. “But whatever flaws exist in data collection and old data can lead to poor representation or underrepresentation in the data used to train the AI tool.”

Racial minorities are underrepresented in studies; therefore, data input into an AI tool might skew results for these patients.

The Framingham Heart Study, for example, which began in 1948, examined heart disease in mainly White participants. The findings from the study resulted in the creation of a sex-specific algorithm that was used to estimate the 10-year cardiovascular risk of a patient. While the cardiovascular risk score was accurate for White persons, it was less accurate for Black patients.

A study published in Science in 2019 revealed bias in an algorithm that used health care costs as a proxy for health needs. Because less money was spent on Black patients who had the same level of need as their White counterparts, the output inaccurately showed that Black patients were healthier and thus did not require extra care.

Developers can also be a source of bias, inasmuch as AI often reflects preexisting human biases, Dr. Collins said.

“Algorithmic bias presents a clear risk of harm that clinicians must play against the benefits of using AI,” Dr. Collins said. “That risk of harm is often disproportionately distributed to marginalized populations.”

As clinicians use AI algorithms to diagnose and detect disease, predict outcomes, and guide treatment, trouble comes when those algorithms perform well for some patients and poorly for others. This gap can exacerbate existing disparities in health care outcomes.

Dr. Collins advised clinicians to push to find out what data were used to train AI algorithms to determine how bias could have influenced the model and whether the developers risk-adjusted for bias. If the training data are not available, clinicians should ask their employers and AI developers to know more about the system.

Clinicians may face the so-called black box phenomenon, which occurs when developers cannot or will not explain what data went into an AI model, Dr. Collins said.

According to Stanford (Calif.) University, AI must be trained on large datasets of images that have been annotated by human experts. Those datasets can cost millions of dollars to create, meaning corporations often fund them and do not always share the data publicly.

Some groups, such as Stanford’s Center for Artificial Intelligence in Medicine and Imaging, are working to acquire annotated datasets so researchers who train AI models can know where the data came from.

Paul Haidet, MD, MPH, an internist at Penn State College of Medicine, Hershey, sees the technology as a tool that requires careful handling.

“It takes a while to learn how to use a stethoscope, and AI is like that,” Dr. Haidet said. “The thing about AI, though, is that it can be just dropped into a system and no one knows how it works.”

Dr. Haidet said he likes knowing how the sausage is made, something AI developers are often reticent to make known.

“If you’re just putting blind faith in a tool, that’s scary,” Dr. Haidet said.
 

Transparency and ‘explainability’

The ability to explain what goes into tools is essential to maintaining trust in the health care system, Dr. Collins said.

“Part of knowing how much trust to place in the system is the transparency of those systems and the ability to audit how well the algorithm is performing,” Dr. Collins said. “The system should also regularly report to users the level of certainty with which it is providing an output rather than providing a simple binary output.”

Dr. Collins recommends that providers develop an understanding of the limits of AI regulations as well, which might including learning how the system was approved and how it is monitored.

“The FDA has oversight over some applications of AI and health care for software as a medical device, but there’s currently no dedicated process to evaluate the systems for the presence of bias,” Dr. Collins said. “The gaps in regulation leave the door open for the use of AI in clinical care that contain significant biases.”

Dr. Haidet likened AI tools to the Global Positioning System: A good GPS system will let users see alternate routes, opt out of toll roads or highways, and will highlight why routes have changed. But users need to understand how to read the map so they can tell when something seems amiss.

Dr. Collins and Dr. Haidet report no relevant financial relationships

A version of this article first appeared on Medscape.com.

AURORA, COLO. – Artificial Intelligence has arrived at medical offices, whether or not clinicians feel ready for it.

AI might result in more accurate, efficient, and cost-effective care. But it’s possible it could cause harm. That’s according to Benjamin Collins, MD, at Vanderbilt University Medical Center, Nashville, Tenn., who spoke on the subject at the annual meeting of the Society of General Internal Medicine.

Understanding the nuances of AI is even more important because of the quick development of the algorithms.

“When I submitted this workshop, there was no ChatGPT,” said Dr. Collins, referring to Chat Generative Pre-trained Transformer, a recently released natural language processing model. “A lot has already changed.”
 

Biased data

Biased data are perhaps the biggest pitfall of AI algorithms, Dr. Collins said. If garbage data go in, garbage predictions come out.

If the dataset that trains the algorithm underrepresents a particular gender or ethnic group, for example, the algorithm may not respond accurately to prompts. When an AI tool compounds existing inequalities related to socioeconomic status, ethnicity, or sexual orientation, the algorithm is biased, according to Harvard researchers.

“People often assume that artificial intelligence is free of bias due to the use of scientific processes and its development,” he said. “But whatever flaws exist in data collection and old data can lead to poor representation or underrepresentation in the data used to train the AI tool.”

Racial minorities are underrepresented in studies; therefore, data input into an AI tool might skew results for these patients.

The Framingham Heart Study, for example, which began in 1948, examined heart disease in mainly White participants. The findings from the study resulted in the creation of a sex-specific algorithm that was used to estimate the 10-year cardiovascular risk of a patient. While the cardiovascular risk score was accurate for White persons, it was less accurate for Black patients.

A study published in Science in 2019 revealed bias in an algorithm that used health care costs as a proxy for health needs. Because less money was spent on Black patients who had the same level of need as their White counterparts, the output inaccurately showed that Black patients were healthier and thus did not require extra care.

Developers can also be a source of bias, inasmuch as AI often reflects preexisting human biases, Dr. Collins said.

“Algorithmic bias presents a clear risk of harm that clinicians must play against the benefits of using AI,” Dr. Collins said. “That risk of harm is often disproportionately distributed to marginalized populations.”

As clinicians use AI algorithms to diagnose and detect disease, predict outcomes, and guide treatment, trouble comes when those algorithms perform well for some patients and poorly for others. This gap can exacerbate existing disparities in health care outcomes.

Dr. Collins advised clinicians to push to find out what data were used to train AI algorithms to determine how bias could have influenced the model and whether the developers risk-adjusted for bias. If the training data are not available, clinicians should ask their employers and AI developers to know more about the system.

Clinicians may face the so-called black box phenomenon, which occurs when developers cannot or will not explain what data went into an AI model, Dr. Collins said.

According to Stanford (Calif.) University, AI must be trained on large datasets of images that have been annotated by human experts. Those datasets can cost millions of dollars to create, meaning corporations often fund them and do not always share the data publicly.

Some groups, such as Stanford’s Center for Artificial Intelligence in Medicine and Imaging, are working to acquire annotated datasets so researchers who train AI models can know where the data came from.

Paul Haidet, MD, MPH, an internist at Penn State College of Medicine, Hershey, sees the technology as a tool that requires careful handling.

“It takes a while to learn how to use a stethoscope, and AI is like that,” Dr. Haidet said. “The thing about AI, though, is that it can be just dropped into a system and no one knows how it works.”

Dr. Haidet said he likes knowing how the sausage is made, something AI developers are often reticent to make known.

“If you’re just putting blind faith in a tool, that’s scary,” Dr. Haidet said.
 

Transparency and ‘explainability’

The ability to explain what goes into tools is essential to maintaining trust in the health care system, Dr. Collins said.

“Part of knowing how much trust to place in the system is the transparency of those systems and the ability to audit how well the algorithm is performing,” Dr. Collins said. “The system should also regularly report to users the level of certainty with which it is providing an output rather than providing a simple binary output.”

Dr. Collins recommends that providers develop an understanding of the limits of AI regulations as well, which might including learning how the system was approved and how it is monitored.

“The FDA has oversight over some applications of AI and health care for software as a medical device, but there’s currently no dedicated process to evaluate the systems for the presence of bias,” Dr. Collins said. “The gaps in regulation leave the door open for the use of AI in clinical care that contain significant biases.”

Dr. Haidet likened AI tools to the Global Positioning System: A good GPS system will let users see alternate routes, opt out of toll roads or highways, and will highlight why routes have changed. But users need to understand how to read the map so they can tell when something seems amiss.

Dr. Collins and Dr. Haidet report no relevant financial relationships

A version of this article first appeared on Medscape.com.

Many of the persistent problems that breast cancer survivors experience after treatment are not part of the conversation with oncologists during treatment, said Patricia A. Ganz, MD, during a presentation at the European Society for Medical Oncology Breast Cancer annual congress.

Several studies suggest that many breast cancer patients are not well prepared to move forward after a breast cancer diagnosis and subsequent treatments, continued Dr. Ganz, who works at the UCLA Jonsson Comprehensive Cancer Center, Los Angeles.

Meeting the survivorship needs of breast cancer patients requires addressing both their physical and psychosocial needs, Dr. Ganz said. She explained how to achieve that, but first pointed to research elaborating on what's missing from some breast cancer survivors' care and barriers to these patients having their variety of health-related needs met.

In a 2021 study published in the Journal of Cancer Survivorship, Dr. Ganz and colleagues conducted a survey of approximately 200 medical oncologists in the United States. They determined that less than 50% provide survivorship care plans to patients at the end of treatment or communicate with patients’ other physicians about follow-up care.

In a secondary analysis of data from the same survey published in 2022 in Breast Cancer Research and Treatment, Dr. Ganz and colleagues examined medical oncologists’ perceived barriers to addressing both physical and psychosocial long-term effects in breast cancer survivors. For both, lack of time was the greatest perceived barrier, cited by nearly two-thirds of oncologists. Other barriers to addressing physical effects included lack of evidence-based, effective interventions, lack of clinical algorithms to guide care, and ambiguity regarding professional responsibility at the end of treatment. Other top barriers to addressing psychosocial issues included lack of mental health providers, lack of psychosocial resources, and lack of clinician knowledge and skills.

Data from additional studies suggest that, overall, cancer patients with greater physical burdens, such as more complex and lengthy treatment regimens, also have greater psychosocial needs, Dr. Ganz noted. Plus, approximately 15%-20% of cancer survivors have ongoing anxiety and depressive symptoms.

Shift to primary care

As more breast cancer and other cancer patients survive for longer periods, more care will likely occur in general medical settings, Dr. Ganz said. Issues to be addressed will include the potential increased risk of comorbid conditions for these survivors, and whether survivorship interventions earlier in the disease trajectory will impact survivorship. For cancer patients who achieve remission after treatment, the first 5 years after a diagnosis involves treatment and short-term surveillance for late effects. Beyond 5 years, care for cancer survivors mainly involves primary care and management of any comorbid conditions, as well as surveillance for late effects and recurrences, and awareness of new research.

A patient consultation early in the process after diagnosis is the start of a continuum of care, Dr. Ganz said. A patient consultation should address symptoms related to initial treatments, such as neuropathy, pain, fatigue, and insomnia, as well as the psychological symptoms of anxiety and depression. An early consultation also should evaluate adherence to endocrine therapy and management of symptoms, if needed, with the larger goal of preparing patients for recovery and the transition to survivorship, and what to expect for long-term follow-up.
 

Delivering the three P’s

The “Three P’s” of survivor care for breast cancer patients are palliation, prevention, and promotion of health, according to Dr. Ganz .

The first “P,” for palliative, is a key part of survivorship care, said Dr. Ganz. Palliative care is defined as care that focuses on reducing symptom severity and improving quality of life. The biological effects of cancer treatment can be associated with physical effects, such as functional limitations and frailty, and behavioral/cognitive effects such as depression, fatigue, and cognitive deficits, she said. To manage these effects and provide palliative care, consultation is needed with specialists in relevant areas including mental health, pain management, physical medicine/rehabilitation, endocrinology, cardiology, and neurology.

The second “P,” which is for prevention in survivorship care, refers to ongoing follow-up screening to identify any potentially serious late-onset complications such as osteoporosis or cardiac disease so they can be addressed, said Dr. Ganz. Other considerations include chemoprevention if available and genetic counseling for patients with hereditary cancers. Prevention also includes counseling patients about lifestyle modifications to help prevent additional cancer.

The goal of the third “P,” which is for health promotion, is to promote risk reduction for the health problems associated with accelerated aging that may arise in cancer survivors, said Dr. Ganz.

Health promotion strategies include maintaining a healthy weight, increasing physical activity, and avoiding harmful exposures, she said. Healthy lifestyle interventions can also reduce the risk of other chronic diseases such as diabetes and heart disease.

To that end, Dr. Ganz outlined several behavioral interventions that may mitigate the effects of cancer treatment on the accelerated aging process, including stress reduction in the form of meditation or yoga, cognitive behavioral therapy, improving sleep, increasing physical activity, reducing obesity, and decreasing tobacco and alcohol use. These interventions may help reduce inflammation and promote tissue repair and healing.

For cancer survivors, the life span may be longer than the health span, and these patients may benefit from an integrated model of care, with systematic screening and consolidated appointments, rather than a fragmented model in which departments and referrals are siloed, which may result in conflicting advice or redundancy, said Dr. Ganz.

Looking ahead, more research is needed to explore models of care delivery, as requirements for survivor care will vary among patients and care settings, Dr. Ganz said.

However, regardless of setting, treatment plans and shared decision-making can help reduce potential long-term or late-emerging effects, she said. Developing a survivorship care plan can help patients learn how to enhance their recovery.

During a question and answer session, Dr. Ganz was asked about whether hormone therapy could be used for patients with hormone negative breast cancer. “I think vaginal estrogen can be used if someone is on tamoxifen,” she said. However, “we need to be cautious” in case there are remaining estrogen positive cells, in order to avoid potential metastases, and use of hormone therapy in breast cancer survivors is an individualized decision based in part on quality of life.
 

Engaging a patient’s partner early can be helpful

If possible, engage the patient’s partner in survivorship discussions, said Luzia Travado, PhD, head of psycho-oncology at the Champalimaud Foundation, Lisbon, who presented on the topic of sexuality and commented on survivorship during the discussion. For those women with partners, engaging the partner early in treatment often means they are more likely to play a larger role in the post treatment and long term by providing stability and emotional support.

“Make sure partners are engaged and understand that they have a role, and that this role is valued,” she said. Unfortunately, there are a lot of divorced women with breast cancer, as the disease can take a toll on relationships. However, remember “sexuality is not just sex; it is caring, loving, and intimacy.”

“To end on a positive note, it is important to empower patients, and to give them self-management skills so they can make things even better in their survivorship,” said Dr. Ganz. In spite of discussing difficulties and challenges, one of the goals of the session was to offer potential solutions and answers.

Dr. Ganz disclosed serving as editor of the cancer survivorship section on Up-to-Date, and serving as a consultant for Blue Note Therapeutics, GRAIL, InformedDNA, and Roche-Genentech. Dr. Travado had no relevant financial conflicts to disclose.

Many of the persistent problems that breast cancer survivors experience after treatment are not part of the conversation with oncologists during treatment, said Patricia A. Ganz, MD, during a presentation at the European Society for Medical Oncology Breast Cancer annual congress.

Several studies suggest that many breast cancer patients are not well prepared to move forward after a breast cancer diagnosis and subsequent treatments, continued Dr. Ganz, who works at the UCLA Jonsson Comprehensive Cancer Center, Los Angeles.

Meeting the survivorship needs of breast cancer patients requires addressing both their physical and psychosocial needs, Dr. Ganz said. She explained how to achieve that, but first pointed to research elaborating on what's missing from some breast cancer survivors' care and barriers to these patients having their variety of health-related needs met.

In a 2021 study published in the Journal of Cancer Survivorship, Dr. Ganz and colleagues conducted a survey of approximately 200 medical oncologists in the United States. They determined that less than 50% provide survivorship care plans to patients at the end of treatment or communicate with patients’ other physicians about follow-up care.

In a secondary analysis of data from the same survey published in 2022 in Breast Cancer Research and Treatment, Dr. Ganz and colleagues examined medical oncologists’ perceived barriers to addressing both physical and psychosocial long-term effects in breast cancer survivors. For both, lack of time was the greatest perceived barrier, cited by nearly two-thirds of oncologists. Other barriers to addressing physical effects included lack of evidence-based, effective interventions, lack of clinical algorithms to guide care, and ambiguity regarding professional responsibility at the end of treatment. Other top barriers to addressing psychosocial issues included lack of mental health providers, lack of psychosocial resources, and lack of clinician knowledge and skills.

Data from additional studies suggest that, overall, cancer patients with greater physical burdens, such as more complex and lengthy treatment regimens, also have greater psychosocial needs, Dr. Ganz noted. Plus, approximately 15%-20% of cancer survivors have ongoing anxiety and depressive symptoms.

Shift to primary care

As more breast cancer and other cancer patients survive for longer periods, more care will likely occur in general medical settings, Dr. Ganz said. Issues to be addressed will include the potential increased risk of comorbid conditions for these survivors, and whether survivorship interventions earlier in the disease trajectory will impact survivorship. For cancer patients who achieve remission after treatment, the first 5 years after a diagnosis involves treatment and short-term surveillance for late effects. Beyond 5 years, care for cancer survivors mainly involves primary care and management of any comorbid conditions, as well as surveillance for late effects and recurrences, and awareness of new research.

A patient consultation early in the process after diagnosis is the start of a continuum of care, Dr. Ganz said. A patient consultation should address symptoms related to initial treatments, such as neuropathy, pain, fatigue, and insomnia, as well as the psychological symptoms of anxiety and depression. An early consultation also should evaluate adherence to endocrine therapy and management of symptoms, if needed, with the larger goal of preparing patients for recovery and the transition to survivorship, and what to expect for long-term follow-up.
 

Delivering the three P’s

The “Three P’s” of survivor care for breast cancer patients are palliation, prevention, and promotion of health, according to Dr. Ganz .

The first “P,” for palliative, is a key part of survivorship care, said Dr. Ganz. Palliative care is defined as care that focuses on reducing symptom severity and improving quality of life. The biological effects of cancer treatment can be associated with physical effects, such as functional limitations and frailty, and behavioral/cognitive effects such as depression, fatigue, and cognitive deficits, she said. To manage these effects and provide palliative care, consultation is needed with specialists in relevant areas including mental health, pain management, physical medicine/rehabilitation, endocrinology, cardiology, and neurology.

The second “P,” which is for prevention in survivorship care, refers to ongoing follow-up screening to identify any potentially serious late-onset complications such as osteoporosis or cardiac disease so they can be addressed, said Dr. Ganz. Other considerations include chemoprevention if available and genetic counseling for patients with hereditary cancers. Prevention also includes counseling patients about lifestyle modifications to help prevent additional cancer.

The goal of the third “P,” which is for health promotion, is to promote risk reduction for the health problems associated with accelerated aging that may arise in cancer survivors, said Dr. Ganz.

Health promotion strategies include maintaining a healthy weight, increasing physical activity, and avoiding harmful exposures, she said. Healthy lifestyle interventions can also reduce the risk of other chronic diseases such as diabetes and heart disease.

To that end, Dr. Ganz outlined several behavioral interventions that may mitigate the effects of cancer treatment on the accelerated aging process, including stress reduction in the form of meditation or yoga, cognitive behavioral therapy, improving sleep, increasing physical activity, reducing obesity, and decreasing tobacco and alcohol use. These interventions may help reduce inflammation and promote tissue repair and healing.

For cancer survivors, the life span may be longer than the health span, and these patients may benefit from an integrated model of care, with systematic screening and consolidated appointments, rather than a fragmented model in which departments and referrals are siloed, which may result in conflicting advice or redundancy, said Dr. Ganz.

Looking ahead, more research is needed to explore models of care delivery, as requirements for survivor care will vary among patients and care settings, Dr. Ganz said.

However, regardless of setting, treatment plans and shared decision-making can help reduce potential long-term or late-emerging effects, she said. Developing a survivorship care plan can help patients learn how to enhance their recovery.

During a question and answer session, Dr. Ganz was asked about whether hormone therapy could be used for patients with hormone negative breast cancer. “I think vaginal estrogen can be used if someone is on tamoxifen,” she said. However, “we need to be cautious” in case there are remaining estrogen positive cells, in order to avoid potential metastases, and use of hormone therapy in breast cancer survivors is an individualized decision based in part on quality of life.
 

Engaging a patient’s partner early can be helpful

If possible, engage the patient’s partner in survivorship discussions, said Luzia Travado, PhD, head of psycho-oncology at the Champalimaud Foundation, Lisbon, who presented on the topic of sexuality and commented on survivorship during the discussion. For those women with partners, engaging the partner early in treatment often means they are more likely to play a larger role in the post treatment and long term by providing stability and emotional support.

“Make sure partners are engaged and understand that they have a role, and that this role is valued,” she said. Unfortunately, there are a lot of divorced women with breast cancer, as the disease can take a toll on relationships. However, remember “sexuality is not just sex; it is caring, loving, and intimacy.”

“To end on a positive note, it is important to empower patients, and to give them self-management skills so they can make things even better in their survivorship,” said Dr. Ganz. In spite of discussing difficulties and challenges, one of the goals of the session was to offer potential solutions and answers.

Dr. Ganz disclosed serving as editor of the cancer survivorship section on Up-to-Date, and serving as a consultant for Blue Note Therapeutics, GRAIL, InformedDNA, and Roche-Genentech. Dr. Travado had no relevant financial conflicts to disclose.

Many of the persistent problems that breast cancer survivors experience after treatment are not part of the conversation with oncologists during treatment, said Patricia A. Ganz, MD, during a presentation at the European Society for Medical Oncology Breast Cancer annual congress.

Several studies suggest that many breast cancer patients are not well prepared to move forward after a breast cancer diagnosis and subsequent treatments, continued Dr. Ganz, who works at the UCLA Jonsson Comprehensive Cancer Center, Los Angeles.

Meeting the survivorship needs of breast cancer patients requires addressing both their physical and psychosocial needs, Dr. Ganz said. She explained how to achieve that, but first pointed to research elaborating on what's missing from some breast cancer survivors' care and barriers to these patients having their variety of health-related needs met.

In a 2021 study published in the Journal of Cancer Survivorship, Dr. Ganz and colleagues conducted a survey of approximately 200 medical oncologists in the United States. They determined that less than 50% provide survivorship care plans to patients at the end of treatment or communicate with patients’ other physicians about follow-up care.

In a secondary analysis of data from the same survey published in 2022 in Breast Cancer Research and Treatment, Dr. Ganz and colleagues examined medical oncologists’ perceived barriers to addressing both physical and psychosocial long-term effects in breast cancer survivors. For both, lack of time was the greatest perceived barrier, cited by nearly two-thirds of oncologists. Other barriers to addressing physical effects included lack of evidence-based, effective interventions, lack of clinical algorithms to guide care, and ambiguity regarding professional responsibility at the end of treatment. Other top barriers to addressing psychosocial issues included lack of mental health providers, lack of psychosocial resources, and lack of clinician knowledge and skills.

Data from additional studies suggest that, overall, cancer patients with greater physical burdens, such as more complex and lengthy treatment regimens, also have greater psychosocial needs, Dr. Ganz noted. Plus, approximately 15%-20% of cancer survivors have ongoing anxiety and depressive symptoms.

Shift to primary care

As more breast cancer and other cancer patients survive for longer periods, more care will likely occur in general medical settings, Dr. Ganz said. Issues to be addressed will include the potential increased risk of comorbid conditions for these survivors, and whether survivorship interventions earlier in the disease trajectory will impact survivorship. For cancer patients who achieve remission after treatment, the first 5 years after a diagnosis involves treatment and short-term surveillance for late effects. Beyond 5 years, care for cancer survivors mainly involves primary care and management of any comorbid conditions, as well as surveillance for late effects and recurrences, and awareness of new research.

A patient consultation early in the process after diagnosis is the start of a continuum of care, Dr. Ganz said. A patient consultation should address symptoms related to initial treatments, such as neuropathy, pain, fatigue, and insomnia, as well as the psychological symptoms of anxiety and depression. An early consultation also should evaluate adherence to endocrine therapy and management of symptoms, if needed, with the larger goal of preparing patients for recovery and the transition to survivorship, and what to expect for long-term follow-up.
 

Delivering the three P’s

The “Three P’s” of survivor care for breast cancer patients are palliation, prevention, and promotion of health, according to Dr. Ganz .

The first “P,” for palliative, is a key part of survivorship care, said Dr. Ganz. Palliative care is defined as care that focuses on reducing symptom severity and improving quality of life. The biological effects of cancer treatment can be associated with physical effects, such as functional limitations and frailty, and behavioral/cognitive effects such as depression, fatigue, and cognitive deficits, she said. To manage these effects and provide palliative care, consultation is needed with specialists in relevant areas including mental health, pain management, physical medicine/rehabilitation, endocrinology, cardiology, and neurology.

The second “P,” which is for prevention in survivorship care, refers to ongoing follow-up screening to identify any potentially serious late-onset complications such as osteoporosis or cardiac disease so they can be addressed, said Dr. Ganz. Other considerations include chemoprevention if available and genetic counseling for patients with hereditary cancers. Prevention also includes counseling patients about lifestyle modifications to help prevent additional cancer.

The goal of the third “P,” which is for health promotion, is to promote risk reduction for the health problems associated with accelerated aging that may arise in cancer survivors, said Dr. Ganz.

Health promotion strategies include maintaining a healthy weight, increasing physical activity, and avoiding harmful exposures, she said. Healthy lifestyle interventions can also reduce the risk of other chronic diseases such as diabetes and heart disease.

To that end, Dr. Ganz outlined several behavioral interventions that may mitigate the effects of cancer treatment on the accelerated aging process, including stress reduction in the form of meditation or yoga, cognitive behavioral therapy, improving sleep, increasing physical activity, reducing obesity, and decreasing tobacco and alcohol use. These interventions may help reduce inflammation and promote tissue repair and healing.

For cancer survivors, the life span may be longer than the health span, and these patients may benefit from an integrated model of care, with systematic screening and consolidated appointments, rather than a fragmented model in which departments and referrals are siloed, which may result in conflicting advice or redundancy, said Dr. Ganz.

Looking ahead, more research is needed to explore models of care delivery, as requirements for survivor care will vary among patients and care settings, Dr. Ganz said.

However, regardless of setting, treatment plans and shared decision-making can help reduce potential long-term or late-emerging effects, she said. Developing a survivorship care plan can help patients learn how to enhance their recovery.

During a question and answer session, Dr. Ganz was asked about whether hormone therapy could be used for patients with hormone negative breast cancer. “I think vaginal estrogen can be used if someone is on tamoxifen,” she said. However, “we need to be cautious” in case there are remaining estrogen positive cells, in order to avoid potential metastases, and use of hormone therapy in breast cancer survivors is an individualized decision based in part on quality of life.
 

Engaging a patient’s partner early can be helpful

If possible, engage the patient’s partner in survivorship discussions, said Luzia Travado, PhD, head of psycho-oncology at the Champalimaud Foundation, Lisbon, who presented on the topic of sexuality and commented on survivorship during the discussion. For those women with partners, engaging the partner early in treatment often means they are more likely to play a larger role in the post treatment and long term by providing stability and emotional support.

“Make sure partners are engaged and understand that they have a role, and that this role is valued,” she said. Unfortunately, there are a lot of divorced women with breast cancer, as the disease can take a toll on relationships. However, remember “sexuality is not just sex; it is caring, loving, and intimacy.”

“To end on a positive note, it is important to empower patients, and to give them self-management skills so they can make things even better in their survivorship,” said Dr. Ganz. In spite of discussing difficulties and challenges, one of the goals of the session was to offer potential solutions and answers.

Dr. Ganz disclosed serving as editor of the cancer survivorship section on Up-to-Date, and serving as a consultant for Blue Note Therapeutics, GRAIL, InformedDNA, and Roche-Genentech. Dr. Travado had no relevant financial conflicts to disclose.

“What am I allowed to do?” radiation oncologist Vivek Kavadi, MD, asked the business manager at Texas Oncology in Sugar Land, Tex.

Dr. Kavadi wanted to give his patient with early-stage breast cancer a standard radiation treatment – hypofractionated 3D conformal radiation therapy – following her lumpectomy.

But his hands were tied.

Dr. Kavadi had submitted a prior authorization request, but the patient’s health insurance was dragging its feet. And without prior authorization, Dr. Kavadi couldn’t schedule his patient’s first treatment.

“I chose the most cost-effective, standard treatment, but nothing could begin without the insurance company’s permission,” he said.

One of the most challenging aspects of the delay was explaining to his patient why he couldn’t schedule her treatment. “We would love to start, but your insurance company has not given us approval. The best I can do is give you a tentative appointment,” he recalled telling her.

After a few days with no word, calls to the insurance company began. “My patient called, I called, my office called,” Dr. Kavadi said. “It was a week or more of aggravation, stress, and time wasted for my patient and my team.”

This type of delay has become increasingly common in radiation oncology. One recent analysis estimated that 97% of radiation oncology services now require prior authorization under Medicare Advantage. And another analysis found that almost all radiation oncologists said prior authorization delays life-saving care for their patients.

The rise of prior authorization requirements may boil down to the fact that prior authorization is no longer reserved for uncertain or high-cost care. Terrence Cunningham, director of administrative simplification policy for the American Hospital Association, told this news organization last year that “prior authorization used to be applied only to new, costly, or high-risk services,” but now “many insurers require authorizations for even routine care, which is inappropriate.”

The growth of prior authorization requirements has forced many doctors, nurses, and pharmacists to dedicate part of their workday to handling requests and appealing denials and has forced many practices to hire staff exclusively for prior authorizations.

This additional work is costly.

One recent study found that the radiation oncology department of Vanderbilt University, Nashville, Tenn., spent nearly $500,000 annually in employee time to obtain prior authorization for radiation therapy treatments. Extrapolated nationally, the researchers estimated that physicians’ annual compensation for prior authorization duties came to an estimated $46 million. Overall, 86% of these costs were for treatments that were ultimately approved, the majority on initial request and some on appeal.

Dr. Kavadi has five full-time employees dedicated to managing prior authorization requests and challenges.

And after a week of delays and hours on the phone with the insurer, his patient’s radiation treatment was ultimately approved.

Dr. Kavadi wondered why something so simple needed to be so onerous.

Stretching out an approval for a standard radiation treatment “feels like a means of intentionally delaying care,” Dr. Kavadi said. “This is an example of a process that has run so far amok. It’s just a burden across the board.”

And even with his 30 years of experience, “I still have to ask my business supervisor what I am allowed to do,” he said. “I can’t just proceed with what’s best for my patient, what the patient has consented to, and what also happens to be the least expensive option.”

A version of this article first appeared on Medscape.com.

“What am I allowed to do?” radiation oncologist Vivek Kavadi, MD, asked the business manager at Texas Oncology in Sugar Land, Tex.

Dr. Kavadi wanted to give his patient with early-stage breast cancer a standard radiation treatment – hypofractionated 3D conformal radiation therapy – following her lumpectomy.

But his hands were tied.

Dr. Kavadi had submitted a prior authorization request, but the patient’s health insurance was dragging its feet. And without prior authorization, Dr. Kavadi couldn’t schedule his patient’s first treatment.

“I chose the most cost-effective, standard treatment, but nothing could begin without the insurance company’s permission,” he said.

One of the most challenging aspects of the delay was explaining to his patient why he couldn’t schedule her treatment. “We would love to start, but your insurance company has not given us approval. The best I can do is give you a tentative appointment,” he recalled telling her.

After a few days with no word, calls to the insurance company began. “My patient called, I called, my office called,” Dr. Kavadi said. “It was a week or more of aggravation, stress, and time wasted for my patient and my team.”

This type of delay has become increasingly common in radiation oncology. One recent analysis estimated that 97% of radiation oncology services now require prior authorization under Medicare Advantage. And another analysis found that almost all radiation oncologists said prior authorization delays life-saving care for their patients.

The rise of prior authorization requirements may boil down to the fact that prior authorization is no longer reserved for uncertain or high-cost care. Terrence Cunningham, director of administrative simplification policy for the American Hospital Association, told this news organization last year that “prior authorization used to be applied only to new, costly, or high-risk services,” but now “many insurers require authorizations for even routine care, which is inappropriate.”

The growth of prior authorization requirements has forced many doctors, nurses, and pharmacists to dedicate part of their workday to handling requests and appealing denials and has forced many practices to hire staff exclusively for prior authorizations.

This additional work is costly.

One recent study found that the radiation oncology department of Vanderbilt University, Nashville, Tenn., spent nearly $500,000 annually in employee time to obtain prior authorization for radiation therapy treatments. Extrapolated nationally, the researchers estimated that physicians’ annual compensation for prior authorization duties came to an estimated $46 million. Overall, 86% of these costs were for treatments that were ultimately approved, the majority on initial request and some on appeal.

Dr. Kavadi has five full-time employees dedicated to managing prior authorization requests and challenges.

And after a week of delays and hours on the phone with the insurer, his patient’s radiation treatment was ultimately approved.

Dr. Kavadi wondered why something so simple needed to be so onerous.

Stretching out an approval for a standard radiation treatment “feels like a means of intentionally delaying care,” Dr. Kavadi said. “This is an example of a process that has run so far amok. It’s just a burden across the board.”

And even with his 30 years of experience, “I still have to ask my business supervisor what I am allowed to do,” he said. “I can’t just proceed with what’s best for my patient, what the patient has consented to, and what also happens to be the least expensive option.”

A version of this article first appeared on Medscape.com.

“What am I allowed to do?” radiation oncologist Vivek Kavadi, MD, asked the business manager at Texas Oncology in Sugar Land, Tex.

Dr. Kavadi wanted to give his patient with early-stage breast cancer a standard radiation treatment – hypofractionated 3D conformal radiation therapy – following her lumpectomy.

But his hands were tied.

Dr. Kavadi had submitted a prior authorization request, but the patient’s health insurance was dragging its feet. And without prior authorization, Dr. Kavadi couldn’t schedule his patient’s first treatment.

“I chose the most cost-effective, standard treatment, but nothing could begin without the insurance company’s permission,” he said.

One of the most challenging aspects of the delay was explaining to his patient why he couldn’t schedule her treatment. “We would love to start, but your insurance company has not given us approval. The best I can do is give you a tentative appointment,” he recalled telling her.

After a few days with no word, calls to the insurance company began. “My patient called, I called, my office called,” Dr. Kavadi said. “It was a week or more of aggravation, stress, and time wasted for my patient and my team.”

This type of delay has become increasingly common in radiation oncology. One recent analysis estimated that 97% of radiation oncology services now require prior authorization under Medicare Advantage. And another analysis found that almost all radiation oncologists said prior authorization delays life-saving care for their patients.

The rise of prior authorization requirements may boil down to the fact that prior authorization is no longer reserved for uncertain or high-cost care. Terrence Cunningham, director of administrative simplification policy for the American Hospital Association, told this news organization last year that “prior authorization used to be applied only to new, costly, or high-risk services,” but now “many insurers require authorizations for even routine care, which is inappropriate.”

The growth of prior authorization requirements has forced many doctors, nurses, and pharmacists to dedicate part of their workday to handling requests and appealing denials and has forced many practices to hire staff exclusively for prior authorizations.

This additional work is costly.

One recent study found that the radiation oncology department of Vanderbilt University, Nashville, Tenn., spent nearly $500,000 annually in employee time to obtain prior authorization for radiation therapy treatments. Extrapolated nationally, the researchers estimated that physicians’ annual compensation for prior authorization duties came to an estimated $46 million. Overall, 86% of these costs were for treatments that were ultimately approved, the majority on initial request and some on appeal.

Dr. Kavadi has five full-time employees dedicated to managing prior authorization requests and challenges.

And after a week of delays and hours on the phone with the insurer, his patient’s radiation treatment was ultimately approved.

Dr. Kavadi wondered why something so simple needed to be so onerous.

Stretching out an approval for a standard radiation treatment “feels like a means of intentionally delaying care,” Dr. Kavadi said. “This is an example of a process that has run so far amok. It’s just a burden across the board.”

And even with his 30 years of experience, “I still have to ask my business supervisor what I am allowed to do,” he said. “I can’t just proceed with what’s best for my patient, what the patient has consented to, and what also happens to be the least expensive option.”

A version of this article first appeared on Medscape.com.

Inpatient dermatology consultation services are becoming increasingly prevalent in hospital institutions.^1-3 Although often underutilized as a consulting service, dermatology-related admissions cost hundreds of millions of dollars for the health care system.^1,2 Misdiagnosis, prolonged hospital stays, and incorrect treatment are common results of lack of involvement by a skin expert.^1-3 The importance of consultative inpatient dermatology cannot be understated. Accreditation Council for Graduate Medical Education requirements for proficiency in dermatology residency include exposure to inpatient dermatology, and it is our duty as residents to aid our colleagues in the management and treatment of cutaneous disease.

Although exposure to inpatient dermatology varies across residency programs, nearly every dermatology resident is bound to perform a consultation and be involved in the care of a hospitalized patient. At our program at the University of Utah (Salt Lake City), we have robust inpatient exposure, and after numerous hours spent on the forefront of inpatient dermatology, I have accrued a list of specific tips and techniques that have aided me as a resident clinician.

Pre-Rounding More Thoroughly

When I started as a postgraduate year 2 (PGY-2) on the inpatient dermatology rotation, I found myself perplexed. I had learned how to round in internal medicine but was unaccustomed to the nuances of specialty rounds. My list included calciphylaxis, small vessel vasculitis, cellulitis, stasis dermatitis, toxic epidermal necrolysis, and atypical mycobacterial infection. The first few days of service were undeniably difficult due to the daily consultations, complexity of admitted patients, and need for efficiency. I sometimes overlooked important laboratory test results, medication changes, and interdisciplinary discussions that prolonged rounding. As dermatologists, we are responsible for the largest organ of the body, and it is important to approach patients in a comprehensive manner. Pre-rounding should include reviewing interdisciplinary notes, laboratory values/results, and medications, and performing a focused skin examination with a review of systems during the encounter. Importantly, most electronic medical record systems offer an automated rounding sheet. In Epic (Epic Systems Corporation), I would use the bone marrow transplant rounding sheet, which includes laboratory test results, vitals, and medications. After printing out the rounding sheet, I would note important updates for each patient. Although pre-rounding and chart review requires time and effort, it aided me in providing elevated patient care and becoming more efficient during rounds. Over time I have come to strongly appreciate the term dermatology hospitalist. Cutaneous manifestations of systemic disease require thoughtful consideration and workup.

New Patient Consultations: Must-Ask Questions

Holding the university inpatient pager can be stressful. At the University of Utah, we often carry 5 to 10 patients on our list and receive 3 to 4 new consultations a day, sometimes right before 5 pm. When receiving new consultations, it is important to obtain the whole story from the requesting provider. Some of the questions may seem obvious, but I am frequently reminded of the inefficiencies I encountered as a first-year dermatology resident. Remember to always be kind to your consulting colleagues. Dermatology is a difficult discipline, and describing rashes is no easy task. Here is my systematic approach to questions that should be asked of the requesting provider during each new patient consultation:

• What is the patient’s name, room number, and medical record number?
• Is this patient getting admitted or admitted currently?
• Is the rash the reason for admission? (This can greatly help with triaging the urgency of evaluation.)
• Is the rash painful?
• Is this patient ill?
• How would you describe the rash?

When evaluating new patients, it is crucial to remember the morphology camps. Formulating a differential diagnosis on a complex patient can be difficult; however, remembering the morphology camps of acneiform, dermal, eczematous, erythematous, subcutaneous, vasculitic, vasculopathic, and vesiculobullous lesions can be extremely helpful. Additionally, it is crucial to perform a thorough and complete skin examination on every patient. When emphasizing the importance of this, I often am reminded of a humbling moment early in my training. Our team was consulted on a patient with cellulitis and stasis dermatitis. It was a busy day, and my examination was quick and focused on the lower and upper extremities, chest, and back. The patient improved from a cutaneous standpoint and was discharged. At follow-up the next week, one of my attending providers biopsied an atypical macule on the retroauricular region, which was found to be consistent with a stage 1A melanoma. Even on the longest and most tiring hospital days, it is important to perform a full-body skin examination on each patient. You may end up saving a life.

An Organized Toolbox: What to Carry

Similar to our ophthalmology colleagues who are seen carrying around a suitcase in the hospital, I highly recommend some form of a toolbox or bag for performing inpatient biopsies (Table). Carrying around an organized bag, albeit bulky and unfashionable, has saved me numerous trips back to clinic for unexpected complications including fixing leaky vessels, closing stubborn ulcers, and coordinating sedated biopsies in the operating room.

Final Thoughts

As I near the completion of my residency journey, I hope these tips will aid budding and current dermatology residents at excelling as dermatology hospitalists during inpatient rotations. Dermatologists can make a profound impact on a variety of patients, especially when treating hospitalized patients on the clinical forefront. Our role extends beyond the skin, as cutaneous manifestations of internal disease are not uncommon.

Inpatient dermatology consultation services are becoming increasingly prevalent in hospital institutions.^1-3 Although often underutilized as a consulting service, dermatology-related admissions cost hundreds of millions of dollars for the health care system.^1,2 Misdiagnosis, prolonged hospital stays, and incorrect treatment are common results of lack of involvement by a skin expert.^1-3 The importance of consultative inpatient dermatology cannot be understated. Accreditation Council for Graduate Medical Education requirements for proficiency in dermatology residency include exposure to inpatient dermatology, and it is our duty as residents to aid our colleagues in the management and treatment of cutaneous disease.

Although exposure to inpatient dermatology varies across residency programs, nearly every dermatology resident is bound to perform a consultation and be involved in the care of a hospitalized patient. At our program at the University of Utah (Salt Lake City), we have robust inpatient exposure, and after numerous hours spent on the forefront of inpatient dermatology, I have accrued a list of specific tips and techniques that have aided me as a resident clinician.

Pre-Rounding More Thoroughly

When I started as a postgraduate year 2 (PGY-2) on the inpatient dermatology rotation, I found myself perplexed. I had learned how to round in internal medicine but was unaccustomed to the nuances of specialty rounds. My list included calciphylaxis, small vessel vasculitis, cellulitis, stasis dermatitis, toxic epidermal necrolysis, and atypical mycobacterial infection. The first few days of service were undeniably difficult due to the daily consultations, complexity of admitted patients, and need for efficiency. I sometimes overlooked important laboratory test results, medication changes, and interdisciplinary discussions that prolonged rounding. As dermatologists, we are responsible for the largest organ of the body, and it is important to approach patients in a comprehensive manner. Pre-rounding should include reviewing interdisciplinary notes, laboratory values/results, and medications, and performing a focused skin examination with a review of systems during the encounter. Importantly, most electronic medical record systems offer an automated rounding sheet. In Epic (Epic Systems Corporation), I would use the bone marrow transplant rounding sheet, which includes laboratory test results, vitals, and medications. After printing out the rounding sheet, I would note important updates for each patient. Although pre-rounding and chart review requires time and effort, it aided me in providing elevated patient care and becoming more efficient during rounds. Over time I have come to strongly appreciate the term dermatology hospitalist. Cutaneous manifestations of systemic disease require thoughtful consideration and workup.

New Patient Consultations: Must-Ask Questions

Holding the university inpatient pager can be stressful. At the University of Utah, we often carry 5 to 10 patients on our list and receive 3 to 4 new consultations a day, sometimes right before 5 pm. When receiving new consultations, it is important to obtain the whole story from the requesting provider. Some of the questions may seem obvious, but I am frequently reminded of the inefficiencies I encountered as a first-year dermatology resident. Remember to always be kind to your consulting colleagues. Dermatology is a difficult discipline, and describing rashes is no easy task. Here is my systematic approach to questions that should be asked of the requesting provider during each new patient consultation:

• What is the patient’s name, room number, and medical record number?
• Is this patient getting admitted or admitted currently?
• Is the rash the reason for admission? (This can greatly help with triaging the urgency of evaluation.)
• Is the rash painful?
• Is this patient ill?
• How would you describe the rash?

When evaluating new patients, it is crucial to remember the morphology camps. Formulating a differential diagnosis on a complex patient can be difficult; however, remembering the morphology camps of acneiform, dermal, eczematous, erythematous, subcutaneous, vasculitic, vasculopathic, and vesiculobullous lesions can be extremely helpful. Additionally, it is crucial to perform a thorough and complete skin examination on every patient. When emphasizing the importance of this, I often am reminded of a humbling moment early in my training. Our team was consulted on a patient with cellulitis and stasis dermatitis. It was a busy day, and my examination was quick and focused on the lower and upper extremities, chest, and back. The patient improved from a cutaneous standpoint and was discharged. At follow-up the next week, one of my attending providers biopsied an atypical macule on the retroauricular region, which was found to be consistent with a stage 1A melanoma. Even on the longest and most tiring hospital days, it is important to perform a full-body skin examination on each patient. You may end up saving a life.

An Organized Toolbox: What to Carry

Similar to our ophthalmology colleagues who are seen carrying around a suitcase in the hospital, I highly recommend some form of a toolbox or bag for performing inpatient biopsies (Table). Carrying around an organized bag, albeit bulky and unfashionable, has saved me numerous trips back to clinic for unexpected complications including fixing leaky vessels, closing stubborn ulcers, and coordinating sedated biopsies in the operating room.

Final Thoughts

As I near the completion of my residency journey, I hope these tips will aid budding and current dermatology residents at excelling as dermatology hospitalists during inpatient rotations. Dermatologists can make a profound impact on a variety of patients, especially when treating hospitalized patients on the clinical forefront. Our role extends beyond the skin, as cutaneous manifestations of internal disease are not uncommon.

Inpatient dermatology consultation services are becoming increasingly prevalent in hospital institutions.^1-3 Although often underutilized as a consulting service, dermatology-related admissions cost hundreds of millions of dollars for the health care system.^1,2 Misdiagnosis, prolonged hospital stays, and incorrect treatment are common results of lack of involvement by a skin expert.^1-3 The importance of consultative inpatient dermatology cannot be understated. Accreditation Council for Graduate Medical Education requirements for proficiency in dermatology residency include exposure to inpatient dermatology, and it is our duty as residents to aid our colleagues in the management and treatment of cutaneous disease.

Although exposure to inpatient dermatology varies across residency programs, nearly every dermatology resident is bound to perform a consultation and be involved in the care of a hospitalized patient. At our program at the University of Utah (Salt Lake City), we have robust inpatient exposure, and after numerous hours spent on the forefront of inpatient dermatology, I have accrued a list of specific tips and techniques that have aided me as a resident clinician.

Pre-Rounding More Thoroughly

When I started as a postgraduate year 2 (PGY-2) on the inpatient dermatology rotation, I found myself perplexed. I had learned how to round in internal medicine but was unaccustomed to the nuances of specialty rounds. My list included calciphylaxis, small vessel vasculitis, cellulitis, stasis dermatitis, toxic epidermal necrolysis, and atypical mycobacterial infection. The first few days of service were undeniably difficult due to the daily consultations, complexity of admitted patients, and need for efficiency. I sometimes overlooked important laboratory test results, medication changes, and interdisciplinary discussions that prolonged rounding. As dermatologists, we are responsible for the largest organ of the body, and it is important to approach patients in a comprehensive manner. Pre-rounding should include reviewing interdisciplinary notes, laboratory values/results, and medications, and performing a focused skin examination with a review of systems during the encounter. Importantly, most electronic medical record systems offer an automated rounding sheet. In Epic (Epic Systems Corporation), I would use the bone marrow transplant rounding sheet, which includes laboratory test results, vitals, and medications. After printing out the rounding sheet, I would note important updates for each patient. Although pre-rounding and chart review requires time and effort, it aided me in providing elevated patient care and becoming more efficient during rounds. Over time I have come to strongly appreciate the term dermatology hospitalist. Cutaneous manifestations of systemic disease require thoughtful consideration and workup.

New Patient Consultations: Must-Ask Questions

Holding the university inpatient pager can be stressful. At the University of Utah, we often carry 5 to 10 patients on our list and receive 3 to 4 new consultations a day, sometimes right before 5 pm. When receiving new consultations, it is important to obtain the whole story from the requesting provider. Some of the questions may seem obvious, but I am frequently reminded of the inefficiencies I encountered as a first-year dermatology resident. Remember to always be kind to your consulting colleagues. Dermatology is a difficult discipline, and describing rashes is no easy task. Here is my systematic approach to questions that should be asked of the requesting provider during each new patient consultation:

• What is the patient’s name, room number, and medical record number?
• Is this patient getting admitted or admitted currently?
• Is the rash the reason for admission? (This can greatly help with triaging the urgency of evaluation.)
• Is the rash painful?
• Is this patient ill?
• How would you describe the rash?

When evaluating new patients, it is crucial to remember the morphology camps. Formulating a differential diagnosis on a complex patient can be difficult; however, remembering the morphology camps of acneiform, dermal, eczematous, erythematous, subcutaneous, vasculitic, vasculopathic, and vesiculobullous lesions can be extremely helpful. Additionally, it is crucial to perform a thorough and complete skin examination on every patient. When emphasizing the importance of this, I often am reminded of a humbling moment early in my training. Our team was consulted on a patient with cellulitis and stasis dermatitis. It was a busy day, and my examination was quick and focused on the lower and upper extremities, chest, and back. The patient improved from a cutaneous standpoint and was discharged. At follow-up the next week, one of my attending providers biopsied an atypical macule on the retroauricular region, which was found to be consistent with a stage 1A melanoma. Even on the longest and most tiring hospital days, it is important to perform a full-body skin examination on each patient. You may end up saving a life.

An Organized Toolbox: What to Carry

Similar to our ophthalmology colleagues who are seen carrying around a suitcase in the hospital, I highly recommend some form of a toolbox or bag for performing inpatient biopsies (Table). Carrying around an organized bag, albeit bulky and unfashionable, has saved me numerous trips back to clinic for unexpected complications including fixing leaky vessels, closing stubborn ulcers, and coordinating sedated biopsies in the operating room.

Final Thoughts

As I near the completion of my residency journey, I hope these tips will aid budding and current dermatology residents at excelling as dermatology hospitalists during inpatient rotations. Dermatologists can make a profound impact on a variety of patients, especially when treating hospitalized patients on the clinical forefront. Our role extends beyond the skin, as cutaneous manifestations of internal disease are not uncommon.

Leaders of medical student groups and legislators in a few states are trying to convince medical schools to end a century-old practice of legacy admissions, which they say offer preferential treatment to applicants based on their association with donors or alumni.

While an estimated 25% of public colleges and universities still use legacy admissions, a growing list of top medical schools have moved away from the practice over the last decade, including Johns Hopkins University, Baltimore, and Tufts University, Medford, Mass.

Legacy admissions contradict schools’ more inclusive policies, Senila Yasmin, MPH, a second-year medical student at Tufts University, said in an interview. While Tufts maintains legacy admissions for its undergraduate applicants, the medical school stopped the practice in 2021, said Ms. Yasmin, a member of a student group that lobbied against the school’s legacy preferences.

Describing herself as a low-income, first-generation Muslim-Pakistani American, Ms. Yasmin wants to use her experience at Tufts to improve accessibility for students like herself.

As a member of the American Medical Association (AMA) Medical Student Section, she coauthored a resolution stating that legacy admissions go against the AMA’s strategic plan to advance racial justice and health equity. The Student Section passed the resolution in November, and in June, the AMA House of Delegates will vote on whether to adopt the policy. 

Along with a Supreme Court decision that could strike down race-conscious college admissions, an AMA policy could convince medical schools to rethink legacy admissions and how to maintain diverse student bodies. In June, the court is expected to issue a decision in the Students for Fair Admissions lawsuit against Harvard University, Cambridge, Mass., and the University of North Carolina, Chapel Hill, which alleges that considering race in holistic admissions constitutes racial discrimination and violates the Equal Protection Clause.

Opponents of legacy admissions, like Ms. Yasmin, say it penalizes students from racial minorities and lower socioeconomic backgrounds, hampering a fair and equitable admissions process that attracts diverse medical school admissions.
 

Diversity of medical applicants

Diversity in medical schools  continued to increase last year with more Black, Hispanic, and female students applying and enrolling, according to a recent report by the Association of American Medical Colleges (AAMC). However, universities often include nonacademic criteria in their admission assessments to improve educational access for underrepresented minorities.

Medical schools carefully consider each applicant’s background “to yield a diverse class of students,” Geoffrey Young, PhD, AAMC’s senior director of transforming the health care workforce, told this news organization.

Some schools, such as Morehouse School of Medicine, Atlanta, the University of Virginia School of Medicine, Charlottesville, and the University of Arizona College of Medicine, Tucson, perform a thorough review of candidates while offering admissions practices designed specifically for legacy applicants. The schools assert that legacy designation doesn’t factor into the student’s likelihood of acceptance.

The arrangement may show that schools want to commit to equity and fairness but have trouble moving away from entrenched traditions, two professors from Penn State College of Medicine, Hershey, Pa., who sit on separate medical admissions subcommittees, wrote last year in Bioethics Today.
 

Legislation may hasten legacies’ end

In December, Ms. Yasmin and a group of Massachusetts Medical Society student-members presented another resolution to the state medical society, which adopted it.

The society’s new policy opposes the use of legacy status in medical school admissions and supports mechanisms to eliminate its inclusion from the application process, Theodore Calianos II, MD, FACS, president of the Massachusetts Medical Society, said in an interview.

“Legacy preferences limit racial and socioeconomic diversity on campuses, so we asked, ‘What can we do so that everyone has equal access to medical education?’ It is exciting to see the students and young physicians – the future of medicine – become involved in policymaking.”

Proposed laws may also hasten the end of legacy admissions. Last year, the U.S. Senate began considering a bill prohibiting colleges receiving federal financial aid from giving preferential treatment to students based on their relations to donors or alumni. However, the bill allows the Department of Education to make exceptions for institutions serving historically underrepresented groups.

The New York State Senate and the New York State Assembly also are reviewing bills that ban legacy and early admissions policies at public and private universities. Connecticut announced similar legislation last year. Massachusetts legislators are considering two bills: one that would ban the practice at the state’s public universities and another that would require all schools using legacy status to pay a “public service fee” equal to a percentage of its endowment. Colleges with endowment assets exceeding $2 billion must pay at least $2 million, according to the bill’s text.

At schools like Harvard,  whose endowment surpasses $50 billion, the option to pay the penalty will make the law moot, Michael Walls, DO, MPH, president of the American Medical Student Association (AMSA), said in an interview. “Smaller schools wouldn’t be able to afford the fine and are less likely to be doing [legacy admissions] anyway,” he said. “The schools that want to continue doing it could just pay the fine.”

Dr. Walls said AMSA supports race-conscious admissions processes and anything that increases fairness for medical school applicants. “Whatever [fair] means is up for interpretation, but it would be great to eliminate legacy admissions,” he said.   
 

A version of this article originally appeared on Medscape.com.

Leaders of medical student groups and legislators in a few states are trying to convince medical schools to end a century-old practice of legacy admissions, which they say offer preferential treatment to applicants based on their association with donors or alumni.

While an estimated 25% of public colleges and universities still use legacy admissions, a growing list of top medical schools have moved away from the practice over the last decade, including Johns Hopkins University, Baltimore, and Tufts University, Medford, Mass.

Legacy admissions contradict schools’ more inclusive policies, Senila Yasmin, MPH, a second-year medical student at Tufts University, said in an interview. While Tufts maintains legacy admissions for its undergraduate applicants, the medical school stopped the practice in 2021, said Ms. Yasmin, a member of a student group that lobbied against the school’s legacy preferences.

Describing herself as a low-income, first-generation Muslim-Pakistani American, Ms. Yasmin wants to use her experience at Tufts to improve accessibility for students like herself.

As a member of the American Medical Association (AMA) Medical Student Section, she coauthored a resolution stating that legacy admissions go against the AMA’s strategic plan to advance racial justice and health equity. The Student Section passed the resolution in November, and in June, the AMA House of Delegates will vote on whether to adopt the policy. 

Along with a Supreme Court decision that could strike down race-conscious college admissions, an AMA policy could convince medical schools to rethink legacy admissions and how to maintain diverse student bodies. In June, the court is expected to issue a decision in the Students for Fair Admissions lawsuit against Harvard University, Cambridge, Mass., and the University of North Carolina, Chapel Hill, which alleges that considering race in holistic admissions constitutes racial discrimination and violates the Equal Protection Clause.

Opponents of legacy admissions, like Ms. Yasmin, say it penalizes students from racial minorities and lower socioeconomic backgrounds, hampering a fair and equitable admissions process that attracts diverse medical school admissions.
 

Diversity of medical applicants

Diversity in medical schools  continued to increase last year with more Black, Hispanic, and female students applying and enrolling, according to a recent report by the Association of American Medical Colleges (AAMC). However, universities often include nonacademic criteria in their admission assessments to improve educational access for underrepresented minorities.

Medical schools carefully consider each applicant’s background “to yield a diverse class of students,” Geoffrey Young, PhD, AAMC’s senior director of transforming the health care workforce, told this news organization.

Some schools, such as Morehouse School of Medicine, Atlanta, the University of Virginia School of Medicine, Charlottesville, and the University of Arizona College of Medicine, Tucson, perform a thorough review of candidates while offering admissions practices designed specifically for legacy applicants. The schools assert that legacy designation doesn’t factor into the student’s likelihood of acceptance.

The arrangement may show that schools want to commit to equity and fairness but have trouble moving away from entrenched traditions, two professors from Penn State College of Medicine, Hershey, Pa., who sit on separate medical admissions subcommittees, wrote last year in Bioethics Today.
 

Legislation may hasten legacies’ end

In December, Ms. Yasmin and a group of Massachusetts Medical Society student-members presented another resolution to the state medical society, which adopted it.

The society’s new policy opposes the use of legacy status in medical school admissions and supports mechanisms to eliminate its inclusion from the application process, Theodore Calianos II, MD, FACS, president of the Massachusetts Medical Society, said in an interview.

“Legacy preferences limit racial and socioeconomic diversity on campuses, so we asked, ‘What can we do so that everyone has equal access to medical education?’ It is exciting to see the students and young physicians – the future of medicine – become involved in policymaking.”

Proposed laws may also hasten the end of legacy admissions. Last year, the U.S. Senate began considering a bill prohibiting colleges receiving federal financial aid from giving preferential treatment to students based on their relations to donors or alumni. However, the bill allows the Department of Education to make exceptions for institutions serving historically underrepresented groups.

The New York State Senate and the New York State Assembly also are reviewing bills that ban legacy and early admissions policies at public and private universities. Connecticut announced similar legislation last year. Massachusetts legislators are considering two bills: one that would ban the practice at the state’s public universities and another that would require all schools using legacy status to pay a “public service fee” equal to a percentage of its endowment. Colleges with endowment assets exceeding $2 billion must pay at least $2 million, according to the bill’s text.

At schools like Harvard,  whose endowment surpasses $50 billion, the option to pay the penalty will make the law moot, Michael Walls, DO, MPH, president of the American Medical Student Association (AMSA), said in an interview. “Smaller schools wouldn’t be able to afford the fine and are less likely to be doing [legacy admissions] anyway,” he said. “The schools that want to continue doing it could just pay the fine.”

Dr. Walls said AMSA supports race-conscious admissions processes and anything that increases fairness for medical school applicants. “Whatever [fair] means is up for interpretation, but it would be great to eliminate legacy admissions,” he said.   
 

A version of this article originally appeared on Medscape.com.

Leaders of medical student groups and legislators in a few states are trying to convince medical schools to end a century-old practice of legacy admissions, which they say offer preferential treatment to applicants based on their association with donors or alumni.

While an estimated 25% of public colleges and universities still use legacy admissions, a growing list of top medical schools have moved away from the practice over the last decade, including Johns Hopkins University, Baltimore, and Tufts University, Medford, Mass.

Legacy admissions contradict schools’ more inclusive policies, Senila Yasmin, MPH, a second-year medical student at Tufts University, said in an interview. While Tufts maintains legacy admissions for its undergraduate applicants, the medical school stopped the practice in 2021, said Ms. Yasmin, a member of a student group that lobbied against the school’s legacy preferences.

Describing herself as a low-income, first-generation Muslim-Pakistani American, Ms. Yasmin wants to use her experience at Tufts to improve accessibility for students like herself.

As a member of the American Medical Association (AMA) Medical Student Section, she coauthored a resolution stating that legacy admissions go against the AMA’s strategic plan to advance racial justice and health equity. The Student Section passed the resolution in November, and in June, the AMA House of Delegates will vote on whether to adopt the policy. 

Along with a Supreme Court decision that could strike down race-conscious college admissions, an AMA policy could convince medical schools to rethink legacy admissions and how to maintain diverse student bodies. In June, the court is expected to issue a decision in the Students for Fair Admissions lawsuit against Harvard University, Cambridge, Mass., and the University of North Carolina, Chapel Hill, which alleges that considering race in holistic admissions constitutes racial discrimination and violates the Equal Protection Clause.

Opponents of legacy admissions, like Ms. Yasmin, say it penalizes students from racial minorities and lower socioeconomic backgrounds, hampering a fair and equitable admissions process that attracts diverse medical school admissions.
 

Diversity of medical applicants

Diversity in medical schools  continued to increase last year with more Black, Hispanic, and female students applying and enrolling, according to a recent report by the Association of American Medical Colleges (AAMC). However, universities often include nonacademic criteria in their admission assessments to improve educational access for underrepresented minorities.

Medical schools carefully consider each applicant’s background “to yield a diverse class of students,” Geoffrey Young, PhD, AAMC’s senior director of transforming the health care workforce, told this news organization.

Some schools, such as Morehouse School of Medicine, Atlanta, the University of Virginia School of Medicine, Charlottesville, and the University of Arizona College of Medicine, Tucson, perform a thorough review of candidates while offering admissions practices designed specifically for legacy applicants. The schools assert that legacy designation doesn’t factor into the student’s likelihood of acceptance.

The arrangement may show that schools want to commit to equity and fairness but have trouble moving away from entrenched traditions, two professors from Penn State College of Medicine, Hershey, Pa., who sit on separate medical admissions subcommittees, wrote last year in Bioethics Today.
 

Legislation may hasten legacies’ end

In December, Ms. Yasmin and a group of Massachusetts Medical Society student-members presented another resolution to the state medical society, which adopted it.

The society’s new policy opposes the use of legacy status in medical school admissions and supports mechanisms to eliminate its inclusion from the application process, Theodore Calianos II, MD, FACS, president of the Massachusetts Medical Society, said in an interview.

“Legacy preferences limit racial and socioeconomic diversity on campuses, so we asked, ‘What can we do so that everyone has equal access to medical education?’ It is exciting to see the students and young physicians – the future of medicine – become involved in policymaking.”

Proposed laws may also hasten the end of legacy admissions. Last year, the U.S. Senate began considering a bill prohibiting colleges receiving federal financial aid from giving preferential treatment to students based on their relations to donors or alumni. However, the bill allows the Department of Education to make exceptions for institutions serving historically underrepresented groups.

The New York State Senate and the New York State Assembly also are reviewing bills that ban legacy and early admissions policies at public and private universities. Connecticut announced similar legislation last year. Massachusetts legislators are considering two bills: one that would ban the practice at the state’s public universities and another that would require all schools using legacy status to pay a “public service fee” equal to a percentage of its endowment. Colleges with endowment assets exceeding $2 billion must pay at least $2 million, according to the bill’s text.

At schools like Harvard,  whose endowment surpasses $50 billion, the option to pay the penalty will make the law moot, Michael Walls, DO, MPH, president of the American Medical Student Association (AMSA), said in an interview. “Smaller schools wouldn’t be able to afford the fine and are less likely to be doing [legacy admissions] anyway,” he said. “The schools that want to continue doing it could just pay the fine.”

Dr. Walls said AMSA supports race-conscious admissions processes and anything that increases fairness for medical school applicants. “Whatever [fair] means is up for interpretation, but it would be great to eliminate legacy admissions,” he said.   
 

A version of this article originally appeared on Medscape.com.