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Do Combined Pharmacist and Prescriber Efforts on Medication Reconciliation Reduce Postdischarge Patient Emergency Department Visits and Hospital Readmissions?
Healthcare systems are targeting effective strategies to improve patient safety and reduce hospital readmissions. Hospital readmissions can be detrimental to patients’ health, a source of avoidable healthcare costs, and are frequently a reflection of the quality of patient care during transitions of care. Medication reconciliation (Med Rec) was identified as 1 of 12 interventions that may reduce 30-day readmissions; however, rigorously designed studies are scarce.1,2 Published systematic reviews and meta-analyses have produced mixed conclusions regarding the impact of Med Rec on unplanned 30-day readmissions.2-4
In several studies, researchers have established the positive impact of Med Rec on reducing patient medication discrepancies and potential adverse drug events.4-8 Pharmacy-led Med Rec interventions have been shown to easily identify more clinically relevant and higher impact medication discrepancies when compared to usual care.8 In a systematic review, Mueller et al.2 suggest that there are several interrelated elements that determine if a Med Rec intervention will influence hospital readmissions. These elements form a multicomponent “bundle” of interventions, including a systematic medication history process, admission reconciliation, patient education on discharge, discharge reconciliation, and communication to outpatient providers.9 Several prospective randomized controlled studies have demonstrated lower readmission rates and fewer visits to the emergency department (ED) after implementing a comprehensive, interprofessional, bundled intervention (including Med Rec) from admission to discharge.10-13 A 2016 systematic review and meta-analysis specifically evaluated pharmacy-led Med Rec programs (the majority of which included interventions involving multicomponent bundles) and demonstrated a significant reduction in posthospital healthcare utilization.14
Although comprehensive, interprofessional, bundled interventions have been shown to reduce readmission rates and ED visits in randomized controlled trials (RCTs), limited resources often prevent hospitals from consistently implementing all aspects of these multicomponent interventions. In practice, clinicians may provide varying components of the bundle, such as the combination of admission medication history by the pharmacist and discharge Med Rec completed by the physician alone. The unique impact of combined pharmacist and prescriber Med Rec interventions from admission to discharge on readmissions remains inconclusive. Further, it is unclear which high-risk patient groups will benefit the most from these interventions. We set out to evaluate the impact of an enhanced, interprofessional Med Rec process from admission to discharge (characterized within the context of a novel taxonomy continuum that specifies clinician involvement and intensity of services) on readmissions to hospital and ED visits within 30 days of discharge.
METHODS
We conducted a retrospective, observational, analytical cohort study using QuadraMed’s Computerized Patient Record and the EMITT (Electronic Medication Information Transfer Tool)15 to collect data from 2007 to 2011.
Setting
The study was conducted at a 417-bed tertiary care teaching hospital in Toronto, Ontario, Canada.
Med Rec Process and Description of Exposure (Intervention)
The targeted clinical areas had sustained interprofessional models of patient care in place from admission to discharge. They also were actively using an in-house EMITT to facilitate the documentation and tracking of Med Rec efforts throughout patient admission, transfer, and discharge.15 On admission, the pharmacist conducted a best possible medication history (BPMH). A BPMH provides the cornerstone for Med Rec. It differs from a routine medication history in that it involves (1) a systematic process for interviewing the patient (or family) and (2) a review of at least one other reliable source of information (eg, a provincial medication database, an inspection of medication vials, or contact with the community pharmacy) to obtain and verify patient medications (prescribed and nonprescribed). The pharmacist recorded the BPMH in the electronic patient record. The application supported admission and discharge Med Rec. On discharge, there were 2 options: (1) the prescriber alone would review and complete the discharge Med Rec and generate electronic prescriptions (Table 1, Silver level care) or (2) the pharmacist would collaborate with the prescriber to complete the discharge reconciliation and the prescriber would electronically generate prescriptions (Table 1, Gold level care). All clinical areas had a combined pharmacist and prescriber Med Rec model in place at admission, but the proportion of patients receiving discharge reconciliation completed by pharmacist and prescriber versus the prescriber-alone varied based on the individual clinician’s practices.
Patient Selection
All consecutive hospitalized patients admitted and discharged by the general internal medicine [GIM] service from March 2007 to December 2011 were included. The GIM service was chosen for the main analysis because they had been performing the intervention for the longest period of time and had the largest population of patients. Patients were identified via their hospital-specific medical record identification number and specific hospital-visit number. Patients were excluded if any of the following occurred: (1) the length of stay of their index admission was less than 24 hours; (2) they died during the visit; (3) they were transferred to a separate acute care inpatient facility; or (4) they left hospital against medical advice. Patient visits were excluded as index cases from the analysis if they were returning within 90 days of a previous discharge.
Outcomes
The primary study outcome was the occurrence of an inpatient readmission or ED visit within 30 days of discharge. In our secondary analyses, we examined the impact of the intervention on high-risk patient populations, such as those ≥65 years of age, with a length of stay, acuity of admission, Charlson comorbidity index, and emergency department visits in past 6 months (LACE) index score ≥10 (see supplementary Appendix 1 for LACE score description), on high-alert medications (1 or more of warfarin, insulin, digoxin, and opioids), and on ≥10 medications.
Data Collection
Identification of Exposure of Interest
We used the electronic database to capture all patients who received pharmacist and prescriber supported admission-to-discharge reconciliation. We explicitly defined increasing intensity of Med Rec care in categories of Bronze, Silver, and Gold care levels (Table 1). The exposed (intervention) group received an enhanced Med Rec bundle (patients receiving Gold level care). The control group was made of patients receiving a partial Med Rec Bundle (patients receiving Silver or Bronze level of care or below).
Determination of Hospital Visits
A search of administrative databases was used to determine if patients admitted to the targeted services had an ED visit or urgent inpatient admission to the study hospital within 30 days.
Statistical Analysis
A logistic regression for outcomes was performed. This yielded an adjusted odds ratio with a 95% confidence interval (CI) between the intervention and control groups. Statistical significance was determined with a 2-sided α level of 0.05. In the analysis, we used Statistical Analysis Software version 9.2.
In our multivariate logistic regression model, we adjusted for confounding factors that might influence the patients’ risk of readmission or the type of Med Rec they received upon discharge. By using administrative databases, patient level demographics, and the Charlson comorbidity index, the most responsible diagnosis and disease burden were collected. Medication-related factors collected included the number of medications on discharge and the presence of predefined high-alert medications. The number of medications on the medication discharge list was determined by using the electronic database. The final adjustment model included age, gender, the number of medications on discharge, and the LACE index score (supplementary Appendix 1). The LACE index score has been validated in Ontario, Canada, populations to quantify the risk of death or unplanned readmission within 30 days of discharge.24
Propensity Score Adjustment
Propensity scoring (probability of treatment assignment conditional on observed baseline characteristics) was planned a priori to account for possible factors that would impact whether a patient received the intervention or control care levels. The propensity score for receiving Med Rec was computed from a logistic model using Med Rec as the outcome. A structured iterative approach was used to refine this model to achieve covariate balance within the matched pairs. Covariate balance was measured by the standardized difference, in which an absolute standardized difference >10% represents meaningful imbalance.25 From the original cohort, we attempted to match patients who had the intervention to patients from the control by means of a matching algorithm using the logit of the propensity score for receiving the intervention.26
Subgroup Analysis
We also examined the impact of the intervention on high-risk patient populations such as those ≥65 years of age, with a LACE index score ≥10, on high-alert medications, and on ≥10 medications. A univariate analysis was conducted to identify patient-related risk predictors that may be independently correlated with a higher risk of hospital visits.
RESULTS
Baseline Characteristics
A total of 8678 patients representing 9931 unique visits met the inclusion criteria for analysis. There were 2541 unique visits (approximately 26% of visits) in the intervention group that received Gold level care and 7390 unique visits in the control group. The patients in the control group were largely patients who received the original standard of care at the institution, Silver level care (67% of the control group). Patients who received Bronze level care or less comprised 33% of the control group.
Patients in the intervention group were significantly older (average of 68 years old versus 64 years old) and on more medications. They also notably had a longer duration of stay in hospital, an increased percentage of visits with a LACE index score ≥10, and were more likely to be discharged home on a high-alert medication and with supports (Table 2).
Main Analysis
The main unadjusted analysis of GIM patients (n = 9931 visits) did not detect a difference in 30-day ED visits and readmissions between the intervention group (540 out of 2541; 21.2%) and control (1423 out of 7390; 19.3%; Table 3). By using a multivariate logistic regression model to account for age, sex, LACE index, and number of medications on discharge, the adjusted odds ratio was 1.06 (95% CI, 0.95-1.19; P = 0.33). After propensity score adjustment, the relative risk of readmission was 0.88 (16.7% vs 18.9%; 95% CI, 0.59-1.32; P = 0.54).
Secondary Analyses
In each predefined high-risk patient subgroup (age ≥65, LACE index score ≥10, number of discharge medications ≥10, and the presence of high-alert medications), analyses of our primary endpoint did not detect significant adjusted odds ratios (Table 4). In our univariate analysis, increasing number of medications, LACE index score, and male gender were independently correlated with a higher risk of hospital visits (supplementary Appendix 2).
DISCUSSION
Med Rec is widely recommended as a patient safety strategy to prevent clinically significant medication discrepancies at transitions in care.4-9 However, Med Rec varies widely in terms of what it entails and who delivers it, with the preponderance of evidence suggesting an impact on clinically significant medication discrepancies only when interprofessional care delivered includes a central role for pharmacists.27 Furthermore, Med Rec appears to impact short term readmissions only when embedded in a broader, multifaceted, bundled intervention in which pharmacists or other team members educate patients about their medications and deliver postdischarge follow-up phone calls.10-13
As very few hospitals have the resources to sustainably deliver intensive care bundles that are represented in RCTs (characterized by Platinum and Diamond levels of care in Table 1), in our observational study, we sought to explore whether a resource-attainable, enhanced Med Rec care bundle (Gold level) had an impact on hospital utilization compared to partial Med Rec care bundles (Bronze and Silver levels). In our findings, we did not detect a significant difference on ED visits and readmissions within 30 days between enhanced and partial care bundles. In a secondary analysis of the influence of the intervention on prespecified high-risk patient subgroups, we also did not detect a difference.
As far as we are aware, our long-term, observational study is the largest to date to explore a real-life, enhanced Med Rec intervention and examine its impact on meaningful patient outcomes. We extrapolated that our intervention group received several critical attributes of a successful bundle as discussed by Mueller in a systematic review.2 Our intervention included the following: (1) a systematic BPMH process on admission; (2) integrated admission-to-discharge reconciliation processes; (3) discharge delineation of medication changes since admission; (4) pharmacist involvement in reconciliation from admission to discharge; (5) an electronic platform; and (6) formal discharge reconciliation with interprofessional collaboration. Additional components in the bundle described by Mueller included the following: patient education at discharge, postdischarge communication with the patient, and communication with outpatient providers and medication management.
In our results, we did not find a difference in outcomes between the intervention and control groups. Therefore, it is possible that the enhanced bundle’s focus on interprofessional involvement in discharge reconciliation (Gold care level) has no impact on hospital utilization compared to partial care bundles (Silver and Bronze levels). Kwan et al.3 describe similar findings in their systematic review, in which they evaluated the effects of hospital-based Med Rec on unintentional discrepancies with nontrivial risks for harm to patients on 30-day postdischarge hospital visits. Kwan et al.3 concluded that larger well-designed studies are required to further evaluate this outcome, but authors of current published studies suggest that Med Rec alone probably does not reduce postdischarge hospital utilization within 30 days. Med Rec may have a more significant impact on utilization when bundled with other interventions that improve discharge coordination.3
There may be several reasons why we were unable to detect a significant difference between the intervention and control groups. One limitation is that our nonrandomized, retrospective design may have led to unmeasured confounders that impacted allocation into the intervention group versus the control group. It was notable that patients in the intervention group had an increased age, longer duration of hospital stay, more medications, and high-alert medications on discharge compared to the control group and that may have biased our results towards the null hypothesis. Although the propensity score analysis attempted to adjust for this, it also did not detect a significant difference between groups.
In addition, the existing standard of care during the study period allowed for patients in the control group to receive varying levels of Med Rec. Ideally, we would have compared the intervention to a placebo group that did not receive any Med Rec-related care elements. However, as this was a real-life observational study, the majority of patients received some Med Rec services as a part of the standard of care. As a result, 67% of patients in the control group received Silver level Med Rec with a BPMH, admission reconciliation, and prescriber-only discharge reconciliation. This may have made it more difficult to show an incremental benefit on readmissions between the intervention and control.
Also, our primary outcome of all-cause ED or hospital readmissions within 30 days may not have been sensitive enough to detect the effect of Med Rec interventions alone. Only a small proportion of readmissions within 30 days of discharge are preventable and many patient and community level factors responsible for readmissions cannot be controlled by the hospital’s actions.28 Comprehensive pharmacy interventions have demonstrated decreased hospitalizations and emergency visits at 12 months; however, the largest impact was seen on the more specific outcome of medication-related hospitalizations (80% reduction).29 Lastly, another limitation was that we were unable to capture hospital visits to other centres. However, in our region, almost 75% of readmissions are to the same site as the initial hospitalization.30
Overall, our findings in this study and novel characterization of Med Rec services are relevant to many hospital sites that are striving to implement integrated Med Rec with limited healthcare resources. Although interprofessional Med Rec likely reduces clinically significant medication discrepancies, enhanced interprofessional Med Rec on discharge (Gold Med Rec) alone may not be enough to impact hospital utilization compared to partial Med Rec services (Silver and Bronze Med Rec). Further research into practical, targeted Med Rec bundles on more specific outcomes (such as preventable postdischarge adverse events, “avoidable” hospital readmissions, and medication-related readmissions) may detect a significant benefit.
CONCLUSION
A long-term observational evaluation of interprofessional Med Rec did not detect a difference in 30-day postdischarge patient hospital visits between patients who received enhanced versus partial Med Rec patient care bundles. Researchers of future prospective studies could focus on evaluating high-risk populations or specific elements of Med Rec services on avoidable medication-related hospital admissions and postdischarge adverse drug events.
Acknowledgments
The authors thank Nita Dhir, MBA.
Presented as a poster and oral presentation at the 2012 American College of Clinical Pharmacy Annual Meeting, Hollywood, Florida, October 21-24, 2012, and as an encore poster presentation at the Canadian Society of Hospital Pharmacists Professional Practice Conference, Toronto, Canada, Feb 3, 2013.
Disclosure
The authors declare no conflicts of interest related to the manuscript submitted. All monies used for the research came from the University Health Network Department of Pharmacy Budget, including the pharmacy residency program.
1. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155:520-528. PubMed
2. Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057-1069. PubMed
3. Kwan JL, Lo L, Sampson M, Shojania KG. Medication reconciliation during transitions of care as a patient safety strategy: a systematic review. Ann Intern Med. 2013;158:397-403. PubMed
4. Safer Health Care Now. Medication Reconciliation in Home Care Getting Started Kit. March 2015. www.ismp-canada.org/download/MedRec/Medrec_HC_English_GSK_v2.pdf. Accessed August 22, 2017.
5. Karapinar-Çarkit F, Borgsteede SD, Zoer J, Smit HJ, Egberts AC, van den Bemt PM. Effect of medication reconciliation with and without patient counseling on the number of pharmaceutical interventions among patients discharged from the hospital. Ann Pharmacother. 2009;43(6):1001-1010. PubMed
6. Wong JD, Bajcar JM, Wong GG, et al. Medication reconciliation at hospital discharge: evaluating discrepancies. Ann Pharmacother. 2008;42(10):1373-1379. PubMed
7. Schnipper JL, Hamann C, Ndumele CD, et al. Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. Arch Intern Med. 2009;169(8):771-780. PubMed
8. Mekonnen AB, McLachlan AJ, Brien JA. Pharmacy-led medication reconciliation programmes at hospital transitions: a systematic review and meta-analysis. J Clin Pharm Ther. 2016;41(2):128-144. PubMed
9. Kaboli PJ, Fernandes O. Medication reconciliation: moving forward. Arch Intern Med. 2012;172(14):1069-1070. PubMed
10. Koehler BE, Richter KM, Youngblood L, et al. Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4:211-218. PubMed
11. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150:178-187. PubMed
12. Gillespie U, Alassaad A, Henrohn D, et al. A comprehensive pharmacist intervention to reduce morbidity in patients 80 years or older. Arch Intern Med. 2009:169(9):894-900. PubMed
13. Makowsky MJ, Koshman SL, Midodzi WK, Tsuyuki RT. Capturing outcomes of clinical activities performed by a rounding pharmacist practicing in a team environment: the COLLABORATE study [NCT00351676]. Med Care. 2009;47(6):642-650. PubMed
14. Mekonnen AB, McLachlan AJ, Brien JA. Effectiveness of pharmacist-led medication reconciliation programmes on clinical outcomes at hospital transitions: a systematic review and meta-analysis. BMJ Open. 2016;6(2):e010003. PubMed
15. Cesta A, Bajcar JM, Ong SW, Fernandes OA. The EMITT study: development and evaluation of a medication information transfer tool. Ann Pharmacother. 2006:40(6):1074-1081 PubMed
16. Cornish P, et al. Unintended medication discrepancies at the time of hospital admission. Arch Internal Medicine, 2005, Feb: 165: 424-29. PubMed
17. Kwan Y, Fernandes OA, Nagge JJ, et al. Pharmacist medication assessments in a surgical preadmission clinic. Arch Intern Med. 2007;167(10):1034-1040 PubMed
18. Dedhia P, Kravet S, Bulger J, et al. A quality improvement intervention to facilitate the transition of older adults from three hospitals back to their homes. J Am Geriatr Soc. 2009;57:1540–1546. PubMed
19. Murphy EM, Oxencis CJ, Klauck JA, et al. Medication reconciliation at an academic medical center: implementation of a comprehensive program from admission to discharge. Am J Health Syst Pharm. 2009;66:2126–31 PubMed
20. , , , , , . A pharmacy discharge plan for hospitalized elderly patients - a randomized controlled trial. Age and Ageing. 2001;30(1):33-40. PubMed
21. Al-Rashed SA, Wright DJ, Roebuck N, et al. The value of inpatient pharmaceutical counselling to elderly patients prior to discharge. Br J Clin Pharmacol. 2002 Dec;54(6):657–64. PubMed
22. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006 Mar 13;166(5):565–71. PubMed
23. Walker PC, Bernstein SJ, Jones JN, et al. Impact of a pharmacist-facilitated hospital discharge program: a quasi-experimental study. Arch Intern Med. 2009 Nov 23;169(21):2003–10. PubMed
24. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551-557. PubMed
25. Normand ST, Landrum MB, Guadagnoli E, et al. Validating recommendations for coronary angiography following an acute myocardial infarction in the elderly: a matched analysis using propensity scores. J Clin Epidemiol. 2001;54(4):387-398. PubMed
26. Rosenbaum PR., Donald BR. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat. 1985;39(1):33-38.
27. Fernandes O, Shojania KG. Medication reconciliation in the hospital: what, why, where, when, who and how? Healthc Q. 2012;15(Special Issue):42-49. PubMed
28. Joynt KE, Jha AK. Thirty-day readmissions—truth and consequences. N Engl J Med. 2012;366(15):1366-1369. PubMed
29. Zed PJ, Abu-Laban RB, Balen RM, et al. Incidence, severity and preventability of medication-related visits to the emergency department: a prospective study. CMAJ. 2008;178(12):1563-1569. PubMed
30. Gruneir A, Dhalla IA, van Walraven C, et al. Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Med. 2011;5(2):e104-e111. PubMed
Healthcare systems are targeting effective strategies to improve patient safety and reduce hospital readmissions. Hospital readmissions can be detrimental to patients’ health, a source of avoidable healthcare costs, and are frequently a reflection of the quality of patient care during transitions of care. Medication reconciliation (Med Rec) was identified as 1 of 12 interventions that may reduce 30-day readmissions; however, rigorously designed studies are scarce.1,2 Published systematic reviews and meta-analyses have produced mixed conclusions regarding the impact of Med Rec on unplanned 30-day readmissions.2-4
In several studies, researchers have established the positive impact of Med Rec on reducing patient medication discrepancies and potential adverse drug events.4-8 Pharmacy-led Med Rec interventions have been shown to easily identify more clinically relevant and higher impact medication discrepancies when compared to usual care.8 In a systematic review, Mueller et al.2 suggest that there are several interrelated elements that determine if a Med Rec intervention will influence hospital readmissions. These elements form a multicomponent “bundle” of interventions, including a systematic medication history process, admission reconciliation, patient education on discharge, discharge reconciliation, and communication to outpatient providers.9 Several prospective randomized controlled studies have demonstrated lower readmission rates and fewer visits to the emergency department (ED) after implementing a comprehensive, interprofessional, bundled intervention (including Med Rec) from admission to discharge.10-13 A 2016 systematic review and meta-analysis specifically evaluated pharmacy-led Med Rec programs (the majority of which included interventions involving multicomponent bundles) and demonstrated a significant reduction in posthospital healthcare utilization.14
Although comprehensive, interprofessional, bundled interventions have been shown to reduce readmission rates and ED visits in randomized controlled trials (RCTs), limited resources often prevent hospitals from consistently implementing all aspects of these multicomponent interventions. In practice, clinicians may provide varying components of the bundle, such as the combination of admission medication history by the pharmacist and discharge Med Rec completed by the physician alone. The unique impact of combined pharmacist and prescriber Med Rec interventions from admission to discharge on readmissions remains inconclusive. Further, it is unclear which high-risk patient groups will benefit the most from these interventions. We set out to evaluate the impact of an enhanced, interprofessional Med Rec process from admission to discharge (characterized within the context of a novel taxonomy continuum that specifies clinician involvement and intensity of services) on readmissions to hospital and ED visits within 30 days of discharge.
METHODS
We conducted a retrospective, observational, analytical cohort study using QuadraMed’s Computerized Patient Record and the EMITT (Electronic Medication Information Transfer Tool)15 to collect data from 2007 to 2011.
Setting
The study was conducted at a 417-bed tertiary care teaching hospital in Toronto, Ontario, Canada.
Med Rec Process and Description of Exposure (Intervention)
The targeted clinical areas had sustained interprofessional models of patient care in place from admission to discharge. They also were actively using an in-house EMITT to facilitate the documentation and tracking of Med Rec efforts throughout patient admission, transfer, and discharge.15 On admission, the pharmacist conducted a best possible medication history (BPMH). A BPMH provides the cornerstone for Med Rec. It differs from a routine medication history in that it involves (1) a systematic process for interviewing the patient (or family) and (2) a review of at least one other reliable source of information (eg, a provincial medication database, an inspection of medication vials, or contact with the community pharmacy) to obtain and verify patient medications (prescribed and nonprescribed). The pharmacist recorded the BPMH in the electronic patient record. The application supported admission and discharge Med Rec. On discharge, there were 2 options: (1) the prescriber alone would review and complete the discharge Med Rec and generate electronic prescriptions (Table 1, Silver level care) or (2) the pharmacist would collaborate with the prescriber to complete the discharge reconciliation and the prescriber would electronically generate prescriptions (Table 1, Gold level care). All clinical areas had a combined pharmacist and prescriber Med Rec model in place at admission, but the proportion of patients receiving discharge reconciliation completed by pharmacist and prescriber versus the prescriber-alone varied based on the individual clinician’s practices.
Patient Selection
All consecutive hospitalized patients admitted and discharged by the general internal medicine [GIM] service from March 2007 to December 2011 were included. The GIM service was chosen for the main analysis because they had been performing the intervention for the longest period of time and had the largest population of patients. Patients were identified via their hospital-specific medical record identification number and specific hospital-visit number. Patients were excluded if any of the following occurred: (1) the length of stay of their index admission was less than 24 hours; (2) they died during the visit; (3) they were transferred to a separate acute care inpatient facility; or (4) they left hospital against medical advice. Patient visits were excluded as index cases from the analysis if they were returning within 90 days of a previous discharge.
Outcomes
The primary study outcome was the occurrence of an inpatient readmission or ED visit within 30 days of discharge. In our secondary analyses, we examined the impact of the intervention on high-risk patient populations, such as those ≥65 years of age, with a length of stay, acuity of admission, Charlson comorbidity index, and emergency department visits in past 6 months (LACE) index score ≥10 (see supplementary Appendix 1 for LACE score description), on high-alert medications (1 or more of warfarin, insulin, digoxin, and opioids), and on ≥10 medications.
Data Collection
Identification of Exposure of Interest
We used the electronic database to capture all patients who received pharmacist and prescriber supported admission-to-discharge reconciliation. We explicitly defined increasing intensity of Med Rec care in categories of Bronze, Silver, and Gold care levels (Table 1). The exposed (intervention) group received an enhanced Med Rec bundle (patients receiving Gold level care). The control group was made of patients receiving a partial Med Rec Bundle (patients receiving Silver or Bronze level of care or below).
Determination of Hospital Visits
A search of administrative databases was used to determine if patients admitted to the targeted services had an ED visit or urgent inpatient admission to the study hospital within 30 days.
Statistical Analysis
A logistic regression for outcomes was performed. This yielded an adjusted odds ratio with a 95% confidence interval (CI) between the intervention and control groups. Statistical significance was determined with a 2-sided α level of 0.05. In the analysis, we used Statistical Analysis Software version 9.2.
In our multivariate logistic regression model, we adjusted for confounding factors that might influence the patients’ risk of readmission or the type of Med Rec they received upon discharge. By using administrative databases, patient level demographics, and the Charlson comorbidity index, the most responsible diagnosis and disease burden were collected. Medication-related factors collected included the number of medications on discharge and the presence of predefined high-alert medications. The number of medications on the medication discharge list was determined by using the electronic database. The final adjustment model included age, gender, the number of medications on discharge, and the LACE index score (supplementary Appendix 1). The LACE index score has been validated in Ontario, Canada, populations to quantify the risk of death or unplanned readmission within 30 days of discharge.24
Propensity Score Adjustment
Propensity scoring (probability of treatment assignment conditional on observed baseline characteristics) was planned a priori to account for possible factors that would impact whether a patient received the intervention or control care levels. The propensity score for receiving Med Rec was computed from a logistic model using Med Rec as the outcome. A structured iterative approach was used to refine this model to achieve covariate balance within the matched pairs. Covariate balance was measured by the standardized difference, in which an absolute standardized difference >10% represents meaningful imbalance.25 From the original cohort, we attempted to match patients who had the intervention to patients from the control by means of a matching algorithm using the logit of the propensity score for receiving the intervention.26
Subgroup Analysis
We also examined the impact of the intervention on high-risk patient populations such as those ≥65 years of age, with a LACE index score ≥10, on high-alert medications, and on ≥10 medications. A univariate analysis was conducted to identify patient-related risk predictors that may be independently correlated with a higher risk of hospital visits.
RESULTS
Baseline Characteristics
A total of 8678 patients representing 9931 unique visits met the inclusion criteria for analysis. There were 2541 unique visits (approximately 26% of visits) in the intervention group that received Gold level care and 7390 unique visits in the control group. The patients in the control group were largely patients who received the original standard of care at the institution, Silver level care (67% of the control group). Patients who received Bronze level care or less comprised 33% of the control group.
Patients in the intervention group were significantly older (average of 68 years old versus 64 years old) and on more medications. They also notably had a longer duration of stay in hospital, an increased percentage of visits with a LACE index score ≥10, and were more likely to be discharged home on a high-alert medication and with supports (Table 2).
Main Analysis
The main unadjusted analysis of GIM patients (n = 9931 visits) did not detect a difference in 30-day ED visits and readmissions between the intervention group (540 out of 2541; 21.2%) and control (1423 out of 7390; 19.3%; Table 3). By using a multivariate logistic regression model to account for age, sex, LACE index, and number of medications on discharge, the adjusted odds ratio was 1.06 (95% CI, 0.95-1.19; P = 0.33). After propensity score adjustment, the relative risk of readmission was 0.88 (16.7% vs 18.9%; 95% CI, 0.59-1.32; P = 0.54).
Secondary Analyses
In each predefined high-risk patient subgroup (age ≥65, LACE index score ≥10, number of discharge medications ≥10, and the presence of high-alert medications), analyses of our primary endpoint did not detect significant adjusted odds ratios (Table 4). In our univariate analysis, increasing number of medications, LACE index score, and male gender were independently correlated with a higher risk of hospital visits (supplementary Appendix 2).
DISCUSSION
Med Rec is widely recommended as a patient safety strategy to prevent clinically significant medication discrepancies at transitions in care.4-9 However, Med Rec varies widely in terms of what it entails and who delivers it, with the preponderance of evidence suggesting an impact on clinically significant medication discrepancies only when interprofessional care delivered includes a central role for pharmacists.27 Furthermore, Med Rec appears to impact short term readmissions only when embedded in a broader, multifaceted, bundled intervention in which pharmacists or other team members educate patients about their medications and deliver postdischarge follow-up phone calls.10-13
As very few hospitals have the resources to sustainably deliver intensive care bundles that are represented in RCTs (characterized by Platinum and Diamond levels of care in Table 1), in our observational study, we sought to explore whether a resource-attainable, enhanced Med Rec care bundle (Gold level) had an impact on hospital utilization compared to partial Med Rec care bundles (Bronze and Silver levels). In our findings, we did not detect a significant difference on ED visits and readmissions within 30 days between enhanced and partial care bundles. In a secondary analysis of the influence of the intervention on prespecified high-risk patient subgroups, we also did not detect a difference.
As far as we are aware, our long-term, observational study is the largest to date to explore a real-life, enhanced Med Rec intervention and examine its impact on meaningful patient outcomes. We extrapolated that our intervention group received several critical attributes of a successful bundle as discussed by Mueller in a systematic review.2 Our intervention included the following: (1) a systematic BPMH process on admission; (2) integrated admission-to-discharge reconciliation processes; (3) discharge delineation of medication changes since admission; (4) pharmacist involvement in reconciliation from admission to discharge; (5) an electronic platform; and (6) formal discharge reconciliation with interprofessional collaboration. Additional components in the bundle described by Mueller included the following: patient education at discharge, postdischarge communication with the patient, and communication with outpatient providers and medication management.
In our results, we did not find a difference in outcomes between the intervention and control groups. Therefore, it is possible that the enhanced bundle’s focus on interprofessional involvement in discharge reconciliation (Gold care level) has no impact on hospital utilization compared to partial care bundles (Silver and Bronze levels). Kwan et al.3 describe similar findings in their systematic review, in which they evaluated the effects of hospital-based Med Rec on unintentional discrepancies with nontrivial risks for harm to patients on 30-day postdischarge hospital visits. Kwan et al.3 concluded that larger well-designed studies are required to further evaluate this outcome, but authors of current published studies suggest that Med Rec alone probably does not reduce postdischarge hospital utilization within 30 days. Med Rec may have a more significant impact on utilization when bundled with other interventions that improve discharge coordination.3
There may be several reasons why we were unable to detect a significant difference between the intervention and control groups. One limitation is that our nonrandomized, retrospective design may have led to unmeasured confounders that impacted allocation into the intervention group versus the control group. It was notable that patients in the intervention group had an increased age, longer duration of hospital stay, more medications, and high-alert medications on discharge compared to the control group and that may have biased our results towards the null hypothesis. Although the propensity score analysis attempted to adjust for this, it also did not detect a significant difference between groups.
In addition, the existing standard of care during the study period allowed for patients in the control group to receive varying levels of Med Rec. Ideally, we would have compared the intervention to a placebo group that did not receive any Med Rec-related care elements. However, as this was a real-life observational study, the majority of patients received some Med Rec services as a part of the standard of care. As a result, 67% of patients in the control group received Silver level Med Rec with a BPMH, admission reconciliation, and prescriber-only discharge reconciliation. This may have made it more difficult to show an incremental benefit on readmissions between the intervention and control.
Also, our primary outcome of all-cause ED or hospital readmissions within 30 days may not have been sensitive enough to detect the effect of Med Rec interventions alone. Only a small proportion of readmissions within 30 days of discharge are preventable and many patient and community level factors responsible for readmissions cannot be controlled by the hospital’s actions.28 Comprehensive pharmacy interventions have demonstrated decreased hospitalizations and emergency visits at 12 months; however, the largest impact was seen on the more specific outcome of medication-related hospitalizations (80% reduction).29 Lastly, another limitation was that we were unable to capture hospital visits to other centres. However, in our region, almost 75% of readmissions are to the same site as the initial hospitalization.30
Overall, our findings in this study and novel characterization of Med Rec services are relevant to many hospital sites that are striving to implement integrated Med Rec with limited healthcare resources. Although interprofessional Med Rec likely reduces clinically significant medication discrepancies, enhanced interprofessional Med Rec on discharge (Gold Med Rec) alone may not be enough to impact hospital utilization compared to partial Med Rec services (Silver and Bronze Med Rec). Further research into practical, targeted Med Rec bundles on more specific outcomes (such as preventable postdischarge adverse events, “avoidable” hospital readmissions, and medication-related readmissions) may detect a significant benefit.
CONCLUSION
A long-term observational evaluation of interprofessional Med Rec did not detect a difference in 30-day postdischarge patient hospital visits between patients who received enhanced versus partial Med Rec patient care bundles. Researchers of future prospective studies could focus on evaluating high-risk populations or specific elements of Med Rec services on avoidable medication-related hospital admissions and postdischarge adverse drug events.
Acknowledgments
The authors thank Nita Dhir, MBA.
Presented as a poster and oral presentation at the 2012 American College of Clinical Pharmacy Annual Meeting, Hollywood, Florida, October 21-24, 2012, and as an encore poster presentation at the Canadian Society of Hospital Pharmacists Professional Practice Conference, Toronto, Canada, Feb 3, 2013.
Disclosure
The authors declare no conflicts of interest related to the manuscript submitted. All monies used for the research came from the University Health Network Department of Pharmacy Budget, including the pharmacy residency program.
Healthcare systems are targeting effective strategies to improve patient safety and reduce hospital readmissions. Hospital readmissions can be detrimental to patients’ health, a source of avoidable healthcare costs, and are frequently a reflection of the quality of patient care during transitions of care. Medication reconciliation (Med Rec) was identified as 1 of 12 interventions that may reduce 30-day readmissions; however, rigorously designed studies are scarce.1,2 Published systematic reviews and meta-analyses have produced mixed conclusions regarding the impact of Med Rec on unplanned 30-day readmissions.2-4
In several studies, researchers have established the positive impact of Med Rec on reducing patient medication discrepancies and potential adverse drug events.4-8 Pharmacy-led Med Rec interventions have been shown to easily identify more clinically relevant and higher impact medication discrepancies when compared to usual care.8 In a systematic review, Mueller et al.2 suggest that there are several interrelated elements that determine if a Med Rec intervention will influence hospital readmissions. These elements form a multicomponent “bundle” of interventions, including a systematic medication history process, admission reconciliation, patient education on discharge, discharge reconciliation, and communication to outpatient providers.9 Several prospective randomized controlled studies have demonstrated lower readmission rates and fewer visits to the emergency department (ED) after implementing a comprehensive, interprofessional, bundled intervention (including Med Rec) from admission to discharge.10-13 A 2016 systematic review and meta-analysis specifically evaluated pharmacy-led Med Rec programs (the majority of which included interventions involving multicomponent bundles) and demonstrated a significant reduction in posthospital healthcare utilization.14
Although comprehensive, interprofessional, bundled interventions have been shown to reduce readmission rates and ED visits in randomized controlled trials (RCTs), limited resources often prevent hospitals from consistently implementing all aspects of these multicomponent interventions. In practice, clinicians may provide varying components of the bundle, such as the combination of admission medication history by the pharmacist and discharge Med Rec completed by the physician alone. The unique impact of combined pharmacist and prescriber Med Rec interventions from admission to discharge on readmissions remains inconclusive. Further, it is unclear which high-risk patient groups will benefit the most from these interventions. We set out to evaluate the impact of an enhanced, interprofessional Med Rec process from admission to discharge (characterized within the context of a novel taxonomy continuum that specifies clinician involvement and intensity of services) on readmissions to hospital and ED visits within 30 days of discharge.
METHODS
We conducted a retrospective, observational, analytical cohort study using QuadraMed’s Computerized Patient Record and the EMITT (Electronic Medication Information Transfer Tool)15 to collect data from 2007 to 2011.
Setting
The study was conducted at a 417-bed tertiary care teaching hospital in Toronto, Ontario, Canada.
Med Rec Process and Description of Exposure (Intervention)
The targeted clinical areas had sustained interprofessional models of patient care in place from admission to discharge. They also were actively using an in-house EMITT to facilitate the documentation and tracking of Med Rec efforts throughout patient admission, transfer, and discharge.15 On admission, the pharmacist conducted a best possible medication history (BPMH). A BPMH provides the cornerstone for Med Rec. It differs from a routine medication history in that it involves (1) a systematic process for interviewing the patient (or family) and (2) a review of at least one other reliable source of information (eg, a provincial medication database, an inspection of medication vials, or contact with the community pharmacy) to obtain and verify patient medications (prescribed and nonprescribed). The pharmacist recorded the BPMH in the electronic patient record. The application supported admission and discharge Med Rec. On discharge, there were 2 options: (1) the prescriber alone would review and complete the discharge Med Rec and generate electronic prescriptions (Table 1, Silver level care) or (2) the pharmacist would collaborate with the prescriber to complete the discharge reconciliation and the prescriber would electronically generate prescriptions (Table 1, Gold level care). All clinical areas had a combined pharmacist and prescriber Med Rec model in place at admission, but the proportion of patients receiving discharge reconciliation completed by pharmacist and prescriber versus the prescriber-alone varied based on the individual clinician’s practices.
Patient Selection
All consecutive hospitalized patients admitted and discharged by the general internal medicine [GIM] service from March 2007 to December 2011 were included. The GIM service was chosen for the main analysis because they had been performing the intervention for the longest period of time and had the largest population of patients. Patients were identified via their hospital-specific medical record identification number and specific hospital-visit number. Patients were excluded if any of the following occurred: (1) the length of stay of their index admission was less than 24 hours; (2) they died during the visit; (3) they were transferred to a separate acute care inpatient facility; or (4) they left hospital against medical advice. Patient visits were excluded as index cases from the analysis if they were returning within 90 days of a previous discharge.
Outcomes
The primary study outcome was the occurrence of an inpatient readmission or ED visit within 30 days of discharge. In our secondary analyses, we examined the impact of the intervention on high-risk patient populations, such as those ≥65 years of age, with a length of stay, acuity of admission, Charlson comorbidity index, and emergency department visits in past 6 months (LACE) index score ≥10 (see supplementary Appendix 1 for LACE score description), on high-alert medications (1 or more of warfarin, insulin, digoxin, and opioids), and on ≥10 medications.
Data Collection
Identification of Exposure of Interest
We used the electronic database to capture all patients who received pharmacist and prescriber supported admission-to-discharge reconciliation. We explicitly defined increasing intensity of Med Rec care in categories of Bronze, Silver, and Gold care levels (Table 1). The exposed (intervention) group received an enhanced Med Rec bundle (patients receiving Gold level care). The control group was made of patients receiving a partial Med Rec Bundle (patients receiving Silver or Bronze level of care or below).
Determination of Hospital Visits
A search of administrative databases was used to determine if patients admitted to the targeted services had an ED visit or urgent inpatient admission to the study hospital within 30 days.
Statistical Analysis
A logistic regression for outcomes was performed. This yielded an adjusted odds ratio with a 95% confidence interval (CI) between the intervention and control groups. Statistical significance was determined with a 2-sided α level of 0.05. In the analysis, we used Statistical Analysis Software version 9.2.
In our multivariate logistic regression model, we adjusted for confounding factors that might influence the patients’ risk of readmission or the type of Med Rec they received upon discharge. By using administrative databases, patient level demographics, and the Charlson comorbidity index, the most responsible diagnosis and disease burden were collected. Medication-related factors collected included the number of medications on discharge and the presence of predefined high-alert medications. The number of medications on the medication discharge list was determined by using the electronic database. The final adjustment model included age, gender, the number of medications on discharge, and the LACE index score (supplementary Appendix 1). The LACE index score has been validated in Ontario, Canada, populations to quantify the risk of death or unplanned readmission within 30 days of discharge.24
Propensity Score Adjustment
Propensity scoring (probability of treatment assignment conditional on observed baseline characteristics) was planned a priori to account for possible factors that would impact whether a patient received the intervention or control care levels. The propensity score for receiving Med Rec was computed from a logistic model using Med Rec as the outcome. A structured iterative approach was used to refine this model to achieve covariate balance within the matched pairs. Covariate balance was measured by the standardized difference, in which an absolute standardized difference >10% represents meaningful imbalance.25 From the original cohort, we attempted to match patients who had the intervention to patients from the control by means of a matching algorithm using the logit of the propensity score for receiving the intervention.26
Subgroup Analysis
We also examined the impact of the intervention on high-risk patient populations such as those ≥65 years of age, with a LACE index score ≥10, on high-alert medications, and on ≥10 medications. A univariate analysis was conducted to identify patient-related risk predictors that may be independently correlated with a higher risk of hospital visits.
RESULTS
Baseline Characteristics
A total of 8678 patients representing 9931 unique visits met the inclusion criteria for analysis. There were 2541 unique visits (approximately 26% of visits) in the intervention group that received Gold level care and 7390 unique visits in the control group. The patients in the control group were largely patients who received the original standard of care at the institution, Silver level care (67% of the control group). Patients who received Bronze level care or less comprised 33% of the control group.
Patients in the intervention group were significantly older (average of 68 years old versus 64 years old) and on more medications. They also notably had a longer duration of stay in hospital, an increased percentage of visits with a LACE index score ≥10, and were more likely to be discharged home on a high-alert medication and with supports (Table 2).
Main Analysis
The main unadjusted analysis of GIM patients (n = 9931 visits) did not detect a difference in 30-day ED visits and readmissions between the intervention group (540 out of 2541; 21.2%) and control (1423 out of 7390; 19.3%; Table 3). By using a multivariate logistic regression model to account for age, sex, LACE index, and number of medications on discharge, the adjusted odds ratio was 1.06 (95% CI, 0.95-1.19; P = 0.33). After propensity score adjustment, the relative risk of readmission was 0.88 (16.7% vs 18.9%; 95% CI, 0.59-1.32; P = 0.54).
Secondary Analyses
In each predefined high-risk patient subgroup (age ≥65, LACE index score ≥10, number of discharge medications ≥10, and the presence of high-alert medications), analyses of our primary endpoint did not detect significant adjusted odds ratios (Table 4). In our univariate analysis, increasing number of medications, LACE index score, and male gender were independently correlated with a higher risk of hospital visits (supplementary Appendix 2).
DISCUSSION
Med Rec is widely recommended as a patient safety strategy to prevent clinically significant medication discrepancies at transitions in care.4-9 However, Med Rec varies widely in terms of what it entails and who delivers it, with the preponderance of evidence suggesting an impact on clinically significant medication discrepancies only when interprofessional care delivered includes a central role for pharmacists.27 Furthermore, Med Rec appears to impact short term readmissions only when embedded in a broader, multifaceted, bundled intervention in which pharmacists or other team members educate patients about their medications and deliver postdischarge follow-up phone calls.10-13
As very few hospitals have the resources to sustainably deliver intensive care bundles that are represented in RCTs (characterized by Platinum and Diamond levels of care in Table 1), in our observational study, we sought to explore whether a resource-attainable, enhanced Med Rec care bundle (Gold level) had an impact on hospital utilization compared to partial Med Rec care bundles (Bronze and Silver levels). In our findings, we did not detect a significant difference on ED visits and readmissions within 30 days between enhanced and partial care bundles. In a secondary analysis of the influence of the intervention on prespecified high-risk patient subgroups, we also did not detect a difference.
As far as we are aware, our long-term, observational study is the largest to date to explore a real-life, enhanced Med Rec intervention and examine its impact on meaningful patient outcomes. We extrapolated that our intervention group received several critical attributes of a successful bundle as discussed by Mueller in a systematic review.2 Our intervention included the following: (1) a systematic BPMH process on admission; (2) integrated admission-to-discharge reconciliation processes; (3) discharge delineation of medication changes since admission; (4) pharmacist involvement in reconciliation from admission to discharge; (5) an electronic platform; and (6) formal discharge reconciliation with interprofessional collaboration. Additional components in the bundle described by Mueller included the following: patient education at discharge, postdischarge communication with the patient, and communication with outpatient providers and medication management.
In our results, we did not find a difference in outcomes between the intervention and control groups. Therefore, it is possible that the enhanced bundle’s focus on interprofessional involvement in discharge reconciliation (Gold care level) has no impact on hospital utilization compared to partial care bundles (Silver and Bronze levels). Kwan et al.3 describe similar findings in their systematic review, in which they evaluated the effects of hospital-based Med Rec on unintentional discrepancies with nontrivial risks for harm to patients on 30-day postdischarge hospital visits. Kwan et al.3 concluded that larger well-designed studies are required to further evaluate this outcome, but authors of current published studies suggest that Med Rec alone probably does not reduce postdischarge hospital utilization within 30 days. Med Rec may have a more significant impact on utilization when bundled with other interventions that improve discharge coordination.3
There may be several reasons why we were unable to detect a significant difference between the intervention and control groups. One limitation is that our nonrandomized, retrospective design may have led to unmeasured confounders that impacted allocation into the intervention group versus the control group. It was notable that patients in the intervention group had an increased age, longer duration of hospital stay, more medications, and high-alert medications on discharge compared to the control group and that may have biased our results towards the null hypothesis. Although the propensity score analysis attempted to adjust for this, it also did not detect a significant difference between groups.
In addition, the existing standard of care during the study period allowed for patients in the control group to receive varying levels of Med Rec. Ideally, we would have compared the intervention to a placebo group that did not receive any Med Rec-related care elements. However, as this was a real-life observational study, the majority of patients received some Med Rec services as a part of the standard of care. As a result, 67% of patients in the control group received Silver level Med Rec with a BPMH, admission reconciliation, and prescriber-only discharge reconciliation. This may have made it more difficult to show an incremental benefit on readmissions between the intervention and control.
Also, our primary outcome of all-cause ED or hospital readmissions within 30 days may not have been sensitive enough to detect the effect of Med Rec interventions alone. Only a small proportion of readmissions within 30 days of discharge are preventable and many patient and community level factors responsible for readmissions cannot be controlled by the hospital’s actions.28 Comprehensive pharmacy interventions have demonstrated decreased hospitalizations and emergency visits at 12 months; however, the largest impact was seen on the more specific outcome of medication-related hospitalizations (80% reduction).29 Lastly, another limitation was that we were unable to capture hospital visits to other centres. However, in our region, almost 75% of readmissions are to the same site as the initial hospitalization.30
Overall, our findings in this study and novel characterization of Med Rec services are relevant to many hospital sites that are striving to implement integrated Med Rec with limited healthcare resources. Although interprofessional Med Rec likely reduces clinically significant medication discrepancies, enhanced interprofessional Med Rec on discharge (Gold Med Rec) alone may not be enough to impact hospital utilization compared to partial Med Rec services (Silver and Bronze Med Rec). Further research into practical, targeted Med Rec bundles on more specific outcomes (such as preventable postdischarge adverse events, “avoidable” hospital readmissions, and medication-related readmissions) may detect a significant benefit.
CONCLUSION
A long-term observational evaluation of interprofessional Med Rec did not detect a difference in 30-day postdischarge patient hospital visits between patients who received enhanced versus partial Med Rec patient care bundles. Researchers of future prospective studies could focus on evaluating high-risk populations or specific elements of Med Rec services on avoidable medication-related hospital admissions and postdischarge adverse drug events.
Acknowledgments
The authors thank Nita Dhir, MBA.
Presented as a poster and oral presentation at the 2012 American College of Clinical Pharmacy Annual Meeting, Hollywood, Florida, October 21-24, 2012, and as an encore poster presentation at the Canadian Society of Hospital Pharmacists Professional Practice Conference, Toronto, Canada, Feb 3, 2013.
Disclosure
The authors declare no conflicts of interest related to the manuscript submitted. All monies used for the research came from the University Health Network Department of Pharmacy Budget, including the pharmacy residency program.
1. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155:520-528. PubMed
2. Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057-1069. PubMed
3. Kwan JL, Lo L, Sampson M, Shojania KG. Medication reconciliation during transitions of care as a patient safety strategy: a systematic review. Ann Intern Med. 2013;158:397-403. PubMed
4. Safer Health Care Now. Medication Reconciliation in Home Care Getting Started Kit. March 2015. www.ismp-canada.org/download/MedRec/Medrec_HC_English_GSK_v2.pdf. Accessed August 22, 2017.
5. Karapinar-Çarkit F, Borgsteede SD, Zoer J, Smit HJ, Egberts AC, van den Bemt PM. Effect of medication reconciliation with and without patient counseling on the number of pharmaceutical interventions among patients discharged from the hospital. Ann Pharmacother. 2009;43(6):1001-1010. PubMed
6. Wong JD, Bajcar JM, Wong GG, et al. Medication reconciliation at hospital discharge: evaluating discrepancies. Ann Pharmacother. 2008;42(10):1373-1379. PubMed
7. Schnipper JL, Hamann C, Ndumele CD, et al. Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. Arch Intern Med. 2009;169(8):771-780. PubMed
8. Mekonnen AB, McLachlan AJ, Brien JA. Pharmacy-led medication reconciliation programmes at hospital transitions: a systematic review and meta-analysis. J Clin Pharm Ther. 2016;41(2):128-144. PubMed
9. Kaboli PJ, Fernandes O. Medication reconciliation: moving forward. Arch Intern Med. 2012;172(14):1069-1070. PubMed
10. Koehler BE, Richter KM, Youngblood L, et al. Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4:211-218. PubMed
11. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150:178-187. PubMed
12. Gillespie U, Alassaad A, Henrohn D, et al. A comprehensive pharmacist intervention to reduce morbidity in patients 80 years or older. Arch Intern Med. 2009:169(9):894-900. PubMed
13. Makowsky MJ, Koshman SL, Midodzi WK, Tsuyuki RT. Capturing outcomes of clinical activities performed by a rounding pharmacist practicing in a team environment: the COLLABORATE study [NCT00351676]. Med Care. 2009;47(6):642-650. PubMed
14. Mekonnen AB, McLachlan AJ, Brien JA. Effectiveness of pharmacist-led medication reconciliation programmes on clinical outcomes at hospital transitions: a systematic review and meta-analysis. BMJ Open. 2016;6(2):e010003. PubMed
15. Cesta A, Bajcar JM, Ong SW, Fernandes OA. The EMITT study: development and evaluation of a medication information transfer tool. Ann Pharmacother. 2006:40(6):1074-1081 PubMed
16. Cornish P, et al. Unintended medication discrepancies at the time of hospital admission. Arch Internal Medicine, 2005, Feb: 165: 424-29. PubMed
17. Kwan Y, Fernandes OA, Nagge JJ, et al. Pharmacist medication assessments in a surgical preadmission clinic. Arch Intern Med. 2007;167(10):1034-1040 PubMed
18. Dedhia P, Kravet S, Bulger J, et al. A quality improvement intervention to facilitate the transition of older adults from three hospitals back to their homes. J Am Geriatr Soc. 2009;57:1540–1546. PubMed
19. Murphy EM, Oxencis CJ, Klauck JA, et al. Medication reconciliation at an academic medical center: implementation of a comprehensive program from admission to discharge. Am J Health Syst Pharm. 2009;66:2126–31 PubMed
20. , , , , , . A pharmacy discharge plan for hospitalized elderly patients - a randomized controlled trial. Age and Ageing. 2001;30(1):33-40. PubMed
21. Al-Rashed SA, Wright DJ, Roebuck N, et al. The value of inpatient pharmaceutical counselling to elderly patients prior to discharge. Br J Clin Pharmacol. 2002 Dec;54(6):657–64. PubMed
22. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006 Mar 13;166(5):565–71. PubMed
23. Walker PC, Bernstein SJ, Jones JN, et al. Impact of a pharmacist-facilitated hospital discharge program: a quasi-experimental study. Arch Intern Med. 2009 Nov 23;169(21):2003–10. PubMed
24. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551-557. PubMed
25. Normand ST, Landrum MB, Guadagnoli E, et al. Validating recommendations for coronary angiography following an acute myocardial infarction in the elderly: a matched analysis using propensity scores. J Clin Epidemiol. 2001;54(4):387-398. PubMed
26. Rosenbaum PR., Donald BR. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat. 1985;39(1):33-38.
27. Fernandes O, Shojania KG. Medication reconciliation in the hospital: what, why, where, when, who and how? Healthc Q. 2012;15(Special Issue):42-49. PubMed
28. Joynt KE, Jha AK. Thirty-day readmissions—truth and consequences. N Engl J Med. 2012;366(15):1366-1369. PubMed
29. Zed PJ, Abu-Laban RB, Balen RM, et al. Incidence, severity and preventability of medication-related visits to the emergency department: a prospective study. CMAJ. 2008;178(12):1563-1569. PubMed
30. Gruneir A, Dhalla IA, van Walraven C, et al. Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Med. 2011;5(2):e104-e111. PubMed
1. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155:520-528. PubMed
2. Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057-1069. PubMed
3. Kwan JL, Lo L, Sampson M, Shojania KG. Medication reconciliation during transitions of care as a patient safety strategy: a systematic review. Ann Intern Med. 2013;158:397-403. PubMed
4. Safer Health Care Now. Medication Reconciliation in Home Care Getting Started Kit. March 2015. www.ismp-canada.org/download/MedRec/Medrec_HC_English_GSK_v2.pdf. Accessed August 22, 2017.
5. Karapinar-Çarkit F, Borgsteede SD, Zoer J, Smit HJ, Egberts AC, van den Bemt PM. Effect of medication reconciliation with and without patient counseling on the number of pharmaceutical interventions among patients discharged from the hospital. Ann Pharmacother. 2009;43(6):1001-1010. PubMed
6. Wong JD, Bajcar JM, Wong GG, et al. Medication reconciliation at hospital discharge: evaluating discrepancies. Ann Pharmacother. 2008;42(10):1373-1379. PubMed
7. Schnipper JL, Hamann C, Ndumele CD, et al. Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. Arch Intern Med. 2009;169(8):771-780. PubMed
8. Mekonnen AB, McLachlan AJ, Brien JA. Pharmacy-led medication reconciliation programmes at hospital transitions: a systematic review and meta-analysis. J Clin Pharm Ther. 2016;41(2):128-144. PubMed
9. Kaboli PJ, Fernandes O. Medication reconciliation: moving forward. Arch Intern Med. 2012;172(14):1069-1070. PubMed
10. Koehler BE, Richter KM, Youngblood L, et al. Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4:211-218. PubMed
11. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150:178-187. PubMed
12. Gillespie U, Alassaad A, Henrohn D, et al. A comprehensive pharmacist intervention to reduce morbidity in patients 80 years or older. Arch Intern Med. 2009:169(9):894-900. PubMed
13. Makowsky MJ, Koshman SL, Midodzi WK, Tsuyuki RT. Capturing outcomes of clinical activities performed by a rounding pharmacist practicing in a team environment: the COLLABORATE study [NCT00351676]. Med Care. 2009;47(6):642-650. PubMed
14. Mekonnen AB, McLachlan AJ, Brien JA. Effectiveness of pharmacist-led medication reconciliation programmes on clinical outcomes at hospital transitions: a systematic review and meta-analysis. BMJ Open. 2016;6(2):e010003. PubMed
15. Cesta A, Bajcar JM, Ong SW, Fernandes OA. The EMITT study: development and evaluation of a medication information transfer tool. Ann Pharmacother. 2006:40(6):1074-1081 PubMed
16. Cornish P, et al. Unintended medication discrepancies at the time of hospital admission. Arch Internal Medicine, 2005, Feb: 165: 424-29. PubMed
17. Kwan Y, Fernandes OA, Nagge JJ, et al. Pharmacist medication assessments in a surgical preadmission clinic. Arch Intern Med. 2007;167(10):1034-1040 PubMed
18. Dedhia P, Kravet S, Bulger J, et al. A quality improvement intervention to facilitate the transition of older adults from three hospitals back to their homes. J Am Geriatr Soc. 2009;57:1540–1546. PubMed
19. Murphy EM, Oxencis CJ, Klauck JA, et al. Medication reconciliation at an academic medical center: implementation of a comprehensive program from admission to discharge. Am J Health Syst Pharm. 2009;66:2126–31 PubMed
20. , , , , , . A pharmacy discharge plan for hospitalized elderly patients - a randomized controlled trial. Age and Ageing. 2001;30(1):33-40. PubMed
21. Al-Rashed SA, Wright DJ, Roebuck N, et al. The value of inpatient pharmaceutical counselling to elderly patients prior to discharge. Br J Clin Pharmacol. 2002 Dec;54(6):657–64. PubMed
22. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006 Mar 13;166(5):565–71. PubMed
23. Walker PC, Bernstein SJ, Jones JN, et al. Impact of a pharmacist-facilitated hospital discharge program: a quasi-experimental study. Arch Intern Med. 2009 Nov 23;169(21):2003–10. PubMed
24. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551-557. PubMed
25. Normand ST, Landrum MB, Guadagnoli E, et al. Validating recommendations for coronary angiography following an acute myocardial infarction in the elderly: a matched analysis using propensity scores. J Clin Epidemiol. 2001;54(4):387-398. PubMed
26. Rosenbaum PR., Donald BR. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat. 1985;39(1):33-38.
27. Fernandes O, Shojania KG. Medication reconciliation in the hospital: what, why, where, when, who and how? Healthc Q. 2012;15(Special Issue):42-49. PubMed
28. Joynt KE, Jha AK. Thirty-day readmissions—truth and consequences. N Engl J Med. 2012;366(15):1366-1369. PubMed
29. Zed PJ, Abu-Laban RB, Balen RM, et al. Incidence, severity and preventability of medication-related visits to the emergency department: a prospective study. CMAJ. 2008;178(12):1563-1569. PubMed
30. Gruneir A, Dhalla IA, van Walraven C, et al. Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Med. 2011;5(2):e104-e111. PubMed
© 2018 Society of Hospital Medicine
Information Continuity on Outcomes
Hospitalists are common in North America.1, 2 Hospitalists have been associated with a range of beneficial outcomes including decreased length of stay.3, 4 A primary concern of the hospitalist model is its potential detrimental effect on continuity of care5 partly because patients are often not seen by their hospitalists after discharge.
Continuity of care6 is primarily composed of provider continuity (an ongoing relationship between a patient and a particular provider over time) and information continuity (availability of data from prior events for subsequent patient encounters).6 The association between continuity of care and patient outcomes has been quantified in many studies.720 However, the relationship of continuity and outcomes is especially relevant after discharge from the hospital since this is a time when patients have a high risk of poor patient outcomes21 and poor provider22 and information continuity.2325
The association between continuity and outcomes after hospital discharge has been directly quantified in 2 studies. One found that patients seen by a physician who treated them in the hospital had a significant adjusted relative risk reduction in 30‐day death or readmission of 5% and 3%, respectively.22 The other study found that patients discharged from a general medicine ward were less likely to be readmitted if they were seen by physicians who had access to their discharge summary.23 However, neither of these studies concurrently measured the influence of provider and information continuity on patient outcomes.
Determining whether and how continuity of care influences patient outcomes after hospital discharge is essential to improve health care in an evidence‐based fashion. In addition, the influence that hospital physician follow‐up has on patient outcomes can best be determined by measuring provider and information continuity in patients after hospital discharge. This study sought to measure the independent association of several provider and information continuity measures on death or urgent readmission after hospital discharge.
Methods
Study Design
This was a multicenter prospective cohort study of consecutive patients discharged to the community from the medical or surgical services of 11 Ontario hospitals (6 university‐affiliated hospitals and 5 community hospitals) in 5 cities after an elective or emergency hospitalization. Patients were invited to participate in the study if they were cognitively intact, had a telephone, and provided written informed consent. Patients were excluded if they were less than 18 years old, were discharged to nursing homes, or were not proficient in English and did not have someone to help communicate with study staff. Enrolled patients were excluded from the analysis if they had less than 2 physician visits prior to one of the study's outcomes or the end of patient observation (which was 6 months postdischarge). This final exclusion criterion was necessary since 2 continuity measures (including postdischarge physician continuity and postdischarge information continuity) were incalculable with less than 2 physician visits during follow‐up (Supporting information). The study was approved by the research ethics board of each participating hospital.
Data Collection
Prior to hospital discharge, patients were interviewed by study personnel to identify their baseline functional status, their living conditions, all physicians who regularly treated the patient prior to admission (including both family physicians and consultants), and chronic medical conditions. The latter were confirmed by a review of the patient's chart and hospital discharge summary, when available. Patients also provided principal contacts whom we could contact in the event patients could not be reached. The chart and discharge summary were also used to identify diagnoses in hospitalincluding complications (diagnoses arising in the hospital)and medications at discharge.
Patients or their designated contacts were telephoned 1, 3, and 6 months after hospital discharge to identify the date and the physician of all postdischarge physician visits. For each postdischarge physician visit, we determined whether the physician had access to a discharge summary for the index hospitalization. We also determined the availability of information from all previous postdischarge visits that the patient had with other physicians. The methods used to collect these data were previously detailed.26 Briefly, we used three complementary methods to elicit this information from each follow‐up physician. First, patients gave the physician a survey on which the physician listed all prior visits with other doctors for which they had information. If this survey was not returned, we faxed the survey to the physician. If the faxed survey was not returned, we telephoned the physician or their office staff and administered the survey over the telephone.
Continuity Measures
We measured components of both provider and information continuity. For the posthospitalization period, we measured provider continuity for physicians who had provided patient care during three distinct phases: the prehospital period; the hospital period; and the postdischarge period. Prehospital physicians were those classified by the patient as their regular physician(s) (defined as physiciansboth family physicians and consultantsthat they had seen in the past and were likely to see again in the future). Hospital provider continuity was divided into 2 components: hospital physician continuity (ie, the most responsible physician in the hospital); and hospital consultant continuity (ie, another physician who consulted on the patient during admission). Information continuity was divided into discharge summary continuity and postdischarge visit information continuity.
We quantified provider and information continuity using Breslau's Usual Provider of Continuity (UPC)27 measure. It is a widely used and validated continuity measure whose values are meaningful and interpretable.6 The UPC measures the proportion of visits with the physician of interest (for provider continuity) or the proportion of visits having the information of interest (for information continuity). The UPC was calculated as: $${\rm UPC} = {\rm n}_{\rm i} / {\rm N}$$
As the formulae in the supporting information suggest, all continuity measures were incalculable prior to the first postdischarge visit and all continuity measures changed value at each visit during patient observation. In addition, a particular physician visit could increase multiple continuity measures simultaneously. For example, a visit with a physician who was the hospital physician and who regularly treated the patient prior to the hospitalization would increase both hospital and prehospital provider continuity. If the patient had previously seen the physician after discharge, the visit would also increase postdischarge physician continuity.
Study Outcomes
Outcomes for the study included time to all‐cause death and time to all‐cause, urgent readmission. To be classified as urgent, readmissions could not be arranged when the patient was originally discharged from hospital or more than 4 weeks prior to the readmission. All hospital admissions meeting these criteria during the 6 month study period were labeled in this study as urgent readmissions even if they were unrelated to the index admission.
Principal contacts were called if we were unable to reach the patient to determine their outcomes. If the patient's vital status remained unclear, we contacted the Office of the Provincial Registrar to determine if and when the patient died during the 6 months after discharge from hospital.
Analysis
Outcome incidence densities and 95% confidence intervals [CIs] were calculated using PROC GENMOD in SAS to account for clustering of patients in hospitals. We used multivariate proportional hazards modeling to determine the independent association of provider and information continuity measures with time to death and time to urgent readmission. Patient observation started when patients were discharged from the hospital. Patient observation ended at the earliest of the following: death; urgent readmission to the hospital; end of follow‐up (which was 6 months after discharge from the hospital) or loss to follow‐up. Because hospital consultant continuity was very highly skewed (95.6% of patients had a value of 0; mean value of 0.016; skewness 6.9), it was not included in the primary regression models but was included in a sensitivity analysis.
To adjust for potential confounders in the association between continuity and the outcomes, our model included all factors that were independently associated with either the outcome or any continuity measure. Factors associated with death or urgent readmission were summarized using the LACE index.29 This index combines a patient's hospital length of stay, admission acuity, patient comorbidity (measured with the Charlson Score30 using updated disease category weights by Schneeweiss et al.),31 and emergency room utilization (measured as the number of visits in the 6 months prior to admission) into a single number ranging from 0 to 19. The LACE index was moderately discriminative and highly accurate at predicting 30‐day death or urgent readmission.29 In a separate study,28 we found that the following factors were independently associated with at least one of the continuity measures: patient age; patient sex; number of admissions in previous 6 months; number of regular treating physicians prior to admission; hospital service (medicine vs. surgery); and number of complications in the hospital (defined as new problems arising after admission to hospital). By including all factors that were independently associated with either the outcome or continuity, we controlled for all measured factors that could act as confounders in the association between continuity and outcomes. We accounted for the clustered study design by using conditional proportional hazards models that stratified by hospitals.32 Analytical details are given in the supporting information.
Results
Between October 2002 and July 2006, we enrolled 5035 patients from 11 hospitals (Figure 1). Of the 5035 patients, 274 (5.4%) had no follow up interview with study personnel. A total of 885 (17.6%) had fewer than 2 post discharge physician visits and were not included in the continuity analyses. This left 3876 patients for this analysis (77.0% of the original cohort), of which 3727 had complete follow up (96.1% of the study cohort). A total of 531 patients (10.6% of the original cohort) had incomplete follow‐up because: 342 (6.8%) were lost to follow‐up; 172 (3.4%) refused participation; and 24 (0.5%) were transferred into a nursing home during the first month of observation.
The 3876 study patients are described in Table 1. Overall, these people had a mean age of 62 and most commonly had no physical limitations. Almost a third of patients had been admitted to the hospital in the previous 6 months. A total of 7.6% of patients had no regular prehospital physician while 5.8% had more than one regular prehospital physician. Patients were evenly split between acute and elective admissions and 12% had a complication during their admission. They were discharged after a median of 4 days on a median of 4 medications.
| Factor | Value | Death or Urgent Readmission | All (n = 3876) | |
|---|---|---|---|---|
| No (n = 3491) | Yes (n = 385) | |||
| ||||
| Mean patient age, years (SD) | 61.59 16.16 | 67.70 15.53 | 62.19 16.20 | |
| Female (%) | 1838 (52.6) | 217 (56.4) | 2055 (53.0) | |
| Lives alone (%) | 791 (22.7) | 107 (27.8) | 898 (23.2) | |
| # activities of daily living requiring aids (%) | 0 | 3277 (93.9) | 354 (91.9) | 3631 (93.7) |
| 1 | 125 (3.6) | 20 (5.2) | 145 (3.7) | |
| >1 | 89 (2.5) | 11 (2.8) | 100 (2.8) | |
| # physicians who see patient regularly (%) | 0 | 241 (6.9) | 22 (5.7) | 263 (6.8) |
| 1 | 3060 (87.7) | 333 (86.5) | 3393 (87.5) | |
| 2 | 150 (4.3) | 21 (5.5) | 171 (4.4) | |
| >2 | 281 (8.0) | 31 (8.0) | 312 (8.0) | |
| # admissions in previous 6 months (%) | 0 | 2420 (69.3) | 222 (57.7) | 2642 (68.2) |
| 1 | 833 (23.9) | 103 (26.8) | 936 (24.1) | |
| >1 | 238 (6.8) | 60 (15.6) | 298 (7.7) | |
| Index hospitalization description | ||||
| Number of discharge medications (IQR) | 4 (2‐7) | 6 (3‐9) | 4 (2‐7) | |
| Admitted to medical service (%) | 1440 (41.2) | 231 (60.0) | 1671 (43.1) | |
| Acute diagnoses: | ||||
| CAD (%) | 238 (6.8) | 23 (6.0) | 261 (6.7) | |
| Neoplasm of unspecified nature (%) | 196 (5.6) | 35 (9.1) | 231 (6.0) | |
| Heart failure (%) | 127 (3.6) | 38 (9.9) | 165 (4.3) | |
| Acute procedures | ||||
| CABG (%) | 182 (5.2) | 14 (3.6) | 196 (5.1) | |
| Total knee arthoplasty (%) | 173 (5.0) | 10 (2.6) | 183 (4.7) | |
| Total hip arthroplasty (%) | 118 (3.4) | (0.5) | 120 (3.1) | |
| Complication during admission (%) | 403 (11.5) | 63 (16.4) | 466 (12.0) | |
| LACE index: mean (SD) | 8.0 (3.6) | 10.3 (3.8) | 8.2 (3.7) | |
| Length of stay in days: median (IQR) | 4 (2‐7) | 6 (3‐10) | 4 (2‐8) | |
| Acute/emergent admission (%) | 1851 (53.0) | 272 (70.6) | 2123 (54.8) | |
| Charlson score (%) | 0 | 2771 (79.4) | 241 (62.6) | 3012 (77.7) |
| 1 | 103 (3.0) | 17 (4.4) | 120 (3.1) | |
| 2 | 446 (12.8) | 86 (22.3) | 532 (13.7) | |
| >2 | 171 (4.9) | 41 (10.6) | 212 (5.5) | |
| Emergency room use (# visits/ year) (%) | 0 | 2342 (67.1) | 190 (49.4) | 2532 (65.3) |
| 1 | 761 (21.8) | 101 (26.2) | 862 (22.2) | |
| >1 | 388 (11.1) | 94 (24.4) | 482 (12.4) | |
Patients were observed in the study for a median of 175 days (interquartile range [IQR] 175‐178). During this time they had a median of 4 physician visits (IQR 3‐6). The first postdischarge physician visit occurred a median of 10 days (IQR 6‐18) after discharge from hospital.
Continuity Measures
Table 2 summarizes all continuity scores. Since continuity scores varied significantly over time,28 Table 2 provides continuity scores on the last day of patient observation. Preadmission provider, postdischarge provider, and discharge summary continuity all had similar values and distributions with median values ranging between 0.444 and 0.571. 1797 (46.4%) patients had a hospital physician provider continuity scorae of 0.
| Minimum | 25th Percentile | Median | 75th Percentile | Maximum | |
|---|---|---|---|---|---|
| Provider continuity | |||||
| A: Pre‐admission physician | 0 | 0.143 | 0.444 | 0.667 | 1.000 |
| B: Hospital physician | 0 | 0 | 0.143 | 0.400 | 1.000 |
| C: Post‐discharge physician | 0 | 0.333 | 0.571 | 0.750 | 1.000 |
| Information continuity | |||||
| D: Discharge summary | 0 | 0.095 | 0.500 | 0.800 | 1.000 |
| E: Post‐discharge information | 0 | 0 | 0.182 | 0.500 | 1.000 |
Study Outcomes
During a median of 175 days of observation, 45 patients died (event rate 2.6 events per 100 patient‐years observation [95% CI 2.0‐3.4]) and 340 patients were urgently readmitted (event rate 19.6 events per 100 patient‐years observation [95% CI 15.9‐24.3]). Figure 2 presents the survival curves for time to death and time to urgent readmission. The hazard of death was consistent through the observation period but the risk of urgent readmission decreased slightly after 90 days postdischarge.
Association Between Continuity and Outcomes
Table 3 summarizes the association between provider and information continuity with study outcomes. No continuity measure was associated with time to death by itself (Table 3, column A) or with the other continuity measures (Table 3, column B). Preadmission physician continuity was associated with a significantly decreased risk of urgent readmission. When the proportion of postdischarge visits with a prehospital physician increased by 10%, the adjusted risk of urgent readmission decreased by 6% (adjusted hazards ratio (adj‐HR)) of 0.94 (95% CI, 0.91‐0.98). None of the other continuity measuresincluding hospital physicianwere significantly associated with urgent readmission either by themselves (Table 3, column A) or after adjusting for other continuity measures (Table 3, column B).
| Outcome | ||||||||
|---|---|---|---|---|---|---|---|---|
| Death (95% CI) | Urgent Readmission (95% CI) | |||||||
| A: Adjusted for Other Confounders Only | B: Adjusted for Other Confounders and Continuity Measures | A: Adjusted for Other Confounders Only | B: Adjusted for Other Confounders and Continuity Measures | |||||
| ||||||||
| Provider continuity | ||||||||
| A: Pre‐admission physician | 1.03 | (0.95, 1.12) | 1.06 | (0.95, 1.18) | 0.95 | (0.92, 0.98) | 0.94 | (0.91, 0.98) |
| B: Hospital physician | 0.87 | (0.74, 1.02) | 0.86 | (0.70, 1.03) | 0.98 | (0.94, 1.02) | 0.97 | (0.92, 1.01) |
| C: Post‐discharge physician | 0.97 | (0.89, 1.06) | 0.93 | (0.84, 1.04) | 0.98 | (0.95, 1.01) | 0.98 | (0.94, 1.02) |
| Information continuity | ||||||||
| D: Discharge Summary | 0.96 | (0.89, 1.04) | 0.94 | (0.87, 1.03) | 1.01 | (0.98, 1.04) | 1.02 | (0.99, 1.05) |
| E: Post‐discharge information | 1.01 | (0.94, 1.08) | 1.03 | (0.95, 1.11) | 1.00 | (0.97, 1.03) | 1.03 | (0.95, 1.11) |
| Other confounders | ||||||||
| Patient age in decades* | 1.43 | (1.13, 1.82) | 1.18 | (1.10, 1.28) | ||||
| Female | 1.50 | (0.81, 2.77) | 1.16 | (0.94, 1.44) | ||||
| # physicians who see patient regularly | ||||||||
| 1 | 1.46 | (0.92, 2.34) | ||||||
| 2 | 2.17 | (1.11, 4.26) | ||||||
| >2 | 3.71 | (1.55, 8.88) | ||||||
| Complications during admission | ||||||||
| 1 | 1.38 | (0.61, 3.10) | 0.81 | (0.55, 1.17) | ||||
| >1 | 1.01 | (0.28, 3.58) | 0.91 | (0.56, 1.48) | ||||
| # admissions in previous 6 months | ||||||||
| 1 | 1.27 | (0.59, 2.70) | 1.34 | (1.02, 1.76) | ||||
| >1 | 1.42 | (0.55, 3.67) | 1.78 | (1.26, 2.51) | ||||
| LACE index* | 1.16 | (1.06, 1.26) | 1.10 | (1.07, 1.14) | ||||
Increased patient age and increased LACE index score were both strongly associated with an increased risk of death (adj‐HR 1.43 [1.13‐1.82] and 1.16 [1.06‐1.26], respectively) and urgent readmission (adj‐HR 1.18 [1.10‐1.28] and 1.10 [1.07‐1.14], respectively). Hospitalization in the 6 months prior to admission significantly increased the risk of urgent readmission but not death. The risk of urgent readmission increased significantly as the number of regular prehospital physicians increased.
Sensitivity Analyses
Our study conclusions did not change in the sensitivity analyses. The number of postdischarge physician visits (expressed as a time‐dependent covariate) was not associated with either death or with urgent readmission and preadmission physician continuity remained significantly associated with time to urgent readmission (supporting information). Adding consultant continuity to the model also did not change our results (supporting information). In‐hospital consultant continuity was associated with an increased risk of urgent readmission (adj‐HR 1.10, 95% CI, 1.01‐1.20). The association between pre‐admission physician continuity and time to urgent readmission did not interact significantly with patient age, LACE index score, or number of previous admissions.
Discussion
This large, prospective cohort study measured the independent association of several provider and information continuity measures with important outcomes in patients discharged from hospital. After adjusting for potential confounders, we found that increased continuity with physicians who regularly cared for the patient prior to the admission was significantly and independently associated with a decreased risk of urgent readmission. Our data suggest that continuity with the hospital physician did not independently influence the risk of patient death or urgent readmission after discharge.
Although hospital physician continuity did not significantly change patient outcomes, we found that follow‐up with a physician who regularly treated the patient prior to their admission was associated with a significantly decreased risk of urgent readmission. This could reflect the important role that a patient's regular physician plays in their health care. Other studies have shown a positive association between continuity with a regular physician and improved outcomes including decreased emergency room utilization7, 8 and decreased hospitalization.10, 11
We were somewhat disappointed that information continuity was not independently associated with improved patient outcomes. Information continuity is likely more amenable to modification than is provider continuity. Of course, our study findings do not mean that information continuity does not improve patient outcomes, as in other studies.23, 33 Instead, our results could reflect that we solely measured the availability of information to physicians. Future studies that measure the quality, relevance, and actual utilization of patient information will be better able to discern the influence of information continuity on patient outcomes.
We believe that our study was methodologically strong and unique. We captured both provider and information continuity in a large group of representative patients using a broad range of measures that captured continuity's diverse components including both provider and information continuity. The continuity measures were expressed and properly analyzed as time‐dependent variables in a multivariate model.34 Our analysis controlled for important potential confounders. Our follow‐up and data collection was rigorous with 96.1% of our study group having complete follow‐up. Finally, the analysis used multiple imputation to appropriately handle missing data in the one incomplete variable (post‐discharge information continuity).3537
Several limitations of our study should be kept in mind. We are uncertain how our results might generalize to patients discharged from obstetrical or psychiatric services or people in other health systems. Our analysis had to exclude patients with less than two physician visits after discharge since this was the minimum required to calculate postdischarge physician and information continuity. Data collection for postdischarge information continuity was incomplete with data missing for 19.0% of all 15 401 visits in the original cohort.38 However, a response rate of 81.0% is very good39 when compared to other survey‐based studies40 and we accounted for the missing data using multiple imputation methods. The primary outcomes of our studytime to death or urgent readmissionmay be relatively insensitive to modification of quality of care, which is presumably improved by increased continuity.41 For example, Clarke found that the majority of readmissions in all patient groups were unavoidable with 94% of medical readmissions 1 month postdischarge judged to be unavoidable.42 Future studies regarding the effects of continuity could focus on its association with other outcomes that are more reflective of quality of care such as the risk of adverse events or medical error.21 Such outcomes would presumably be more sensitive to improved quality of care from increased continuity.
We believe that our study's major limitation was its inability to establish a causal association between continuity and patient outcomes. Our finding that increased consultant continuity was associated with an increased risk of poor outcomes highlights this concern. Presumably, patient follow‐up with a hospital consultant indicates a disease status with a high risk of bad patient outcomesa risk that is not entirely accounted for by the covariates used in this study. If we accept that unresolved confounding explains this association, the same could also apply to the association between preadmission physician continuity and improved outcomes. Perhaps patients who are doing well after discharge from hospital are able to return to their regular physician. Our analysis would therefore identify an association between increased preadmission physician continuity and improved patient outcomes. Analyses could also incorporate more discriminative measures of severity of hospital illness, such as those developed by Escobar et al.43 Since patients may experience health events after their discharge from hospital that could influence outcomes, recording these and expressing them in the study model as time‐dependent covariates will be important. Finally, similar to the classic study by Wasson et al.44 in 1984, a proper randomized trial that measures the effect of a continuity‐building intervention on both continuity of care and patient outcomes would help determine how continuity influences outcomes.
In conclusion, after discharge from hospital, increased continuity with physicians who routinely care for the patient is significantly and independently associated with a decreased risk of urgent readmission. Continuity with the hospital physician after discharge did not independently influence the risk of patient death or urgent readmission in our study. Further research is required to determine the causal association between preadmission physician continuity and improved outcomes. Until that time, clinicians should strive to optimize continuity with physicians their patients have seen prior to the hospitalization.
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- ,,.The impact of insurance type and forced discontinuity on the delivery of primary care. [see comments.].J Fam Pract.1997;45:129–135.
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- ,,,.Continuity of care and cardiovascular risk factor management: does care by a single clinician add to informational continuity provided by electronic medical records?Am J Manag Care.2005;11:689–696.
- ,,,,.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161–167.
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- ,,,.Effect of discharge summary availability during post‐discharge visits on hospital readmission.J Gen Intern Med.2002;17:186–192.
- ,,, et al.Association of communication between hospital‐based physicians and primary care providers with patient outcomes.[see comment].J Gen Intern Med2009;24(3):381–386.
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- ,,, et al.Derivation and validation of the LACE index to predict early death or unplanned readmission after discharge from hospital to the community.CMAJ. (In press)
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Hospitalists are common in North America.1, 2 Hospitalists have been associated with a range of beneficial outcomes including decreased length of stay.3, 4 A primary concern of the hospitalist model is its potential detrimental effect on continuity of care5 partly because patients are often not seen by their hospitalists after discharge.
Continuity of care6 is primarily composed of provider continuity (an ongoing relationship between a patient and a particular provider over time) and information continuity (availability of data from prior events for subsequent patient encounters).6 The association between continuity of care and patient outcomes has been quantified in many studies.720 However, the relationship of continuity and outcomes is especially relevant after discharge from the hospital since this is a time when patients have a high risk of poor patient outcomes21 and poor provider22 and information continuity.2325
The association between continuity and outcomes after hospital discharge has been directly quantified in 2 studies. One found that patients seen by a physician who treated them in the hospital had a significant adjusted relative risk reduction in 30‐day death or readmission of 5% and 3%, respectively.22 The other study found that patients discharged from a general medicine ward were less likely to be readmitted if they were seen by physicians who had access to their discharge summary.23 However, neither of these studies concurrently measured the influence of provider and information continuity on patient outcomes.
Determining whether and how continuity of care influences patient outcomes after hospital discharge is essential to improve health care in an evidence‐based fashion. In addition, the influence that hospital physician follow‐up has on patient outcomes can best be determined by measuring provider and information continuity in patients after hospital discharge. This study sought to measure the independent association of several provider and information continuity measures on death or urgent readmission after hospital discharge.
Methods
Study Design
This was a multicenter prospective cohort study of consecutive patients discharged to the community from the medical or surgical services of 11 Ontario hospitals (6 university‐affiliated hospitals and 5 community hospitals) in 5 cities after an elective or emergency hospitalization. Patients were invited to participate in the study if they were cognitively intact, had a telephone, and provided written informed consent. Patients were excluded if they were less than 18 years old, were discharged to nursing homes, or were not proficient in English and did not have someone to help communicate with study staff. Enrolled patients were excluded from the analysis if they had less than 2 physician visits prior to one of the study's outcomes or the end of patient observation (which was 6 months postdischarge). This final exclusion criterion was necessary since 2 continuity measures (including postdischarge physician continuity and postdischarge information continuity) were incalculable with less than 2 physician visits during follow‐up (Supporting information). The study was approved by the research ethics board of each participating hospital.
Data Collection
Prior to hospital discharge, patients were interviewed by study personnel to identify their baseline functional status, their living conditions, all physicians who regularly treated the patient prior to admission (including both family physicians and consultants), and chronic medical conditions. The latter were confirmed by a review of the patient's chart and hospital discharge summary, when available. Patients also provided principal contacts whom we could contact in the event patients could not be reached. The chart and discharge summary were also used to identify diagnoses in hospitalincluding complications (diagnoses arising in the hospital)and medications at discharge.
Patients or their designated contacts were telephoned 1, 3, and 6 months after hospital discharge to identify the date and the physician of all postdischarge physician visits. For each postdischarge physician visit, we determined whether the physician had access to a discharge summary for the index hospitalization. We also determined the availability of information from all previous postdischarge visits that the patient had with other physicians. The methods used to collect these data were previously detailed.26 Briefly, we used three complementary methods to elicit this information from each follow‐up physician. First, patients gave the physician a survey on which the physician listed all prior visits with other doctors for which they had information. If this survey was not returned, we faxed the survey to the physician. If the faxed survey was not returned, we telephoned the physician or their office staff and administered the survey over the telephone.
Continuity Measures
We measured components of both provider and information continuity. For the posthospitalization period, we measured provider continuity for physicians who had provided patient care during three distinct phases: the prehospital period; the hospital period; and the postdischarge period. Prehospital physicians were those classified by the patient as their regular physician(s) (defined as physiciansboth family physicians and consultantsthat they had seen in the past and were likely to see again in the future). Hospital provider continuity was divided into 2 components: hospital physician continuity (ie, the most responsible physician in the hospital); and hospital consultant continuity (ie, another physician who consulted on the patient during admission). Information continuity was divided into discharge summary continuity and postdischarge visit information continuity.
We quantified provider and information continuity using Breslau's Usual Provider of Continuity (UPC)27 measure. It is a widely used and validated continuity measure whose values are meaningful and interpretable.6 The UPC measures the proportion of visits with the physician of interest (for provider continuity) or the proportion of visits having the information of interest (for information continuity). The UPC was calculated as: $${\rm UPC} = {\rm n}_{\rm i} / {\rm N}$$
As the formulae in the supporting information suggest, all continuity measures were incalculable prior to the first postdischarge visit and all continuity measures changed value at each visit during patient observation. In addition, a particular physician visit could increase multiple continuity measures simultaneously. For example, a visit with a physician who was the hospital physician and who regularly treated the patient prior to the hospitalization would increase both hospital and prehospital provider continuity. If the patient had previously seen the physician after discharge, the visit would also increase postdischarge physician continuity.
Study Outcomes
Outcomes for the study included time to all‐cause death and time to all‐cause, urgent readmission. To be classified as urgent, readmissions could not be arranged when the patient was originally discharged from hospital or more than 4 weeks prior to the readmission. All hospital admissions meeting these criteria during the 6 month study period were labeled in this study as urgent readmissions even if they were unrelated to the index admission.
Principal contacts were called if we were unable to reach the patient to determine their outcomes. If the patient's vital status remained unclear, we contacted the Office of the Provincial Registrar to determine if and when the patient died during the 6 months after discharge from hospital.
Analysis
Outcome incidence densities and 95% confidence intervals [CIs] were calculated using PROC GENMOD in SAS to account for clustering of patients in hospitals. We used multivariate proportional hazards modeling to determine the independent association of provider and information continuity measures with time to death and time to urgent readmission. Patient observation started when patients were discharged from the hospital. Patient observation ended at the earliest of the following: death; urgent readmission to the hospital; end of follow‐up (which was 6 months after discharge from the hospital) or loss to follow‐up. Because hospital consultant continuity was very highly skewed (95.6% of patients had a value of 0; mean value of 0.016; skewness 6.9), it was not included in the primary regression models but was included in a sensitivity analysis.
To adjust for potential confounders in the association between continuity and the outcomes, our model included all factors that were independently associated with either the outcome or any continuity measure. Factors associated with death or urgent readmission were summarized using the LACE index.29 This index combines a patient's hospital length of stay, admission acuity, patient comorbidity (measured with the Charlson Score30 using updated disease category weights by Schneeweiss et al.),31 and emergency room utilization (measured as the number of visits in the 6 months prior to admission) into a single number ranging from 0 to 19. The LACE index was moderately discriminative and highly accurate at predicting 30‐day death or urgent readmission.29 In a separate study,28 we found that the following factors were independently associated with at least one of the continuity measures: patient age; patient sex; number of admissions in previous 6 months; number of regular treating physicians prior to admission; hospital service (medicine vs. surgery); and number of complications in the hospital (defined as new problems arising after admission to hospital). By including all factors that were independently associated with either the outcome or continuity, we controlled for all measured factors that could act as confounders in the association between continuity and outcomes. We accounted for the clustered study design by using conditional proportional hazards models that stratified by hospitals.32 Analytical details are given in the supporting information.
Results
Between October 2002 and July 2006, we enrolled 5035 patients from 11 hospitals (Figure 1). Of the 5035 patients, 274 (5.4%) had no follow up interview with study personnel. A total of 885 (17.6%) had fewer than 2 post discharge physician visits and were not included in the continuity analyses. This left 3876 patients for this analysis (77.0% of the original cohort), of which 3727 had complete follow up (96.1% of the study cohort). A total of 531 patients (10.6% of the original cohort) had incomplete follow‐up because: 342 (6.8%) were lost to follow‐up; 172 (3.4%) refused participation; and 24 (0.5%) were transferred into a nursing home during the first month of observation.
The 3876 study patients are described in Table 1. Overall, these people had a mean age of 62 and most commonly had no physical limitations. Almost a third of patients had been admitted to the hospital in the previous 6 months. A total of 7.6% of patients had no regular prehospital physician while 5.8% had more than one regular prehospital physician. Patients were evenly split between acute and elective admissions and 12% had a complication during their admission. They were discharged after a median of 4 days on a median of 4 medications.
| Factor | Value | Death or Urgent Readmission | All (n = 3876) | |
|---|---|---|---|---|
| No (n = 3491) | Yes (n = 385) | |||
| ||||
| Mean patient age, years (SD) | 61.59 16.16 | 67.70 15.53 | 62.19 16.20 | |
| Female (%) | 1838 (52.6) | 217 (56.4) | 2055 (53.0) | |
| Lives alone (%) | 791 (22.7) | 107 (27.8) | 898 (23.2) | |
| # activities of daily living requiring aids (%) | 0 | 3277 (93.9) | 354 (91.9) | 3631 (93.7) |
| 1 | 125 (3.6) | 20 (5.2) | 145 (3.7) | |
| >1 | 89 (2.5) | 11 (2.8) | 100 (2.8) | |
| # physicians who see patient regularly (%) | 0 | 241 (6.9) | 22 (5.7) | 263 (6.8) |
| 1 | 3060 (87.7) | 333 (86.5) | 3393 (87.5) | |
| 2 | 150 (4.3) | 21 (5.5) | 171 (4.4) | |
| >2 | 281 (8.0) | 31 (8.0) | 312 (8.0) | |
| # admissions in previous 6 months (%) | 0 | 2420 (69.3) | 222 (57.7) | 2642 (68.2) |
| 1 | 833 (23.9) | 103 (26.8) | 936 (24.1) | |
| >1 | 238 (6.8) | 60 (15.6) | 298 (7.7) | |
| Index hospitalization description | ||||
| Number of discharge medications (IQR) | 4 (2‐7) | 6 (3‐9) | 4 (2‐7) | |
| Admitted to medical service (%) | 1440 (41.2) | 231 (60.0) | 1671 (43.1) | |
| Acute diagnoses: | ||||
| CAD (%) | 238 (6.8) | 23 (6.0) | 261 (6.7) | |
| Neoplasm of unspecified nature (%) | 196 (5.6) | 35 (9.1) | 231 (6.0) | |
| Heart failure (%) | 127 (3.6) | 38 (9.9) | 165 (4.3) | |
| Acute procedures | ||||
| CABG (%) | 182 (5.2) | 14 (3.6) | 196 (5.1) | |
| Total knee arthoplasty (%) | 173 (5.0) | 10 (2.6) | 183 (4.7) | |
| Total hip arthroplasty (%) | 118 (3.4) | (0.5) | 120 (3.1) | |
| Complication during admission (%) | 403 (11.5) | 63 (16.4) | 466 (12.0) | |
| LACE index: mean (SD) | 8.0 (3.6) | 10.3 (3.8) | 8.2 (3.7) | |
| Length of stay in days: median (IQR) | 4 (2‐7) | 6 (3‐10) | 4 (2‐8) | |
| Acute/emergent admission (%) | 1851 (53.0) | 272 (70.6) | 2123 (54.8) | |
| Charlson score (%) | 0 | 2771 (79.4) | 241 (62.6) | 3012 (77.7) |
| 1 | 103 (3.0) | 17 (4.4) | 120 (3.1) | |
| 2 | 446 (12.8) | 86 (22.3) | 532 (13.7) | |
| >2 | 171 (4.9) | 41 (10.6) | 212 (5.5) | |
| Emergency room use (# visits/ year) (%) | 0 | 2342 (67.1) | 190 (49.4) | 2532 (65.3) |
| 1 | 761 (21.8) | 101 (26.2) | 862 (22.2) | |
| >1 | 388 (11.1) | 94 (24.4) | 482 (12.4) | |
Patients were observed in the study for a median of 175 days (interquartile range [IQR] 175‐178). During this time they had a median of 4 physician visits (IQR 3‐6). The first postdischarge physician visit occurred a median of 10 days (IQR 6‐18) after discharge from hospital.
Continuity Measures
Table 2 summarizes all continuity scores. Since continuity scores varied significantly over time,28 Table 2 provides continuity scores on the last day of patient observation. Preadmission provider, postdischarge provider, and discharge summary continuity all had similar values and distributions with median values ranging between 0.444 and 0.571. 1797 (46.4%) patients had a hospital physician provider continuity scorae of 0.
| Minimum | 25th Percentile | Median | 75th Percentile | Maximum | |
|---|---|---|---|---|---|
| Provider continuity | |||||
| A: Pre‐admission physician | 0 | 0.143 | 0.444 | 0.667 | 1.000 |
| B: Hospital physician | 0 | 0 | 0.143 | 0.400 | 1.000 |
| C: Post‐discharge physician | 0 | 0.333 | 0.571 | 0.750 | 1.000 |
| Information continuity | |||||
| D: Discharge summary | 0 | 0.095 | 0.500 | 0.800 | 1.000 |
| E: Post‐discharge information | 0 | 0 | 0.182 | 0.500 | 1.000 |
Study Outcomes
During a median of 175 days of observation, 45 patients died (event rate 2.6 events per 100 patient‐years observation [95% CI 2.0‐3.4]) and 340 patients were urgently readmitted (event rate 19.6 events per 100 patient‐years observation [95% CI 15.9‐24.3]). Figure 2 presents the survival curves for time to death and time to urgent readmission. The hazard of death was consistent through the observation period but the risk of urgent readmission decreased slightly after 90 days postdischarge.
Association Between Continuity and Outcomes
Table 3 summarizes the association between provider and information continuity with study outcomes. No continuity measure was associated with time to death by itself (Table 3, column A) or with the other continuity measures (Table 3, column B). Preadmission physician continuity was associated with a significantly decreased risk of urgent readmission. When the proportion of postdischarge visits with a prehospital physician increased by 10%, the adjusted risk of urgent readmission decreased by 6% (adjusted hazards ratio (adj‐HR)) of 0.94 (95% CI, 0.91‐0.98). None of the other continuity measuresincluding hospital physicianwere significantly associated with urgent readmission either by themselves (Table 3, column A) or after adjusting for other continuity measures (Table 3, column B).
| Outcome | ||||||||
|---|---|---|---|---|---|---|---|---|
| Death (95% CI) | Urgent Readmission (95% CI) | |||||||
| A: Adjusted for Other Confounders Only | B: Adjusted for Other Confounders and Continuity Measures | A: Adjusted for Other Confounders Only | B: Adjusted for Other Confounders and Continuity Measures | |||||
| ||||||||
| Provider continuity | ||||||||
| A: Pre‐admission physician | 1.03 | (0.95, 1.12) | 1.06 | (0.95, 1.18) | 0.95 | (0.92, 0.98) | 0.94 | (0.91, 0.98) |
| B: Hospital physician | 0.87 | (0.74, 1.02) | 0.86 | (0.70, 1.03) | 0.98 | (0.94, 1.02) | 0.97 | (0.92, 1.01) |
| C: Post‐discharge physician | 0.97 | (0.89, 1.06) | 0.93 | (0.84, 1.04) | 0.98 | (0.95, 1.01) | 0.98 | (0.94, 1.02) |
| Information continuity | ||||||||
| D: Discharge Summary | 0.96 | (0.89, 1.04) | 0.94 | (0.87, 1.03) | 1.01 | (0.98, 1.04) | 1.02 | (0.99, 1.05) |
| E: Post‐discharge information | 1.01 | (0.94, 1.08) | 1.03 | (0.95, 1.11) | 1.00 | (0.97, 1.03) | 1.03 | (0.95, 1.11) |
| Other confounders | ||||||||
| Patient age in decades* | 1.43 | (1.13, 1.82) | 1.18 | (1.10, 1.28) | ||||
| Female | 1.50 | (0.81, 2.77) | 1.16 | (0.94, 1.44) | ||||
| # physicians who see patient regularly | ||||||||
| 1 | 1.46 | (0.92, 2.34) | ||||||
| 2 | 2.17 | (1.11, 4.26) | ||||||
| >2 | 3.71 | (1.55, 8.88) | ||||||
| Complications during admission | ||||||||
| 1 | 1.38 | (0.61, 3.10) | 0.81 | (0.55, 1.17) | ||||
| >1 | 1.01 | (0.28, 3.58) | 0.91 | (0.56, 1.48) | ||||
| # admissions in previous 6 months | ||||||||
| 1 | 1.27 | (0.59, 2.70) | 1.34 | (1.02, 1.76) | ||||
| >1 | 1.42 | (0.55, 3.67) | 1.78 | (1.26, 2.51) | ||||
| LACE index* | 1.16 | (1.06, 1.26) | 1.10 | (1.07, 1.14) | ||||
Increased patient age and increased LACE index score were both strongly associated with an increased risk of death (adj‐HR 1.43 [1.13‐1.82] and 1.16 [1.06‐1.26], respectively) and urgent readmission (adj‐HR 1.18 [1.10‐1.28] and 1.10 [1.07‐1.14], respectively). Hospitalization in the 6 months prior to admission significantly increased the risk of urgent readmission but not death. The risk of urgent readmission increased significantly as the number of regular prehospital physicians increased.
Sensitivity Analyses
Our study conclusions did not change in the sensitivity analyses. The number of postdischarge physician visits (expressed as a time‐dependent covariate) was not associated with either death or with urgent readmission and preadmission physician continuity remained significantly associated with time to urgent readmission (supporting information). Adding consultant continuity to the model also did not change our results (supporting information). In‐hospital consultant continuity was associated with an increased risk of urgent readmission (adj‐HR 1.10, 95% CI, 1.01‐1.20). The association between pre‐admission physician continuity and time to urgent readmission did not interact significantly with patient age, LACE index score, or number of previous admissions.
Discussion
This large, prospective cohort study measured the independent association of several provider and information continuity measures with important outcomes in patients discharged from hospital. After adjusting for potential confounders, we found that increased continuity with physicians who regularly cared for the patient prior to the admission was significantly and independently associated with a decreased risk of urgent readmission. Our data suggest that continuity with the hospital physician did not independently influence the risk of patient death or urgent readmission after discharge.
Although hospital physician continuity did not significantly change patient outcomes, we found that follow‐up with a physician who regularly treated the patient prior to their admission was associated with a significantly decreased risk of urgent readmission. This could reflect the important role that a patient's regular physician plays in their health care. Other studies have shown a positive association between continuity with a regular physician and improved outcomes including decreased emergency room utilization7, 8 and decreased hospitalization.10, 11
We were somewhat disappointed that information continuity was not independently associated with improved patient outcomes. Information continuity is likely more amenable to modification than is provider continuity. Of course, our study findings do not mean that information continuity does not improve patient outcomes, as in other studies.23, 33 Instead, our results could reflect that we solely measured the availability of information to physicians. Future studies that measure the quality, relevance, and actual utilization of patient information will be better able to discern the influence of information continuity on patient outcomes.
We believe that our study was methodologically strong and unique. We captured both provider and information continuity in a large group of representative patients using a broad range of measures that captured continuity's diverse components including both provider and information continuity. The continuity measures were expressed and properly analyzed as time‐dependent variables in a multivariate model.34 Our analysis controlled for important potential confounders. Our follow‐up and data collection was rigorous with 96.1% of our study group having complete follow‐up. Finally, the analysis used multiple imputation to appropriately handle missing data in the one incomplete variable (post‐discharge information continuity).3537
Several limitations of our study should be kept in mind. We are uncertain how our results might generalize to patients discharged from obstetrical or psychiatric services or people in other health systems. Our analysis had to exclude patients with less than two physician visits after discharge since this was the minimum required to calculate postdischarge physician and information continuity. Data collection for postdischarge information continuity was incomplete with data missing for 19.0% of all 15 401 visits in the original cohort.38 However, a response rate of 81.0% is very good39 when compared to other survey‐based studies40 and we accounted for the missing data using multiple imputation methods. The primary outcomes of our studytime to death or urgent readmissionmay be relatively insensitive to modification of quality of care, which is presumably improved by increased continuity.41 For example, Clarke found that the majority of readmissions in all patient groups were unavoidable with 94% of medical readmissions 1 month postdischarge judged to be unavoidable.42 Future studies regarding the effects of continuity could focus on its association with other outcomes that are more reflective of quality of care such as the risk of adverse events or medical error.21 Such outcomes would presumably be more sensitive to improved quality of care from increased continuity.
We believe that our study's major limitation was its inability to establish a causal association between continuity and patient outcomes. Our finding that increased consultant continuity was associated with an increased risk of poor outcomes highlights this concern. Presumably, patient follow‐up with a hospital consultant indicates a disease status with a high risk of bad patient outcomesa risk that is not entirely accounted for by the covariates used in this study. If we accept that unresolved confounding explains this association, the same could also apply to the association between preadmission physician continuity and improved outcomes. Perhaps patients who are doing well after discharge from hospital are able to return to their regular physician. Our analysis would therefore identify an association between increased preadmission physician continuity and improved patient outcomes. Analyses could also incorporate more discriminative measures of severity of hospital illness, such as those developed by Escobar et al.43 Since patients may experience health events after their discharge from hospital that could influence outcomes, recording these and expressing them in the study model as time‐dependent covariates will be important. Finally, similar to the classic study by Wasson et al.44 in 1984, a proper randomized trial that measures the effect of a continuity‐building intervention on both continuity of care and patient outcomes would help determine how continuity influences outcomes.
In conclusion, after discharge from hospital, increased continuity with physicians who routinely care for the patient is significantly and independently associated with a decreased risk of urgent readmission. Continuity with the hospital physician after discharge did not independently influence the risk of patient death or urgent readmission in our study. Further research is required to determine the causal association between preadmission physician continuity and improved outcomes. Until that time, clinicians should strive to optimize continuity with physicians their patients have seen prior to the hospitalization.
Hospitalists are common in North America.1, 2 Hospitalists have been associated with a range of beneficial outcomes including decreased length of stay.3, 4 A primary concern of the hospitalist model is its potential detrimental effect on continuity of care5 partly because patients are often not seen by their hospitalists after discharge.
Continuity of care6 is primarily composed of provider continuity (an ongoing relationship between a patient and a particular provider over time) and information continuity (availability of data from prior events for subsequent patient encounters).6 The association between continuity of care and patient outcomes has been quantified in many studies.720 However, the relationship of continuity and outcomes is especially relevant after discharge from the hospital since this is a time when patients have a high risk of poor patient outcomes21 and poor provider22 and information continuity.2325
The association between continuity and outcomes after hospital discharge has been directly quantified in 2 studies. One found that patients seen by a physician who treated them in the hospital had a significant adjusted relative risk reduction in 30‐day death or readmission of 5% and 3%, respectively.22 The other study found that patients discharged from a general medicine ward were less likely to be readmitted if they were seen by physicians who had access to their discharge summary.23 However, neither of these studies concurrently measured the influence of provider and information continuity on patient outcomes.
Determining whether and how continuity of care influences patient outcomes after hospital discharge is essential to improve health care in an evidence‐based fashion. In addition, the influence that hospital physician follow‐up has on patient outcomes can best be determined by measuring provider and information continuity in patients after hospital discharge. This study sought to measure the independent association of several provider and information continuity measures on death or urgent readmission after hospital discharge.
Methods
Study Design
This was a multicenter prospective cohort study of consecutive patients discharged to the community from the medical or surgical services of 11 Ontario hospitals (6 university‐affiliated hospitals and 5 community hospitals) in 5 cities after an elective or emergency hospitalization. Patients were invited to participate in the study if they were cognitively intact, had a telephone, and provided written informed consent. Patients were excluded if they were less than 18 years old, were discharged to nursing homes, or were not proficient in English and did not have someone to help communicate with study staff. Enrolled patients were excluded from the analysis if they had less than 2 physician visits prior to one of the study's outcomes or the end of patient observation (which was 6 months postdischarge). This final exclusion criterion was necessary since 2 continuity measures (including postdischarge physician continuity and postdischarge information continuity) were incalculable with less than 2 physician visits during follow‐up (Supporting information). The study was approved by the research ethics board of each participating hospital.
Data Collection
Prior to hospital discharge, patients were interviewed by study personnel to identify their baseline functional status, their living conditions, all physicians who regularly treated the patient prior to admission (including both family physicians and consultants), and chronic medical conditions. The latter were confirmed by a review of the patient's chart and hospital discharge summary, when available. Patients also provided principal contacts whom we could contact in the event patients could not be reached. The chart and discharge summary were also used to identify diagnoses in hospitalincluding complications (diagnoses arising in the hospital)and medications at discharge.
Patients or their designated contacts were telephoned 1, 3, and 6 months after hospital discharge to identify the date and the physician of all postdischarge physician visits. For each postdischarge physician visit, we determined whether the physician had access to a discharge summary for the index hospitalization. We also determined the availability of information from all previous postdischarge visits that the patient had with other physicians. The methods used to collect these data were previously detailed.26 Briefly, we used three complementary methods to elicit this information from each follow‐up physician. First, patients gave the physician a survey on which the physician listed all prior visits with other doctors for which they had information. If this survey was not returned, we faxed the survey to the physician. If the faxed survey was not returned, we telephoned the physician or their office staff and administered the survey over the telephone.
Continuity Measures
We measured components of both provider and information continuity. For the posthospitalization period, we measured provider continuity for physicians who had provided patient care during three distinct phases: the prehospital period; the hospital period; and the postdischarge period. Prehospital physicians were those classified by the patient as their regular physician(s) (defined as physiciansboth family physicians and consultantsthat they had seen in the past and were likely to see again in the future). Hospital provider continuity was divided into 2 components: hospital physician continuity (ie, the most responsible physician in the hospital); and hospital consultant continuity (ie, another physician who consulted on the patient during admission). Information continuity was divided into discharge summary continuity and postdischarge visit information continuity.
We quantified provider and information continuity using Breslau's Usual Provider of Continuity (UPC)27 measure. It is a widely used and validated continuity measure whose values are meaningful and interpretable.6 The UPC measures the proportion of visits with the physician of interest (for provider continuity) or the proportion of visits having the information of interest (for information continuity). The UPC was calculated as: $${\rm UPC} = {\rm n}_{\rm i} / {\rm N}$$
As the formulae in the supporting information suggest, all continuity measures were incalculable prior to the first postdischarge visit and all continuity measures changed value at each visit during patient observation. In addition, a particular physician visit could increase multiple continuity measures simultaneously. For example, a visit with a physician who was the hospital physician and who regularly treated the patient prior to the hospitalization would increase both hospital and prehospital provider continuity. If the patient had previously seen the physician after discharge, the visit would also increase postdischarge physician continuity.
Study Outcomes
Outcomes for the study included time to all‐cause death and time to all‐cause, urgent readmission. To be classified as urgent, readmissions could not be arranged when the patient was originally discharged from hospital or more than 4 weeks prior to the readmission. All hospital admissions meeting these criteria during the 6 month study period were labeled in this study as urgent readmissions even if they were unrelated to the index admission.
Principal contacts were called if we were unable to reach the patient to determine their outcomes. If the patient's vital status remained unclear, we contacted the Office of the Provincial Registrar to determine if and when the patient died during the 6 months after discharge from hospital.
Analysis
Outcome incidence densities and 95% confidence intervals [CIs] were calculated using PROC GENMOD in SAS to account for clustering of patients in hospitals. We used multivariate proportional hazards modeling to determine the independent association of provider and information continuity measures with time to death and time to urgent readmission. Patient observation started when patients were discharged from the hospital. Patient observation ended at the earliest of the following: death; urgent readmission to the hospital; end of follow‐up (which was 6 months after discharge from the hospital) or loss to follow‐up. Because hospital consultant continuity was very highly skewed (95.6% of patients had a value of 0; mean value of 0.016; skewness 6.9), it was not included in the primary regression models but was included in a sensitivity analysis.
To adjust for potential confounders in the association between continuity and the outcomes, our model included all factors that were independently associated with either the outcome or any continuity measure. Factors associated with death or urgent readmission were summarized using the LACE index.29 This index combines a patient's hospital length of stay, admission acuity, patient comorbidity (measured with the Charlson Score30 using updated disease category weights by Schneeweiss et al.),31 and emergency room utilization (measured as the number of visits in the 6 months prior to admission) into a single number ranging from 0 to 19. The LACE index was moderately discriminative and highly accurate at predicting 30‐day death or urgent readmission.29 In a separate study,28 we found that the following factors were independently associated with at least one of the continuity measures: patient age; patient sex; number of admissions in previous 6 months; number of regular treating physicians prior to admission; hospital service (medicine vs. surgery); and number of complications in the hospital (defined as new problems arising after admission to hospital). By including all factors that were independently associated with either the outcome or continuity, we controlled for all measured factors that could act as confounders in the association between continuity and outcomes. We accounted for the clustered study design by using conditional proportional hazards models that stratified by hospitals.32 Analytical details are given in the supporting information.
Results
Between October 2002 and July 2006, we enrolled 5035 patients from 11 hospitals (Figure 1). Of the 5035 patients, 274 (5.4%) had no follow up interview with study personnel. A total of 885 (17.6%) had fewer than 2 post discharge physician visits and were not included in the continuity analyses. This left 3876 patients for this analysis (77.0% of the original cohort), of which 3727 had complete follow up (96.1% of the study cohort). A total of 531 patients (10.6% of the original cohort) had incomplete follow‐up because: 342 (6.8%) were lost to follow‐up; 172 (3.4%) refused participation; and 24 (0.5%) were transferred into a nursing home during the first month of observation.
The 3876 study patients are described in Table 1. Overall, these people had a mean age of 62 and most commonly had no physical limitations. Almost a third of patients had been admitted to the hospital in the previous 6 months. A total of 7.6% of patients had no regular prehospital physician while 5.8% had more than one regular prehospital physician. Patients were evenly split between acute and elective admissions and 12% had a complication during their admission. They were discharged after a median of 4 days on a median of 4 medications.
| Factor | Value | Death or Urgent Readmission | All (n = 3876) | |
|---|---|---|---|---|
| No (n = 3491) | Yes (n = 385) | |||
| ||||
| Mean patient age, years (SD) | 61.59 16.16 | 67.70 15.53 | 62.19 16.20 | |
| Female (%) | 1838 (52.6) | 217 (56.4) | 2055 (53.0) | |
| Lives alone (%) | 791 (22.7) | 107 (27.8) | 898 (23.2) | |
| # activities of daily living requiring aids (%) | 0 | 3277 (93.9) | 354 (91.9) | 3631 (93.7) |
| 1 | 125 (3.6) | 20 (5.2) | 145 (3.7) | |
| >1 | 89 (2.5) | 11 (2.8) | 100 (2.8) | |
| # physicians who see patient regularly (%) | 0 | 241 (6.9) | 22 (5.7) | 263 (6.8) |
| 1 | 3060 (87.7) | 333 (86.5) | 3393 (87.5) | |
| 2 | 150 (4.3) | 21 (5.5) | 171 (4.4) | |
| >2 | 281 (8.0) | 31 (8.0) | 312 (8.0) | |
| # admissions in previous 6 months (%) | 0 | 2420 (69.3) | 222 (57.7) | 2642 (68.2) |
| 1 | 833 (23.9) | 103 (26.8) | 936 (24.1) | |
| >1 | 238 (6.8) | 60 (15.6) | 298 (7.7) | |
| Index hospitalization description | ||||
| Number of discharge medications (IQR) | 4 (2‐7) | 6 (3‐9) | 4 (2‐7) | |
| Admitted to medical service (%) | 1440 (41.2) | 231 (60.0) | 1671 (43.1) | |
| Acute diagnoses: | ||||
| CAD (%) | 238 (6.8) | 23 (6.0) | 261 (6.7) | |
| Neoplasm of unspecified nature (%) | 196 (5.6) | 35 (9.1) | 231 (6.0) | |
| Heart failure (%) | 127 (3.6) | 38 (9.9) | 165 (4.3) | |
| Acute procedures | ||||
| CABG (%) | 182 (5.2) | 14 (3.6) | 196 (5.1) | |
| Total knee arthoplasty (%) | 173 (5.0) | 10 (2.6) | 183 (4.7) | |
| Total hip arthroplasty (%) | 118 (3.4) | (0.5) | 120 (3.1) | |
| Complication during admission (%) | 403 (11.5) | 63 (16.4) | 466 (12.0) | |
| LACE index: mean (SD) | 8.0 (3.6) | 10.3 (3.8) | 8.2 (3.7) | |
| Length of stay in days: median (IQR) | 4 (2‐7) | 6 (3‐10) | 4 (2‐8) | |
| Acute/emergent admission (%) | 1851 (53.0) | 272 (70.6) | 2123 (54.8) | |
| Charlson score (%) | 0 | 2771 (79.4) | 241 (62.6) | 3012 (77.7) |
| 1 | 103 (3.0) | 17 (4.4) | 120 (3.1) | |
| 2 | 446 (12.8) | 86 (22.3) | 532 (13.7) | |
| >2 | 171 (4.9) | 41 (10.6) | 212 (5.5) | |
| Emergency room use (# visits/ year) (%) | 0 | 2342 (67.1) | 190 (49.4) | 2532 (65.3) |
| 1 | 761 (21.8) | 101 (26.2) | 862 (22.2) | |
| >1 | 388 (11.1) | 94 (24.4) | 482 (12.4) | |
Patients were observed in the study for a median of 175 days (interquartile range [IQR] 175‐178). During this time they had a median of 4 physician visits (IQR 3‐6). The first postdischarge physician visit occurred a median of 10 days (IQR 6‐18) after discharge from hospital.
Continuity Measures
Table 2 summarizes all continuity scores. Since continuity scores varied significantly over time,28 Table 2 provides continuity scores on the last day of patient observation. Preadmission provider, postdischarge provider, and discharge summary continuity all had similar values and distributions with median values ranging between 0.444 and 0.571. 1797 (46.4%) patients had a hospital physician provider continuity scorae of 0.
| Minimum | 25th Percentile | Median | 75th Percentile | Maximum | |
|---|---|---|---|---|---|
| Provider continuity | |||||
| A: Pre‐admission physician | 0 | 0.143 | 0.444 | 0.667 | 1.000 |
| B: Hospital physician | 0 | 0 | 0.143 | 0.400 | 1.000 |
| C: Post‐discharge physician | 0 | 0.333 | 0.571 | 0.750 | 1.000 |
| Information continuity | |||||
| D: Discharge summary | 0 | 0.095 | 0.500 | 0.800 | 1.000 |
| E: Post‐discharge information | 0 | 0 | 0.182 | 0.500 | 1.000 |
Study Outcomes
During a median of 175 days of observation, 45 patients died (event rate 2.6 events per 100 patient‐years observation [95% CI 2.0‐3.4]) and 340 patients were urgently readmitted (event rate 19.6 events per 100 patient‐years observation [95% CI 15.9‐24.3]). Figure 2 presents the survival curves for time to death and time to urgent readmission. The hazard of death was consistent through the observation period but the risk of urgent readmission decreased slightly after 90 days postdischarge.
Association Between Continuity and Outcomes
Table 3 summarizes the association between provider and information continuity with study outcomes. No continuity measure was associated with time to death by itself (Table 3, column A) or with the other continuity measures (Table 3, column B). Preadmission physician continuity was associated with a significantly decreased risk of urgent readmission. When the proportion of postdischarge visits with a prehospital physician increased by 10%, the adjusted risk of urgent readmission decreased by 6% (adjusted hazards ratio (adj‐HR)) of 0.94 (95% CI, 0.91‐0.98). None of the other continuity measuresincluding hospital physicianwere significantly associated with urgent readmission either by themselves (Table 3, column A) or after adjusting for other continuity measures (Table 3, column B).
| Outcome | ||||||||
|---|---|---|---|---|---|---|---|---|
| Death (95% CI) | Urgent Readmission (95% CI) | |||||||
| A: Adjusted for Other Confounders Only | B: Adjusted for Other Confounders and Continuity Measures | A: Adjusted for Other Confounders Only | B: Adjusted for Other Confounders and Continuity Measures | |||||
| ||||||||
| Provider continuity | ||||||||
| A: Pre‐admission physician | 1.03 | (0.95, 1.12) | 1.06 | (0.95, 1.18) | 0.95 | (0.92, 0.98) | 0.94 | (0.91, 0.98) |
| B: Hospital physician | 0.87 | (0.74, 1.02) | 0.86 | (0.70, 1.03) | 0.98 | (0.94, 1.02) | 0.97 | (0.92, 1.01) |
| C: Post‐discharge physician | 0.97 | (0.89, 1.06) | 0.93 | (0.84, 1.04) | 0.98 | (0.95, 1.01) | 0.98 | (0.94, 1.02) |
| Information continuity | ||||||||
| D: Discharge Summary | 0.96 | (0.89, 1.04) | 0.94 | (0.87, 1.03) | 1.01 | (0.98, 1.04) | 1.02 | (0.99, 1.05) |
| E: Post‐discharge information | 1.01 | (0.94, 1.08) | 1.03 | (0.95, 1.11) | 1.00 | (0.97, 1.03) | 1.03 | (0.95, 1.11) |
| Other confounders | ||||||||
| Patient age in decades* | 1.43 | (1.13, 1.82) | 1.18 | (1.10, 1.28) | ||||
| Female | 1.50 | (0.81, 2.77) | 1.16 | (0.94, 1.44) | ||||
| # physicians who see patient regularly | ||||||||
| 1 | 1.46 | (0.92, 2.34) | ||||||
| 2 | 2.17 | (1.11, 4.26) | ||||||
| >2 | 3.71 | (1.55, 8.88) | ||||||
| Complications during admission | ||||||||
| 1 | 1.38 | (0.61, 3.10) | 0.81 | (0.55, 1.17) | ||||
| >1 | 1.01 | (0.28, 3.58) | 0.91 | (0.56, 1.48) | ||||
| # admissions in previous 6 months | ||||||||
| 1 | 1.27 | (0.59, 2.70) | 1.34 | (1.02, 1.76) | ||||
| >1 | 1.42 | (0.55, 3.67) | 1.78 | (1.26, 2.51) | ||||
| LACE index* | 1.16 | (1.06, 1.26) | 1.10 | (1.07, 1.14) | ||||
Increased patient age and increased LACE index score were both strongly associated with an increased risk of death (adj‐HR 1.43 [1.13‐1.82] and 1.16 [1.06‐1.26], respectively) and urgent readmission (adj‐HR 1.18 [1.10‐1.28] and 1.10 [1.07‐1.14], respectively). Hospitalization in the 6 months prior to admission significantly increased the risk of urgent readmission but not death. The risk of urgent readmission increased significantly as the number of regular prehospital physicians increased.
Sensitivity Analyses
Our study conclusions did not change in the sensitivity analyses. The number of postdischarge physician visits (expressed as a time‐dependent covariate) was not associated with either death or with urgent readmission and preadmission physician continuity remained significantly associated with time to urgent readmission (supporting information). Adding consultant continuity to the model also did not change our results (supporting information). In‐hospital consultant continuity was associated with an increased risk of urgent readmission (adj‐HR 1.10, 95% CI, 1.01‐1.20). The association between pre‐admission physician continuity and time to urgent readmission did not interact significantly with patient age, LACE index score, or number of previous admissions.
Discussion
This large, prospective cohort study measured the independent association of several provider and information continuity measures with important outcomes in patients discharged from hospital. After adjusting for potential confounders, we found that increased continuity with physicians who regularly cared for the patient prior to the admission was significantly and independently associated with a decreased risk of urgent readmission. Our data suggest that continuity with the hospital physician did not independently influence the risk of patient death or urgent readmission after discharge.
Although hospital physician continuity did not significantly change patient outcomes, we found that follow‐up with a physician who regularly treated the patient prior to their admission was associated with a significantly decreased risk of urgent readmission. This could reflect the important role that a patient's regular physician plays in their health care. Other studies have shown a positive association between continuity with a regular physician and improved outcomes including decreased emergency room utilization7, 8 and decreased hospitalization.10, 11
We were somewhat disappointed that information continuity was not independently associated with improved patient outcomes. Information continuity is likely more amenable to modification than is provider continuity. Of course, our study findings do not mean that information continuity does not improve patient outcomes, as in other studies.23, 33 Instead, our results could reflect that we solely measured the availability of information to physicians. Future studies that measure the quality, relevance, and actual utilization of patient information will be better able to discern the influence of information continuity on patient outcomes.
We believe that our study was methodologically strong and unique. We captured both provider and information continuity in a large group of representative patients using a broad range of measures that captured continuity's diverse components including both provider and information continuity. The continuity measures were expressed and properly analyzed as time‐dependent variables in a multivariate model.34 Our analysis controlled for important potential confounders. Our follow‐up and data collection was rigorous with 96.1% of our study group having complete follow‐up. Finally, the analysis used multiple imputation to appropriately handle missing data in the one incomplete variable (post‐discharge information continuity).3537
Several limitations of our study should be kept in mind. We are uncertain how our results might generalize to patients discharged from obstetrical or psychiatric services or people in other health systems. Our analysis had to exclude patients with less than two physician visits after discharge since this was the minimum required to calculate postdischarge physician and information continuity. Data collection for postdischarge information continuity was incomplete with data missing for 19.0% of all 15 401 visits in the original cohort.38 However, a response rate of 81.0% is very good39 when compared to other survey‐based studies40 and we accounted for the missing data using multiple imputation methods. The primary outcomes of our studytime to death or urgent readmissionmay be relatively insensitive to modification of quality of care, which is presumably improved by increased continuity.41 For example, Clarke found that the majority of readmissions in all patient groups were unavoidable with 94% of medical readmissions 1 month postdischarge judged to be unavoidable.42 Future studies regarding the effects of continuity could focus on its association with other outcomes that are more reflective of quality of care such as the risk of adverse events or medical error.21 Such outcomes would presumably be more sensitive to improved quality of care from increased continuity.
We believe that our study's major limitation was its inability to establish a causal association between continuity and patient outcomes. Our finding that increased consultant continuity was associated with an increased risk of poor outcomes highlights this concern. Presumably, patient follow‐up with a hospital consultant indicates a disease status with a high risk of bad patient outcomesa risk that is not entirely accounted for by the covariates used in this study. If we accept that unresolved confounding explains this association, the same could also apply to the association between preadmission physician continuity and improved outcomes. Perhaps patients who are doing well after discharge from hospital are able to return to their regular physician. Our analysis would therefore identify an association between increased preadmission physician continuity and improved patient outcomes. Analyses could also incorporate more discriminative measures of severity of hospital illness, such as those developed by Escobar et al.43 Since patients may experience health events after their discharge from hospital that could influence outcomes, recording these and expressing them in the study model as time‐dependent covariates will be important. Finally, similar to the classic study by Wasson et al.44 in 1984, a proper randomized trial that measures the effect of a continuity‐building intervention on both continuity of care and patient outcomes would help determine how continuity influences outcomes.
In conclusion, after discharge from hospital, increased continuity with physicians who routinely care for the patient is significantly and independently associated with a decreased risk of urgent readmission. Continuity with the hospital physician after discharge did not independently influence the risk of patient death or urgent readmission in our study. Further research is required to determine the causal association between preadmission physician continuity and improved outcomes. Until that time, clinicians should strive to optimize continuity with physicians their patients have seen prior to the hospitalization.
- Society of Hospital Medicine.2009.Ref Type: Internet Communication.
- ,,,.The status of hospital medicine groups in the United States.J Hosp Med.2006;1:75–80.
- ,.The hospitalist movement 5 years later. [see comment].JAMA.2002;287:487–494. [Review]
- ,,,.Hospitalists and the practice of inpatient medicine: results of a survey of the National Association of Inpatient Physicians. [see comment].Ann Intern Med.1999;130:343–349.
- ,,,.Primary care physician attitudes regarding communication with hospitalists.Am J Med.2001;111:15S–20S.
- ,,.Defusing the confusion: concepts and measures of continuity of healthcare.Ottawa,Canadian Health Services Research Foundation. Ref Type: Report.2002;1–50.
- ,,,,.Association between infant continuity of care and pediatric emergency department utilization.Pediatrics.2004;113:738–741.
- ,,,,.Is greater continuity of care associated with less emergency department utilization?Pediatrics.1999;103:738–742.
- ,,,,.Association of lower continuity of care with greater risk of emergency department use and hospitalization in children.Pediatrics.2001;107:524–529.
- ,,The role of provider continuity in preventing hospitalizations.Arch Fam Med.1998;7:352–357.
- ,.The importance of continuity of care in the likelihood of future hospitalization: is site of care equivalent to a primary clinician?Am J Public Health.1998;88:1539–1541.
- ,,,.Exploration of the relationship between continuity, trust in regular doctors and patient satisfaction with consultations with family doctors.Scand J Prim Health Care.2003;21:27–32.
- ,,,,.Longitudinal continuity of care is associated with high patient satisfaction with physical therapy.Phys Ther.2005;85:1046–1052.
- ,,,,.Provider continuity and outcomes of care for persons with schizophrenia.Ment Health Serv Res.2000;V2:201–211.
- ,,,,.Continuity of care is associated with well‐coordinated care.Ambul Pediatr.2003;3:82–86.
- ,,.The impact of insurance type and forced discontinuity on the delivery of primary care. [see comments.].J Fam Pract.1997;45:129–135.
- .Measuring attributes of primary care: development of a new instrument.J Fam Pract.1997;45:64–74.
- .Continuity of care during pregnancy: the effect of provider continuity on outcome.J Fam Pract.1985;21:375–380.
- ,,,,,.Physician‐patient relationship and medication compliance: a primary care investigation.Ann Fam Med.2004;2:455–461.
- ,,,.Continuity of care and cardiovascular risk factor management: does care by a single clinician add to informational continuity provided by electronic medical records?Am J Manag Care.2005;11:689–696.
- ,,,,.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161–167.
- ,,,.Continuity of care and patient outcomes after hospital discharge.J Gen lntern Med.2004;19:624–645.
- ,,,.Effect of discharge summary availability during post‐discharge visits on hospital readmission.J Gen Intern Med.2002;17:186–192.
- ,,, et al.Association of communication between hospital‐based physicians and primary care providers with patient outcomes.[see comment].J Gen Intern Med2009;24(3):381–386.
- ,,,,,.Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297:831–841.
- ,,, et al.Information exchange among physicians caring for the same patient in the community.Can Med Assoc J.2008;179:1013–1018.
- ,.Continuity of care in a university‐based practice.J Med Educ.1975;965–969.
- ,,, et al.Provider and information continuity after discharge from hospital: a prospective cohort study.2009. Ref Type: Unpublished Work.
- ,,, et al.Derivation and validation of the LACE index to predict early death or unplanned readmission after discharge from hospital to the community.CMAJ. (In press)
- ,,,.A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40:373–383.
- ,,,.Improved comorbidity adjustment for predicting mortality in Medicare populations.Health Serv Res.2003;38(4):1103–1120.
- ,.Modelling clustered survival data from multicentre clinical trials.Stat Med.2004;23:369–388.
- ,,,.Prevalence of information gaps in the emergency department and the effect on patient outcomes.CMAJ.2003;169:1023–1028.
- ,,,.Time‐dependent bias due to improper analytical methodology is common in prominent medical journals.J Clin Epidemiol.2004;57:672–682.
- .What do we do with missing data? Some options for analysis of incomplete data.Annu Rev Public Health.2004;25:99–117.
- ,,,.Survival estimates of a prognostic classification depended more on year of treatment than on imputation of missing values.J Clin Epidemiol.2006;59:246–253. [Review]
- .Bias arising from missing data in predictive models.[see comment].J Clin Epidemiol.2006;59:1115–1123.
- ,,, et al.Information exchange among physicians caring for the same patient in the community.CMAJ.2008;179:1013–1018.
- .Survey Research Methods.2nd ed.,Beverly Hills:Sage;1993.
- ,,.Response rates to mail surveys published in medical journals.J Clin Epidemiol.1997;50:1129–1136.
- .Readmission of patients to hospital: still ill defined and poorly understood.Int J Qual Health Care.2001;13:177–179.
- .Are readmissions avoidable?Br Med J.1990;301:1136–1138.
- ,,,,,.Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care.2008;46:232–239.
- ,,, et al.Continuity of outpatient medical care in elderly men. A randomized trial.JAMA.1984;252:2413–2417.
- Society of Hospital Medicine.2009.Ref Type: Internet Communication.
- ,,,.The status of hospital medicine groups in the United States.J Hosp Med.2006;1:75–80.
- ,.The hospitalist movement 5 years later. [see comment].JAMA.2002;287:487–494. [Review]
- ,,,.Hospitalists and the practice of inpatient medicine: results of a survey of the National Association of Inpatient Physicians. [see comment].Ann Intern Med.1999;130:343–349.
- ,,,.Primary care physician attitudes regarding communication with hospitalists.Am J Med.2001;111:15S–20S.
- ,,.Defusing the confusion: concepts and measures of continuity of healthcare.Ottawa,Canadian Health Services Research Foundation. Ref Type: Report.2002;1–50.
- ,,,,.Association between infant continuity of care and pediatric emergency department utilization.Pediatrics.2004;113:738–741.
- ,,,,.Is greater continuity of care associated with less emergency department utilization?Pediatrics.1999;103:738–742.
- ,,,,.Association of lower continuity of care with greater risk of emergency department use and hospitalization in children.Pediatrics.2001;107:524–529.
- ,,The role of provider continuity in preventing hospitalizations.Arch Fam Med.1998;7:352–357.
- ,.The importance of continuity of care in the likelihood of future hospitalization: is site of care equivalent to a primary clinician?Am J Public Health.1998;88:1539–1541.
- ,,,.Exploration of the relationship between continuity, trust in regular doctors and patient satisfaction with consultations with family doctors.Scand J Prim Health Care.2003;21:27–32.
- ,,,,.Longitudinal continuity of care is associated with high patient satisfaction with physical therapy.Phys Ther.2005;85:1046–1052.
- ,,,,.Provider continuity and outcomes of care for persons with schizophrenia.Ment Health Serv Res.2000;V2:201–211.
- ,,,,.Continuity of care is associated with well‐coordinated care.Ambul Pediatr.2003;3:82–86.
- ,,.The impact of insurance type and forced discontinuity on the delivery of primary care. [see comments.].J Fam Pract.1997;45:129–135.
- .Measuring attributes of primary care: development of a new instrument.J Fam Pract.1997;45:64–74.
- .Continuity of care during pregnancy: the effect of provider continuity on outcome.J Fam Pract.1985;21:375–380.
- ,,,,,.Physician‐patient relationship and medication compliance: a primary care investigation.Ann Fam Med.2004;2:455–461.
- ,,,.Continuity of care and cardiovascular risk factor management: does care by a single clinician add to informational continuity provided by electronic medical records?Am J Manag Care.2005;11:689–696.
- ,,,,.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161–167.
- ,,,.Continuity of care and patient outcomes after hospital discharge.J Gen lntern Med.2004;19:624–645.
- ,,,.Effect of discharge summary availability during post‐discharge visits on hospital readmission.J Gen Intern Med.2002;17:186–192.
- ,,, et al.Association of communication between hospital‐based physicians and primary care providers with patient outcomes.[see comment].J Gen Intern Med2009;24(3):381–386.
- ,,,,,.Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297:831–841.
- ,,, et al.Information exchange among physicians caring for the same patient in the community.Can Med Assoc J.2008;179:1013–1018.
- ,.Continuity of care in a university‐based practice.J Med Educ.1975;965–969.
- ,,, et al.Provider and information continuity after discharge from hospital: a prospective cohort study.2009. Ref Type: Unpublished Work.
- ,,, et al.Derivation and validation of the LACE index to predict early death or unplanned readmission after discharge from hospital to the community.CMAJ. (In press)
- ,,,.A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40:373–383.
- ,,,.Improved comorbidity adjustment for predicting mortality in Medicare populations.Health Serv Res.2003;38(4):1103–1120.
- ,.Modelling clustered survival data from multicentre clinical trials.Stat Med.2004;23:369–388.
- ,,,.Prevalence of information gaps in the emergency department and the effect on patient outcomes.CMAJ.2003;169:1023–1028.
- ,,,.Time‐dependent bias due to improper analytical methodology is common in prominent medical journals.J Clin Epidemiol.2004;57:672–682.
- .What do we do with missing data? Some options for analysis of incomplete data.Annu Rev Public Health.2004;25:99–117.
- ,,,.Survival estimates of a prognostic classification depended more on year of treatment than on imputation of missing values.J Clin Epidemiol.2006;59:246–253. [Review]
- .Bias arising from missing data in predictive models.[see comment].J Clin Epidemiol.2006;59:1115–1123.
- ,,, et al.Information exchange among physicians caring for the same patient in the community.CMAJ.2008;179:1013–1018.
- .Survey Research Methods.2nd ed.,Beverly Hills:Sage;1993.
- ,,.Response rates to mail surveys published in medical journals.J Clin Epidemiol.1997;50:1129–1136.
- .Readmission of patients to hospital: still ill defined and poorly understood.Int J Qual Health Care.2001;13:177–179.
- .Are readmissions avoidable?Br Med J.1990;301:1136–1138.
- ,,,,,.Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care.2008;46:232–239.
- ,,, et al.Continuity of outpatient medical care in elderly men. A randomized trial.JAMA.1984;252:2413–2417.
Copyright © 2010 Society of Hospital Medicine
