Healthcare quality is defined as the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.1 Delivering high quality care to patients in the hospital setting is especially challenging, given the rapid pace of clinical care, the severity and multitude of patient conditions, and the interdependence of complex processes within the hospital system. Research has shown that hospitalized patients do not consistently receive recommended care2 and are at risk for experiencing preventable harm.3 In an effort to stimulate improvement, stakeholders have called for increased accountability, including enhanced transparency and differential payment based on performance. A growing number of hospital process and outcome measures are readily available to the public via the Internet.46 The Joint Commission, which accredits US hospitals, requires the collection of core quality measure data7 and sets the expectation that National Patient Safety Goals be met to maintain accreditation.8 Moreover, the Center for Medicare and Medicaid Services (CMS) has developed a Value‐Based Purchasing (VBP) plan intended to adjust hospital payment based on quality measures and the occurrence of certain hospital‐acquired conditions.9, 10

Because of their clinical expertise, understanding of hospital clinical operations, leadership of multidisciplinary inpatient teams, and vested interest to improve the systems in which they work, hospitalists are perfectly positioned to collaborate with their institutions to improve the quality of care delivered to inpatients. However, many hospitalists are inadequately prepared to engage in efforts to improve quality, because medical schools and residency programs have not traditionally included or emphasized healthcare quality and patient safety in their curricula.1113 In a survey of 389 internal medicine‐trained hospitalists, significant educational deficiencies were identified in the area of systems‐based practice.14 Specifically, the topics of quality improvement, team management, practice guideline development, health information systems management, and coordination of care between healthcare settings were listed as essential skills for hospitalist practice but underemphasized in residency training. Recognizing the gap between the needs of practicing physicians and current medical education provided in healthcare quality, professional societies have recently published position papers calling for increased training in quality, safety, and systems, both in medical school11 and residency training.15, 16

The Society of Hospital Medicine (SHM) convened a Quality Summit in December 2008 to develop strategic plans related to healthcare quality. Summit attendees felt that most hospitalists lack the formal training necessary to evaluate, implement, and sustain system changes within the hospital. In response, the SHM Hospital Quality and Patient Safety (HQPS) Committee formed a Quality Improvement Education (QIE) subcommittee in 2009 to assess the needs of hospitalists with respect to hospital quality and patient safety, and to evaluate and expand upon existing educational programs in this area. Membership of the QIE subcommittee consisted of hospitalists with extensive experience in healthcare quality and medical education. The QIE subcommittee refined and expanded upon the healthcare quality and patient safety‐related competencies initially described in the Core Competencies in Hospital Medicine.17 The purpose of this report is to describe the development, provide definitions, and make recommendations on the use of the Hospital Quality and Patient Safety (HQPS) Competencies.

Development of The Hospital Quality and Patient Safety Competencies

The multistep process used by the SHM QIE subcommittee to develop the HQPS Competencies is summarized in Figure 1. We performed an in‐depth evaluation of current educational materials and offerings, including a review of the Core Competencies in Hospital Medicine, past annual SHM Quality Improvement Pre‐Course objectives, and the content of training courses offered by other organizations.1722 Throughout our analysis, we emphasized the identification of gaps in content relevant to hospitalists. We then used the Institute of Medicine's (IOM) 6 aims for healthcare quality as a foundation for developing the HQPS Competencies.1 Specifically, the IOM states that healthcare should be safe, effective, patient‐centered, timely, efficient, and equitable. Additionally, we reviewed and integrated elements of the Practice‐Based Learning and Improvement (PBLI) and Systems‐Based Practice (SBP) competencies as defined by the Accreditation Council for Graduate Medical Education (ACGME).23 We defined general areas of competence and specific standards for knowledge, skills, and attitudes within each area. Subcommittee members reflected on their own experience, as clinicians, educators, and leaders in healthcare quality and patient safety, to inform and refine the competency definitions and standards. Acknowledging that some hospitalists may serve as collaborators or clinical content experts, while others may serve as leaders of hospital quality initiatives, 3 levels of expertise were established: basic, intermediate, and advanced.

Figure 1

The QIE subcommittee presented a draft version of the HQPS Competencies to the HQPS Committee in the fall of 2009 and incorporated suggested revisions. The revised set of competencies was then reviewed by members of the Leadership and Education Committees during the winter of 2009‐2010, and additional recommendations were included in the final version now described.

Description of The Competencies

The 8 areas of competence include: Quality Measurement and Stakeholder Interests, Data Acquisition and Interpretation, Organizational Knowledge and Leadership Skills, Patient Safety Principles, Teamwork and Communication, Quality and Safety Improvement Methods, Health Information Systems, and Patient Centeredness. Three levels of competence and standards within each level and area are defined in Table 1. Standards use carefully selected action verbs to reflect educational goals for hospitalists at each level.24 The basic level represents a minimum level of competency for all practicing hospitalists. The intermediate level represents a hospitalist who is prepared to meaningfully engage and collaborate with his or her institution in quality improvement efforts. A hospitalist at this level may also lead uncomplicated improvement projects for his or her medical center and/or hospital medicine group. The advanced level represents a hospitalist prepared to lead quality improvement efforts for his or her institution and/or hospital medicine group. Many hospitalists at this level will have, or will be prepared to have, leadership positions in quality and patient safety at their institutions. Advanced level hospitalists will also have the expertise to teach and mentor other individuals in their quality improvement efforts.

Recommended Use of The Competencies

The HQPS Competencies provide a framework for curricula and other professional development experiences in healthcare quality and patient safety. We recommend a step‐wise approach to curriculum development which includes conducting a targeted needs assessment, defining goals and specific learning objectives, and evaluation of the curriculum.25 The HQPS Competencies can be used at each step and provide educational targets for learners across a range of interest and experience.

Conclusion

Hospitalists are poised to have a tremendous impact on improving the quality of care for hospitalized patients. The lack of training in quality improvement in traditional medical education programs, in which most current hospitalists were trained, can be overcome through appropriate use of the HQPS Competencies. Formal incorporation of the HQPS Competencies into professional development programs, and innovative educational initiatives and curricula, will help provide current hospitalists and the next generations of hospitalists with the needed skills to be successful.

Healthcare quality is defined as the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.1 Delivering high quality care to patients in the hospital setting is especially challenging, given the rapid pace of clinical care, the severity and multitude of patient conditions, and the interdependence of complex processes within the hospital system. Research has shown that hospitalized patients do not consistently receive recommended care2 and are at risk for experiencing preventable harm.3 In an effort to stimulate improvement, stakeholders have called for increased accountability, including enhanced transparency and differential payment based on performance. A growing number of hospital process and outcome measures are readily available to the public via the Internet.46 The Joint Commission, which accredits US hospitals, requires the collection of core quality measure data7 and sets the expectation that National Patient Safety Goals be met to maintain accreditation.8 Moreover, the Center for Medicare and Medicaid Services (CMS) has developed a Value‐Based Purchasing (VBP) plan intended to adjust hospital payment based on quality measures and the occurrence of certain hospital‐acquired conditions.9, 10

Because of their clinical expertise, understanding of hospital clinical operations, leadership of multidisciplinary inpatient teams, and vested interest to improve the systems in which they work, hospitalists are perfectly positioned to collaborate with their institutions to improve the quality of care delivered to inpatients. However, many hospitalists are inadequately prepared to engage in efforts to improve quality, because medical schools and residency programs have not traditionally included or emphasized healthcare quality and patient safety in their curricula.1113 In a survey of 389 internal medicine‐trained hospitalists, significant educational deficiencies were identified in the area of systems‐based practice.14 Specifically, the topics of quality improvement, team management, practice guideline development, health information systems management, and coordination of care between healthcare settings were listed as essential skills for hospitalist practice but underemphasized in residency training. Recognizing the gap between the needs of practicing physicians and current medical education provided in healthcare quality, professional societies have recently published position papers calling for increased training in quality, safety, and systems, both in medical school11 and residency training.15, 16

The Society of Hospital Medicine (SHM) convened a Quality Summit in December 2008 to develop strategic plans related to healthcare quality. Summit attendees felt that most hospitalists lack the formal training necessary to evaluate, implement, and sustain system changes within the hospital. In response, the SHM Hospital Quality and Patient Safety (HQPS) Committee formed a Quality Improvement Education (QIE) subcommittee in 2009 to assess the needs of hospitalists with respect to hospital quality and patient safety, and to evaluate and expand upon existing educational programs in this area. Membership of the QIE subcommittee consisted of hospitalists with extensive experience in healthcare quality and medical education. The QIE subcommittee refined and expanded upon the healthcare quality and patient safety‐related competencies initially described in the Core Competencies in Hospital Medicine.17 The purpose of this report is to describe the development, provide definitions, and make recommendations on the use of the Hospital Quality and Patient Safety (HQPS) Competencies.

Development of The Hospital Quality and Patient Safety Competencies

The multistep process used by the SHM QIE subcommittee to develop the HQPS Competencies is summarized in Figure 1. We performed an in‐depth evaluation of current educational materials and offerings, including a review of the Core Competencies in Hospital Medicine, past annual SHM Quality Improvement Pre‐Course objectives, and the content of training courses offered by other organizations.1722 Throughout our analysis, we emphasized the identification of gaps in content relevant to hospitalists. We then used the Institute of Medicine's (IOM) 6 aims for healthcare quality as a foundation for developing the HQPS Competencies.1 Specifically, the IOM states that healthcare should be safe, effective, patient‐centered, timely, efficient, and equitable. Additionally, we reviewed and integrated elements of the Practice‐Based Learning and Improvement (PBLI) and Systems‐Based Practice (SBP) competencies as defined by the Accreditation Council for Graduate Medical Education (ACGME).23 We defined general areas of competence and specific standards for knowledge, skills, and attitudes within each area. Subcommittee members reflected on their own experience, as clinicians, educators, and leaders in healthcare quality and patient safety, to inform and refine the competency definitions and standards. Acknowledging that some hospitalists may serve as collaborators or clinical content experts, while others may serve as leaders of hospital quality initiatives, 3 levels of expertise were established: basic, intermediate, and advanced.

Figure 1

The QIE subcommittee presented a draft version of the HQPS Competencies to the HQPS Committee in the fall of 2009 and incorporated suggested revisions. The revised set of competencies was then reviewed by members of the Leadership and Education Committees during the winter of 2009‐2010, and additional recommendations were included in the final version now described.

Description of The Competencies

The 8 areas of competence include: Quality Measurement and Stakeholder Interests, Data Acquisition and Interpretation, Organizational Knowledge and Leadership Skills, Patient Safety Principles, Teamwork and Communication, Quality and Safety Improvement Methods, Health Information Systems, and Patient Centeredness. Three levels of competence and standards within each level and area are defined in Table 1. Standards use carefully selected action verbs to reflect educational goals for hospitalists at each level.24 The basic level represents a minimum level of competency for all practicing hospitalists. The intermediate level represents a hospitalist who is prepared to meaningfully engage and collaborate with his or her institution in quality improvement efforts. A hospitalist at this level may also lead uncomplicated improvement projects for his or her medical center and/or hospital medicine group. The advanced level represents a hospitalist prepared to lead quality improvement efforts for his or her institution and/or hospital medicine group. Many hospitalists at this level will have, or will be prepared to have, leadership positions in quality and patient safety at their institutions. Advanced level hospitalists will also have the expertise to teach and mentor other individuals in their quality improvement efforts.

Recommended Use of The Competencies

The HQPS Competencies provide a framework for curricula and other professional development experiences in healthcare quality and patient safety. We recommend a step‐wise approach to curriculum development which includes conducting a targeted needs assessment, defining goals and specific learning objectives, and evaluation of the curriculum.25 The HQPS Competencies can be used at each step and provide educational targets for learners across a range of interest and experience.

Conclusion

Hospitalists are poised to have a tremendous impact on improving the quality of care for hospitalized patients. The lack of training in quality improvement in traditional medical education programs, in which most current hospitalists were trained, can be overcome through appropriate use of the HQPS Competencies. Formal incorporation of the HQPS Competencies into professional development programs, and innovative educational initiatives and curricula, will help provide current hospitalists and the next generations of hospitalists with the needed skills to be successful.

Healthcare quality is defined as the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.1 Delivering high quality care to patients in the hospital setting is especially challenging, given the rapid pace of clinical care, the severity and multitude of patient conditions, and the interdependence of complex processes within the hospital system. Research has shown that hospitalized patients do not consistently receive recommended care2 and are at risk for experiencing preventable harm.3 In an effort to stimulate improvement, stakeholders have called for increased accountability, including enhanced transparency and differential payment based on performance. A growing number of hospital process and outcome measures are readily available to the public via the Internet.46 The Joint Commission, which accredits US hospitals, requires the collection of core quality measure data7 and sets the expectation that National Patient Safety Goals be met to maintain accreditation.8 Moreover, the Center for Medicare and Medicaid Services (CMS) has developed a Value‐Based Purchasing (VBP) plan intended to adjust hospital payment based on quality measures and the occurrence of certain hospital‐acquired conditions.9, 10

Because of their clinical expertise, understanding of hospital clinical operations, leadership of multidisciplinary inpatient teams, and vested interest to improve the systems in which they work, hospitalists are perfectly positioned to collaborate with their institutions to improve the quality of care delivered to inpatients. However, many hospitalists are inadequately prepared to engage in efforts to improve quality, because medical schools and residency programs have not traditionally included or emphasized healthcare quality and patient safety in their curricula.1113 In a survey of 389 internal medicine‐trained hospitalists, significant educational deficiencies were identified in the area of systems‐based practice.14 Specifically, the topics of quality improvement, team management, practice guideline development, health information systems management, and coordination of care between healthcare settings were listed as essential skills for hospitalist practice but underemphasized in residency training. Recognizing the gap between the needs of practicing physicians and current medical education provided in healthcare quality, professional societies have recently published position papers calling for increased training in quality, safety, and systems, both in medical school11 and residency training.15, 16

The Society of Hospital Medicine (SHM) convened a Quality Summit in December 2008 to develop strategic plans related to healthcare quality. Summit attendees felt that most hospitalists lack the formal training necessary to evaluate, implement, and sustain system changes within the hospital. In response, the SHM Hospital Quality and Patient Safety (HQPS) Committee formed a Quality Improvement Education (QIE) subcommittee in 2009 to assess the needs of hospitalists with respect to hospital quality and patient safety, and to evaluate and expand upon existing educational programs in this area. Membership of the QIE subcommittee consisted of hospitalists with extensive experience in healthcare quality and medical education. The QIE subcommittee refined and expanded upon the healthcare quality and patient safety‐related competencies initially described in the Core Competencies in Hospital Medicine.17 The purpose of this report is to describe the development, provide definitions, and make recommendations on the use of the Hospital Quality and Patient Safety (HQPS) Competencies.

Development of The Hospital Quality and Patient Safety Competencies

The multistep process used by the SHM QIE subcommittee to develop the HQPS Competencies is summarized in Figure 1. We performed an in‐depth evaluation of current educational materials and offerings, including a review of the Core Competencies in Hospital Medicine, past annual SHM Quality Improvement Pre‐Course objectives, and the content of training courses offered by other organizations.1722 Throughout our analysis, we emphasized the identification of gaps in content relevant to hospitalists. We then used the Institute of Medicine's (IOM) 6 aims for healthcare quality as a foundation for developing the HQPS Competencies.1 Specifically, the IOM states that healthcare should be safe, effective, patient‐centered, timely, efficient, and equitable. Additionally, we reviewed and integrated elements of the Practice‐Based Learning and Improvement (PBLI) and Systems‐Based Practice (SBP) competencies as defined by the Accreditation Council for Graduate Medical Education (ACGME).23 We defined general areas of competence and specific standards for knowledge, skills, and attitudes within each area. Subcommittee members reflected on their own experience, as clinicians, educators, and leaders in healthcare quality and patient safety, to inform and refine the competency definitions and standards. Acknowledging that some hospitalists may serve as collaborators or clinical content experts, while others may serve as leaders of hospital quality initiatives, 3 levels of expertise were established: basic, intermediate, and advanced.

Figure 1

The QIE subcommittee presented a draft version of the HQPS Competencies to the HQPS Committee in the fall of 2009 and incorporated suggested revisions. The revised set of competencies was then reviewed by members of the Leadership and Education Committees during the winter of 2009‐2010, and additional recommendations were included in the final version now described.

Description of The Competencies

The 8 areas of competence include: Quality Measurement and Stakeholder Interests, Data Acquisition and Interpretation, Organizational Knowledge and Leadership Skills, Patient Safety Principles, Teamwork and Communication, Quality and Safety Improvement Methods, Health Information Systems, and Patient Centeredness. Three levels of competence and standards within each level and area are defined in Table 1. Standards use carefully selected action verbs to reflect educational goals for hospitalists at each level.24 The basic level represents a minimum level of competency for all practicing hospitalists. The intermediate level represents a hospitalist who is prepared to meaningfully engage and collaborate with his or her institution in quality improvement efforts. A hospitalist at this level may also lead uncomplicated improvement projects for his or her medical center and/or hospital medicine group. The advanced level represents a hospitalist prepared to lead quality improvement efforts for his or her institution and/or hospital medicine group. Many hospitalists at this level will have, or will be prepared to have, leadership positions in quality and patient safety at their institutions. Advanced level hospitalists will also have the expertise to teach and mentor other individuals in their quality improvement efforts.

Recommended Use of The Competencies

The HQPS Competencies provide a framework for curricula and other professional development experiences in healthcare quality and patient safety. We recommend a step‐wise approach to curriculum development which includes conducting a targeted needs assessment, defining goals and specific learning objectives, and evaluation of the curriculum.25 The HQPS Competencies can be used at each step and provide educational targets for learners across a range of interest and experience.

Conclusion

Hospitalists are poised to have a tremendous impact on improving the quality of care for hospitalized patients. The lack of training in quality improvement in traditional medical education programs, in which most current hospitalists were trained, can be overcome through appropriate use of the HQPS Competencies. Formal incorporation of the HQPS Competencies into professional development programs, and innovative educational initiatives and curricula, will help provide current hospitalists and the next generations of hospitalists with the needed skills to be successful.

As the population ages, hip fractures and heart failure increase in prevalence.1, 2 Heart failure prevalence is also increasing in hospitalized patients.3 Indeed, hospitalizations involving heart failure as an active issue tripled in the last 30 years.4 Heart failure has been associated with an increased risk for hip fracture,5, 6 and previous studies report a 6%20% prevalence of preoperative heart failure in hip fracture patients.710 While exacerbation of heart failure increases the mortality risk in patients admitted for hip fractures,8 the incidence of new heart failure, as well as the preoperative factors that predict postoperative heart failure in this patient population remain unclear.

American College of Cardiology/American Heart Association (ACC/AHA) perioperative guidelines identify orthopedic surgeries, including hip fracture repair, as intermediate risk procedures.11 Compared to other intermediate risk operations, however, postoperative outcomes following hip fracture repair differ significantly.1216 Overall mortality in hip fracture patients has been reported at 29% at one year,8 with the excess mortality from hip fracture alone at nearly 20%.10, 13 However, the exact factors that contribute to this excess mortality, particularly with regard to heart failure, remain unclear.

To examine the preoperative prevalence, subsequent incidence, and predictors of heart failure in patients undergoing hip fracture repair operations, this study used an established, population‐based database to compare the postoperative consequences in hip fracture repair patients with and without preexisting heart failure. We hypothesized that preoperative heart failure worsens postoperative outcomes in hip fracture patients.

METHODS

RESULTS

From among 1327 potential hip fracture repairs, we excluded 115 cases involving multiple injuries or operations (19), pathological fractures (20), in‐hospital fractures (3), or an operation >72 hours after the initial fracture (5). Three patients under 65 years of age were also excluded, as were cases with missing information (9) or cases managed nonoperatively (56). The final analysis included 1212 surgical cases in 1116 subjects. No subjects were lost to surveillance for 1 year following their hip fracture repair.

Table 1 summarizes the baseline characteristics of the study population. The overall prevalence of preoperative heart failure was 27.0% (327 of 1212). Those with preoperative heart failure were older, heavier, more likely male and white, and less likely to live independently preoperatively. They were also more likely to suffer from preexisting cardiovascular comorbidities.

Table 1 also summarizes the main outcome characteristics of the study population. Those with preoperative heart failure had longer mean lengths of stay (LOS), were more often discharged to a skilled facility, and demonstrated higher inpatient mortality rates.

Table 2 summarizes the outcomes associated with preoperative heart failure. The overall rate of postoperative heart failure was 6.7% within 7 postoperative days and 21.3% within 1 postoperative year. Postoperative heart failure was significantly more common among those with preoperative heart failure (hazard ratio [HR], 3.0; 95% confidence interval [CI], 2.3 to 3.9; P < 0.001). Among those without preoperative heart failure, rates of postoperative incident heart failure were 4.8% at 7 days and 15.0% at 1 year. Compared to patients without preoperative heart failure, those with preoperative heart failure demonstrated higher one year mortality rates and higher rates of postoperative heart failure at 7 days and 1 year.

Figure 1 displays the outcomes to 1 year of surveillance. Rates of postoperative heart failure and postoperative mortality were consistently higher among those with, versus without, preoperative heart failure. Figure 2 displays similar data stratified by gender. Postoperative heart failure rates did not differ significantly between genders (HR, 1.0; 95% CI, 0.8 to 1.4), but postoperative mortality rates were significantly higher among males than females (HR, 1.9; 95% CI, 1.5 to 2.5; P < 0.001).

Figure 1

Figure 2

Figure 3 displays survival rates to 1 year based on the occurrence of incident or recurrent heart failure within the first 7 postoperative days. Survival rates were lowest among patients with recurrent heart failure in the first 7 postoperative days and highest among those with no preoperative or postoperative heart failure. Subjects with incident heart failure in the first postoperative week, and those with preoperative heart failure who did not suffer a recurrence, demonstrated intermediate survival rates (P < 0.001 for trend across all four groups).

Figure 3

DISCUSSION

This population‐based study found that heart failure represents a highly prevalent condition in elderly patients undergoing hip fracture repairs. It demonstrates that those with preoperative heart failure typically suffer from more cardiovascular comorbidities and carry a higher risk of postoperative heart failure and postoperative mortality.

While many studies have focused on the epidemiology of hip fractures,21 population‐based data on cardiac complications following hip fracture repair are significantly less common. The ACC/AHA preoperative cardiac evaluation guidelines classify orthopedic procedures, including hip fracture repair, as intermediate risk.11 Consequently, some may assume that all orthopedic patients will have a mortality rate less than 5%. Indeed, the 30‐day postoperative mortality rate published from our institution's Total Joint Registry was 0.6% following elective total hip arthroplasty.22 However, the present study demonstrates that current ACC/AHA preoperative cardiac evaluation guidelines may not apply to the population of frail patients undergoing hip fracture repair. Particularly among those who experience new heart failure within the first seven days following surgery, outcomes are substantially worse than the ACC/AHA perioperative guidelines may suggest.11

Preoperative heart failure has been associated with adverse risk for postoperative mortality after hip fracture.9, 10, 12 However, these studies did not report heart failure as a complication of hip fracture repair. A prospective cohort study of 2448 hip fracture patients at an academic hospital in Great Britain found a 5% rate of inpatient heart failure as a postoperative complication.23 The hazard ratio for one‐year mortality was 11.3 with postoperative heart failure.23 However, the British study did not distinguish heart failure from other cardiovascular diseases as a preoperative comorbidity or stratify the risk for postoperative mortality by preoperative heart failure status.23 Our findings add to previous literature by measuring heart failure as a specific complication of hip fracture repair and examining the association of preoperative heart failure with postoperative heart failure and mortality.

Length of stay after hip fracture repair varies in the literature, but previous work has not clearly associated heart failure with length of hospitalization in the setting of hip fracture repair.24, 25 Our study found a significantly higher mean length of stay among those with preoperative heart failure. This adds to previous work by delineating an association between heart failure and increased length of stay after hip fracture repair.

We found a higher rate of postoperative mortality among men compared to women. Rates of postoperative heart failure, however, were more similar (Figure 2). Previous studies have found a consistently higher mortality rate among men versus women after hip fracture.9, 23, 2529 Possible explanations for these findings include the overall increased burden of cardiovascular disease among men, lower treatment rates of osteoporosis in men,30 and increased susceptibility to other postoperative complications, such as infection.25

The findings of this study carry important clinical implications for the perioperative care of hip fracture patients with, or at risk for, heart failure. They suggest that current risk stratification guidelines classifying orthopedic operations as intermediate risk procedures do not reflect the high risk for morbidity that hip fracture patients face.11 The association of heart failure with adverse outcomes implies the need for heightened surveillance in the perioperative period, particularly with regard to volume status and medication reconciliation. Hip fracture patients and their families must be counseled about the ramifications of perioperative heart failure, including higher rates of postoperative heart failure, longer hospitalizations, and ultimate mortality.

This research carries several limitations and remains subject to biases inherent in retrospective cohort studies. The reported effects of heart failure on outcomes after hip fracture repair may be due to confounding from age, functional status, and other comorbidities. We attempted to minimize sampling bias through complete enumeration of hip fracture surgeries among Olmsted County residents. Completeness of follow‐up (100% at one year) was possible given the availability of documentation of all inpatient and outpatient medical care in the community.17 We used objectively defined outcomes to minimize measurement bias. Applicability to a more diverse population may be limited because >95% of the research population was from a single, predominantly white community. However, prior studies have documented that hip fracture incidence rates31 and socioeconomic factors17 in Olmsted County are similar to those for other white residents of the United States. Heart failure rates were determined clinically according to the Framingham criteria. However, the Framingham criteria may inappropriately diagnose individuals with heart failure32 and falsely elevate the prevalence of heart failure as a preoperative comorbidity or postoperative complication.

The statistical analysis included patients counted multiple times if they underwent subsequent hip fracture repair during the study period. Including these patients may inaccurately inflate event rates or contribute to incorrect estimates of standard error. However, we felt it was appropriate to include recurrent hip fracture repair cases in the analysis because they represent a clinically distinct patient from both a medical and functional perspective. We used a robust variance estimator in the Cox proportional hazards models to provide an accurate calculation of the standard error given the possibility for correlation within subjects.20 Finally, the proportion of these patients was low (94 of 1116 unique patients; 8.4%).

Future work must involve further risk stratification and therapeutic interventions in perioperative hip fracture patients. A more robust analysis of heart failure, with differentiation between systolic and diastolic dysfunction, may facilitate risk stratification. Assessment of compliance with standard preoperative heart failure medications and the impact of heightened clinical vigilance may enlighten means to improve postoperative outcomes. Studies on risk stratification and therapeutic interventions may then inform policy regarding length of stay and reimbursement in hip fracture patients.

CONCLUSION

In summary, our population‐based findings reveal that heart failure represents a prevalent and serious comorbidity in patients undergoing hip fracture repair. Clinicians caring for perioperative hip fracture patients must pay particular attention to risk for, and implications of, new or recurrent heart failure.

As the population ages, hip fractures and heart failure increase in prevalence.1, 2 Heart failure prevalence is also increasing in hospitalized patients.3 Indeed, hospitalizations involving heart failure as an active issue tripled in the last 30 years.4 Heart failure has been associated with an increased risk for hip fracture,5, 6 and previous studies report a 6%20% prevalence of preoperative heart failure in hip fracture patients.710 While exacerbation of heart failure increases the mortality risk in patients admitted for hip fractures,8 the incidence of new heart failure, as well as the preoperative factors that predict postoperative heart failure in this patient population remain unclear.

American College of Cardiology/American Heart Association (ACC/AHA) perioperative guidelines identify orthopedic surgeries, including hip fracture repair, as intermediate risk procedures.11 Compared to other intermediate risk operations, however, postoperative outcomes following hip fracture repair differ significantly.1216 Overall mortality in hip fracture patients has been reported at 29% at one year,8 with the excess mortality from hip fracture alone at nearly 20%.10, 13 However, the exact factors that contribute to this excess mortality, particularly with regard to heart failure, remain unclear.

To examine the preoperative prevalence, subsequent incidence, and predictors of heart failure in patients undergoing hip fracture repair operations, this study used an established, population‐based database to compare the postoperative consequences in hip fracture repair patients with and without preexisting heart failure. We hypothesized that preoperative heart failure worsens postoperative outcomes in hip fracture patients.

METHODS

RESULTS

From among 1327 potential hip fracture repairs, we excluded 115 cases involving multiple injuries or operations (19), pathological fractures (20), in‐hospital fractures (3), or an operation >72 hours after the initial fracture (5). Three patients under 65 years of age were also excluded, as were cases with missing information (9) or cases managed nonoperatively (56). The final analysis included 1212 surgical cases in 1116 subjects. No subjects were lost to surveillance for 1 year following their hip fracture repair.

Table 1 summarizes the baseline characteristics of the study population. The overall prevalence of preoperative heart failure was 27.0% (327 of 1212). Those with preoperative heart failure were older, heavier, more likely male and white, and less likely to live independently preoperatively. They were also more likely to suffer from preexisting cardiovascular comorbidities.

Table 1 also summarizes the main outcome characteristics of the study population. Those with preoperative heart failure had longer mean lengths of stay (LOS), were more often discharged to a skilled facility, and demonstrated higher inpatient mortality rates.

Table 2 summarizes the outcomes associated with preoperative heart failure. The overall rate of postoperative heart failure was 6.7% within 7 postoperative days and 21.3% within 1 postoperative year. Postoperative heart failure was significantly more common among those with preoperative heart failure (hazard ratio [HR], 3.0; 95% confidence interval [CI], 2.3 to 3.9; P < 0.001). Among those without preoperative heart failure, rates of postoperative incident heart failure were 4.8% at 7 days and 15.0% at 1 year. Compared to patients without preoperative heart failure, those with preoperative heart failure demonstrated higher one year mortality rates and higher rates of postoperative heart failure at 7 days and 1 year.

Figure 1 displays the outcomes to 1 year of surveillance. Rates of postoperative heart failure and postoperative mortality were consistently higher among those with, versus without, preoperative heart failure. Figure 2 displays similar data stratified by gender. Postoperative heart failure rates did not differ significantly between genders (HR, 1.0; 95% CI, 0.8 to 1.4), but postoperative mortality rates were significantly higher among males than females (HR, 1.9; 95% CI, 1.5 to 2.5; P < 0.001).

Figure 1

Figure 2

Figure 3 displays survival rates to 1 year based on the occurrence of incident or recurrent heart failure within the first 7 postoperative days. Survival rates were lowest among patients with recurrent heart failure in the first 7 postoperative days and highest among those with no preoperative or postoperative heart failure. Subjects with incident heart failure in the first postoperative week, and those with preoperative heart failure who did not suffer a recurrence, demonstrated intermediate survival rates (P < 0.001 for trend across all four groups).

Figure 3

DISCUSSION

This population‐based study found that heart failure represents a highly prevalent condition in elderly patients undergoing hip fracture repairs. It demonstrates that those with preoperative heart failure typically suffer from more cardiovascular comorbidities and carry a higher risk of postoperative heart failure and postoperative mortality.

While many studies have focused on the epidemiology of hip fractures,21 population‐based data on cardiac complications following hip fracture repair are significantly less common. The ACC/AHA preoperative cardiac evaluation guidelines classify orthopedic procedures, including hip fracture repair, as intermediate risk.11 Consequently, some may assume that all orthopedic patients will have a mortality rate less than 5%. Indeed, the 30‐day postoperative mortality rate published from our institution's Total Joint Registry was 0.6% following elective total hip arthroplasty.22 However, the present study demonstrates that current ACC/AHA preoperative cardiac evaluation guidelines may not apply to the population of frail patients undergoing hip fracture repair. Particularly among those who experience new heart failure within the first seven days following surgery, outcomes are substantially worse than the ACC/AHA perioperative guidelines may suggest.11

Preoperative heart failure has been associated with adverse risk for postoperative mortality after hip fracture.9, 10, 12 However, these studies did not report heart failure as a complication of hip fracture repair. A prospective cohort study of 2448 hip fracture patients at an academic hospital in Great Britain found a 5% rate of inpatient heart failure as a postoperative complication.23 The hazard ratio for one‐year mortality was 11.3 with postoperative heart failure.23 However, the British study did not distinguish heart failure from other cardiovascular diseases as a preoperative comorbidity or stratify the risk for postoperative mortality by preoperative heart failure status.23 Our findings add to previous literature by measuring heart failure as a specific complication of hip fracture repair and examining the association of preoperative heart failure with postoperative heart failure and mortality.

Length of stay after hip fracture repair varies in the literature, but previous work has not clearly associated heart failure with length of hospitalization in the setting of hip fracture repair.24, 25 Our study found a significantly higher mean length of stay among those with preoperative heart failure. This adds to previous work by delineating an association between heart failure and increased length of stay after hip fracture repair.

We found a higher rate of postoperative mortality among men compared to women. Rates of postoperative heart failure, however, were more similar (Figure 2). Previous studies have found a consistently higher mortality rate among men versus women after hip fracture.9, 23, 2529 Possible explanations for these findings include the overall increased burden of cardiovascular disease among men, lower treatment rates of osteoporosis in men,30 and increased susceptibility to other postoperative complications, such as infection.25

The findings of this study carry important clinical implications for the perioperative care of hip fracture patients with, or at risk for, heart failure. They suggest that current risk stratification guidelines classifying orthopedic operations as intermediate risk procedures do not reflect the high risk for morbidity that hip fracture patients face.11 The association of heart failure with adverse outcomes implies the need for heightened surveillance in the perioperative period, particularly with regard to volume status and medication reconciliation. Hip fracture patients and their families must be counseled about the ramifications of perioperative heart failure, including higher rates of postoperative heart failure, longer hospitalizations, and ultimate mortality.

This research carries several limitations and remains subject to biases inherent in retrospective cohort studies. The reported effects of heart failure on outcomes after hip fracture repair may be due to confounding from age, functional status, and other comorbidities. We attempted to minimize sampling bias through complete enumeration of hip fracture surgeries among Olmsted County residents. Completeness of follow‐up (100% at one year) was possible given the availability of documentation of all inpatient and outpatient medical care in the community.17 We used objectively defined outcomes to minimize measurement bias. Applicability to a more diverse population may be limited because >95% of the research population was from a single, predominantly white community. However, prior studies have documented that hip fracture incidence rates31 and socioeconomic factors17 in Olmsted County are similar to those for other white residents of the United States. Heart failure rates were determined clinically according to the Framingham criteria. However, the Framingham criteria may inappropriately diagnose individuals with heart failure32 and falsely elevate the prevalence of heart failure as a preoperative comorbidity or postoperative complication.

The statistical analysis included patients counted multiple times if they underwent subsequent hip fracture repair during the study period. Including these patients may inaccurately inflate event rates or contribute to incorrect estimates of standard error. However, we felt it was appropriate to include recurrent hip fracture repair cases in the analysis because they represent a clinically distinct patient from both a medical and functional perspective. We used a robust variance estimator in the Cox proportional hazards models to provide an accurate calculation of the standard error given the possibility for correlation within subjects.20 Finally, the proportion of these patients was low (94 of 1116 unique patients; 8.4%).

Future work must involve further risk stratification and therapeutic interventions in perioperative hip fracture patients. A more robust analysis of heart failure, with differentiation between systolic and diastolic dysfunction, may facilitate risk stratification. Assessment of compliance with standard preoperative heart failure medications and the impact of heightened clinical vigilance may enlighten means to improve postoperative outcomes. Studies on risk stratification and therapeutic interventions may then inform policy regarding length of stay and reimbursement in hip fracture patients.

CONCLUSION

In summary, our population‐based findings reveal that heart failure represents a prevalent and serious comorbidity in patients undergoing hip fracture repair. Clinicians caring for perioperative hip fracture patients must pay particular attention to risk for, and implications of, new or recurrent heart failure.

As the population ages, hip fractures and heart failure increase in prevalence.1, 2 Heart failure prevalence is also increasing in hospitalized patients.3 Indeed, hospitalizations involving heart failure as an active issue tripled in the last 30 years.4 Heart failure has been associated with an increased risk for hip fracture,5, 6 and previous studies report a 6%20% prevalence of preoperative heart failure in hip fracture patients.710 While exacerbation of heart failure increases the mortality risk in patients admitted for hip fractures,8 the incidence of new heart failure, as well as the preoperative factors that predict postoperative heart failure in this patient population remain unclear.

American College of Cardiology/American Heart Association (ACC/AHA) perioperative guidelines identify orthopedic surgeries, including hip fracture repair, as intermediate risk procedures.11 Compared to other intermediate risk operations, however, postoperative outcomes following hip fracture repair differ significantly.1216 Overall mortality in hip fracture patients has been reported at 29% at one year,8 with the excess mortality from hip fracture alone at nearly 20%.10, 13 However, the exact factors that contribute to this excess mortality, particularly with regard to heart failure, remain unclear.

To examine the preoperative prevalence, subsequent incidence, and predictors of heart failure in patients undergoing hip fracture repair operations, this study used an established, population‐based database to compare the postoperative consequences in hip fracture repair patients with and without preexisting heart failure. We hypothesized that preoperative heart failure worsens postoperative outcomes in hip fracture patients.

METHODS

RESULTS

From among 1327 potential hip fracture repairs, we excluded 115 cases involving multiple injuries or operations (19), pathological fractures (20), in‐hospital fractures (3), or an operation >72 hours after the initial fracture (5). Three patients under 65 years of age were also excluded, as were cases with missing information (9) or cases managed nonoperatively (56). The final analysis included 1212 surgical cases in 1116 subjects. No subjects were lost to surveillance for 1 year following their hip fracture repair.

Table 1 summarizes the baseline characteristics of the study population. The overall prevalence of preoperative heart failure was 27.0% (327 of 1212). Those with preoperative heart failure were older, heavier, more likely male and white, and less likely to live independently preoperatively. They were also more likely to suffer from preexisting cardiovascular comorbidities.

Table 1 also summarizes the main outcome characteristics of the study population. Those with preoperative heart failure had longer mean lengths of stay (LOS), were more often discharged to a skilled facility, and demonstrated higher inpatient mortality rates.

Table 2 summarizes the outcomes associated with preoperative heart failure. The overall rate of postoperative heart failure was 6.7% within 7 postoperative days and 21.3% within 1 postoperative year. Postoperative heart failure was significantly more common among those with preoperative heart failure (hazard ratio [HR], 3.0; 95% confidence interval [CI], 2.3 to 3.9; P < 0.001). Among those without preoperative heart failure, rates of postoperative incident heart failure were 4.8% at 7 days and 15.0% at 1 year. Compared to patients without preoperative heart failure, those with preoperative heart failure demonstrated higher one year mortality rates and higher rates of postoperative heart failure at 7 days and 1 year.

Figure 1 displays the outcomes to 1 year of surveillance. Rates of postoperative heart failure and postoperative mortality were consistently higher among those with, versus without, preoperative heart failure. Figure 2 displays similar data stratified by gender. Postoperative heart failure rates did not differ significantly between genders (HR, 1.0; 95% CI, 0.8 to 1.4), but postoperative mortality rates were significantly higher among males than females (HR, 1.9; 95% CI, 1.5 to 2.5; P < 0.001).

Figure 1

Figure 2

Figure 3 displays survival rates to 1 year based on the occurrence of incident or recurrent heart failure within the first 7 postoperative days. Survival rates were lowest among patients with recurrent heart failure in the first 7 postoperative days and highest among those with no preoperative or postoperative heart failure. Subjects with incident heart failure in the first postoperative week, and those with preoperative heart failure who did not suffer a recurrence, demonstrated intermediate survival rates (P < 0.001 for trend across all four groups).

Figure 3

DISCUSSION

This population‐based study found that heart failure represents a highly prevalent condition in elderly patients undergoing hip fracture repairs. It demonstrates that those with preoperative heart failure typically suffer from more cardiovascular comorbidities and carry a higher risk of postoperative heart failure and postoperative mortality.

While many studies have focused on the epidemiology of hip fractures,21 population‐based data on cardiac complications following hip fracture repair are significantly less common. The ACC/AHA preoperative cardiac evaluation guidelines classify orthopedic procedures, including hip fracture repair, as intermediate risk.11 Consequently, some may assume that all orthopedic patients will have a mortality rate less than 5%. Indeed, the 30‐day postoperative mortality rate published from our institution's Total Joint Registry was 0.6% following elective total hip arthroplasty.22 However, the present study demonstrates that current ACC/AHA preoperative cardiac evaluation guidelines may not apply to the population of frail patients undergoing hip fracture repair. Particularly among those who experience new heart failure within the first seven days following surgery, outcomes are substantially worse than the ACC/AHA perioperative guidelines may suggest.11

Preoperative heart failure has been associated with adverse risk for postoperative mortality after hip fracture.9, 10, 12 However, these studies did not report heart failure as a complication of hip fracture repair. A prospective cohort study of 2448 hip fracture patients at an academic hospital in Great Britain found a 5% rate of inpatient heart failure as a postoperative complication.23 The hazard ratio for one‐year mortality was 11.3 with postoperative heart failure.23 However, the British study did not distinguish heart failure from other cardiovascular diseases as a preoperative comorbidity or stratify the risk for postoperative mortality by preoperative heart failure status.23 Our findings add to previous literature by measuring heart failure as a specific complication of hip fracture repair and examining the association of preoperative heart failure with postoperative heart failure and mortality.

Length of stay after hip fracture repair varies in the literature, but previous work has not clearly associated heart failure with length of hospitalization in the setting of hip fracture repair.24, 25 Our study found a significantly higher mean length of stay among those with preoperative heart failure. This adds to previous work by delineating an association between heart failure and increased length of stay after hip fracture repair.

We found a higher rate of postoperative mortality among men compared to women. Rates of postoperative heart failure, however, were more similar (Figure 2). Previous studies have found a consistently higher mortality rate among men versus women after hip fracture.9, 23, 2529 Possible explanations for these findings include the overall increased burden of cardiovascular disease among men, lower treatment rates of osteoporosis in men,30 and increased susceptibility to other postoperative complications, such as infection.25

The findings of this study carry important clinical implications for the perioperative care of hip fracture patients with, or at risk for, heart failure. They suggest that current risk stratification guidelines classifying orthopedic operations as intermediate risk procedures do not reflect the high risk for morbidity that hip fracture patients face.11 The association of heart failure with adverse outcomes implies the need for heightened surveillance in the perioperative period, particularly with regard to volume status and medication reconciliation. Hip fracture patients and their families must be counseled about the ramifications of perioperative heart failure, including higher rates of postoperative heart failure, longer hospitalizations, and ultimate mortality.

This research carries several limitations and remains subject to biases inherent in retrospective cohort studies. The reported effects of heart failure on outcomes after hip fracture repair may be due to confounding from age, functional status, and other comorbidities. We attempted to minimize sampling bias through complete enumeration of hip fracture surgeries among Olmsted County residents. Completeness of follow‐up (100% at one year) was possible given the availability of documentation of all inpatient and outpatient medical care in the community.17 We used objectively defined outcomes to minimize measurement bias. Applicability to a more diverse population may be limited because >95% of the research population was from a single, predominantly white community. However, prior studies have documented that hip fracture incidence rates31 and socioeconomic factors17 in Olmsted County are similar to those for other white residents of the United States. Heart failure rates were determined clinically according to the Framingham criteria. However, the Framingham criteria may inappropriately diagnose individuals with heart failure32 and falsely elevate the prevalence of heart failure as a preoperative comorbidity or postoperative complication.

The statistical analysis included patients counted multiple times if they underwent subsequent hip fracture repair during the study period. Including these patients may inaccurately inflate event rates or contribute to incorrect estimates of standard error. However, we felt it was appropriate to include recurrent hip fracture repair cases in the analysis because they represent a clinically distinct patient from both a medical and functional perspective. We used a robust variance estimator in the Cox proportional hazards models to provide an accurate calculation of the standard error given the possibility for correlation within subjects.20 Finally, the proportion of these patients was low (94 of 1116 unique patients; 8.4%).

Future work must involve further risk stratification and therapeutic interventions in perioperative hip fracture patients. A more robust analysis of heart failure, with differentiation between systolic and diastolic dysfunction, may facilitate risk stratification. Assessment of compliance with standard preoperative heart failure medications and the impact of heightened clinical vigilance may enlighten means to improve postoperative outcomes. Studies on risk stratification and therapeutic interventions may then inform policy regarding length of stay and reimbursement in hip fracture patients.

CONCLUSION

In summary, our population‐based findings reveal that heart failure represents a prevalent and serious comorbidity in patients undergoing hip fracture repair. Clinicians caring for perioperative hip fracture patients must pay particular attention to risk for, and implications of, new or recurrent heart failure.

With the aging of the US population, complex medication regimens to treat multiple comorbidities are increasingly common.1 Nevertheless, patients often do not fully understand the instructions for safe and effective medication use. Aspects of medication understanding include knowledge of the drug indication, dose, frequency, and for certain medications, special instructions.2 Medication understanding is associated with better medication adherence, fewer drug‐related problems, and fewer emergency department visits.3 Among patients with chronic conditions, such as cardiovascular disease (CVD), understanding and adherence to the medication regimen are critical for successful disease control and clinical outcomes.4

Patients' understanding of their medication regimen is also vitally important upon admission to the hospital. Patients often are the main source of information for the admission medication history and subsequent medication reconciliation.5 Poor patient understanding of the preadmission medication regimen can contribute to errors in inpatient and postdischarge medication orders, and adversely affect patient safety.6 However, little research has examined patients' understanding of the preadmission medication regimen and factors that affect it.

In the outpatient setting, previous investigations have suggested that low health literacy, advanced age, and impaired cognitive function adversely affect patients' understanding of medication instructions.2, 7, 8 These studies were limited by a small sample size, single site, or focus on a specific population, such as inner‐city patients. Additionally, the measures used to assess medication understanding were time‐consuming and required patients' medications to be present for testing, thus limiting their utility.2

To address these gaps in the literature, we developed and implemented the Medication Understanding Questionnaire (MUQ), an original and relatively rapid measure that does not require patients' medications be present for testing. In a study of adults at 2 large teaching hospitals, we examined the association of health literacy, age, cognitive function, number of preadmission medications, and other factors on patients' understanding of their preadmission medication regimen. We hypothesized that lower health literacy would be independently associated with lower medication understanding as assessed using the MUQ.

METHODS

The present study was a cross‐sectional assessment conducted using baseline interview data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) Study (ClinicalTrials.gov Registration #NCT00632021; available at: http://clinicaltrials.gov/show/NCT00632021). The PILL‐CVD Study is a randomized controlled trial of a pharmacist‐based intervention, consisting of pharmacist‐assisted medication reconciliation, inpatient counseling, low‐literacy adherence aids, and postdischarge telephone follow‐up. It was conducted at 2 academic medical centersVanderbilt University Hospital (VUH) in Nashville, Tennessee, and Brigham and Women's Hospital (BWH) in Boston, Massachusetts.9 This study was approved by the Institutional Review Board at each site, and all participants provided written informed consent.

RESULTS

Predictors of Medication Understanding

Unadjusted relationships of health literacy, cognition, and number of medications with medication understanding are shown in Figure 1 (panels A, B, and C, respectively). The figure demonstrates a linear relationship with both health literacy (Figure 1A) and cognition (Figure 1B), and a nonlinear relationship between number of preadmission medications and MUQ score (Figure 1C).

Figure 1

Adjusted relationships using imputed data for missing covariates are shown in Figure 2. Lower health literacy, cognitive impairment, male gender, and black race were independently associated with lower understanding of preadmission medications. Each 1 point increase in s‐TOFHLA or Mini‐Cog score led to an increase in medication understanding (OR = 1.04; 95% CI, 1.02 to 1.06; P = 0.0001; and OR = 1.24; 95% CI, 1.1 to 1.4; P = 0.001; respectively). Patients with marginal or inadequate health literacy had lower odds of understanding their regimen (OR = 0.53; 95% CI, 0.34 to 0.84; and OR = 0.49; 95% CI, 0.31 to 0.78, respectively) compared to those with adequate health literacy. Impaired cognitive function (Mini‐Cog score <3, indicating dementia) was also associated with lower odds of medication understanding (OR = 0.57; 95% CI, 0.38 to 0.86) compared to those with no cognitive impairment. An increase in the number of preadmission medications (up to 10) was also strongly associated with lower MUQ scores. For each increase by 1 medication, there was a significant decrease in medication understanding, up to 10 medications, beyond which understanding did not significantly decrease further. Patients on 6 medications were about half as likely to understand their medication regimen as patients on only 1 medication (OR = 0.52; 95% CI, 0.36 to 0.75). For patients on 11 medications, the odds of medication understanding were 24% lower than for patients on 6 medications (OR = 0.76; 95% CI, 0.65 to 0.89). Patients' age, years of schooling, and household income were not independently associated with medication understanding. Results were similar using data without multiple imputation.

Figure 2

DISCUSSION

We used a novel four‐component medication understanding questionnaire, developed for this study, to assess patients' understanding of up to 5 drugs selected randomly from the participant's preadmission medication list. The MUQ proved to be easy to administer by nonmedical staff within a short period of time (approximately 5 minutes per patient). It was well understood by patients. By limiting the assessment to 5 or fewer medications, the MUQ has a distinct advantage over existing measures of medication understanding that require testing the entire regimen. We did not find any limitations related to cutting off the assessment at 5 medications. In addition, this tool affords assessment of medication understanding without requiring medication bottles be present, enhancing its utility in the inpatient setting.

MUQ scores were associated with health literacy and other patient characteristics in an expected manner. We demonstrated that inadequate or marginal health literacy, as well as impaired cognitive function, were associated with low medication understanding. We also were able to demonstrate a relationship between increasing number of medications and lower medication understanding. Interestingly, in our patient population, understanding continued to decrease until reaching 10 medications, beyond which further increases in the number of medications had no additional detrimental effect on medication understanding. This nonlinear relationship between number of medications and medication understanding has potential implications for prescribing practice.

Our findings which utilize the MUQ among inpatients are consistent with prior literature in other settings.2, 7, 8 In a previous outpatient study, we identified that health literacy plays an important role in a patient's ability to successfully report and manage their daily medications.2 Other studies have also shown that patients with low health literacy have more difficulty understanding prescription drug information, and that they often experience medication‐related problems after hospital discharge.15, 16 The number and often the types of medications an individual takes have also been shown to increase the risk for adverse events and nonadherence to the treatment plan.1720 We postulate that this risk of adverse drug events is related at least in part to a patient's understanding of their medication regimen.

There are several limitations to this study. First, the MUQ did not assess certain aspects of medication understanding, such as knowledge of pill appearance or side effects, nor did it assess components of patients' actual drug‐taking behavior, such as organization of medications or behavioral cues. Thus, adaptive behaviors that patients may perform to improve their medication management, such as writing on labels or memory cues, are not captured by this test. Second, in administering and scoring the MUQ, we used the patient's preadmission medication list documented in the EHR as the reference standard. This was the best available reference list, and was generally accurate, as both hospitals had medication reconciliation systems in use at the time of the study21; nevertheless, it may contain inaccuracies. Documentation for certain medications, such as warfarin, in which dose can change frequently, often did not reflect the latest prescribed dose. In such cases, we scored the patient's answer as correct if the dose appeared reasonable and appropriate to the clinical pharmacist. As a result, a patient's MUQ score may have been overestimated in these cases.

Additional research will be needed to further validate the MUQ in other settings. In particular, studies should establish the relationship between the MUQ, serious medication errors after discharge, and potential to benefit from educational interventions. Also, as noted above, the nonlinear relationship between number of medications and medication understanding should be confirmed in other studies.

In conclusion, we demonstrated that patients with low health literacy, impaired cognition, or a higher number of medications had significantly poorer understanding of their preadmission medication regimen. These findings have important clinical implications. It would be appropriate to exercise greater caution when taking a medication history from patients who cannot readily provide the purpose, strength, units, and frequency of their medications. Attempts to validate the information obtained from patients with other sources of data, such as family members, inpatient or outpatient health records, and community pharmacy records should be considered. Patients at high risk for poor medication understanding, either measured directly using the MUQ or identified via risk factors such as polypharmacy, low cognition, or low health literacy, may warrant more intensive medication reconciliation interventions and/or educational counseling and follow‐up to prevent postdischarge adverse drug events. Further research is needed to determine if targeting these populations for interventions improves medication safety during transitions in care.

With the aging of the US population, complex medication regimens to treat multiple comorbidities are increasingly common.1 Nevertheless, patients often do not fully understand the instructions for safe and effective medication use. Aspects of medication understanding include knowledge of the drug indication, dose, frequency, and for certain medications, special instructions.2 Medication understanding is associated with better medication adherence, fewer drug‐related problems, and fewer emergency department visits.3 Among patients with chronic conditions, such as cardiovascular disease (CVD), understanding and adherence to the medication regimen are critical for successful disease control and clinical outcomes.4

Patients' understanding of their medication regimen is also vitally important upon admission to the hospital. Patients often are the main source of information for the admission medication history and subsequent medication reconciliation.5 Poor patient understanding of the preadmission medication regimen can contribute to errors in inpatient and postdischarge medication orders, and adversely affect patient safety.6 However, little research has examined patients' understanding of the preadmission medication regimen and factors that affect it.

In the outpatient setting, previous investigations have suggested that low health literacy, advanced age, and impaired cognitive function adversely affect patients' understanding of medication instructions.2, 7, 8 These studies were limited by a small sample size, single site, or focus on a specific population, such as inner‐city patients. Additionally, the measures used to assess medication understanding were time‐consuming and required patients' medications to be present for testing, thus limiting their utility.2

To address these gaps in the literature, we developed and implemented the Medication Understanding Questionnaire (MUQ), an original and relatively rapid measure that does not require patients' medications be present for testing. In a study of adults at 2 large teaching hospitals, we examined the association of health literacy, age, cognitive function, number of preadmission medications, and other factors on patients' understanding of their preadmission medication regimen. We hypothesized that lower health literacy would be independently associated with lower medication understanding as assessed using the MUQ.

METHODS

The present study was a cross‐sectional assessment conducted using baseline interview data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) Study (ClinicalTrials.gov Registration #NCT00632021; available at: http://clinicaltrials.gov/show/NCT00632021). The PILL‐CVD Study is a randomized controlled trial of a pharmacist‐based intervention, consisting of pharmacist‐assisted medication reconciliation, inpatient counseling, low‐literacy adherence aids, and postdischarge telephone follow‐up. It was conducted at 2 academic medical centersVanderbilt University Hospital (VUH) in Nashville, Tennessee, and Brigham and Women's Hospital (BWH) in Boston, Massachusetts.9 This study was approved by the Institutional Review Board at each site, and all participants provided written informed consent.

RESULTS

Predictors of Medication Understanding

Unadjusted relationships of health literacy, cognition, and number of medications with medication understanding are shown in Figure 1 (panels A, B, and C, respectively). The figure demonstrates a linear relationship with both health literacy (Figure 1A) and cognition (Figure 1B), and a nonlinear relationship between number of preadmission medications and MUQ score (Figure 1C).

Figure 1

Adjusted relationships using imputed data for missing covariates are shown in Figure 2. Lower health literacy, cognitive impairment, male gender, and black race were independently associated with lower understanding of preadmission medications. Each 1 point increase in s‐TOFHLA or Mini‐Cog score led to an increase in medication understanding (OR = 1.04; 95% CI, 1.02 to 1.06; P = 0.0001; and OR = 1.24; 95% CI, 1.1 to 1.4; P = 0.001; respectively). Patients with marginal or inadequate health literacy had lower odds of understanding their regimen (OR = 0.53; 95% CI, 0.34 to 0.84; and OR = 0.49; 95% CI, 0.31 to 0.78, respectively) compared to those with adequate health literacy. Impaired cognitive function (Mini‐Cog score <3, indicating dementia) was also associated with lower odds of medication understanding (OR = 0.57; 95% CI, 0.38 to 0.86) compared to those with no cognitive impairment. An increase in the number of preadmission medications (up to 10) was also strongly associated with lower MUQ scores. For each increase by 1 medication, there was a significant decrease in medication understanding, up to 10 medications, beyond which understanding did not significantly decrease further. Patients on 6 medications were about half as likely to understand their medication regimen as patients on only 1 medication (OR = 0.52; 95% CI, 0.36 to 0.75). For patients on 11 medications, the odds of medication understanding were 24% lower than for patients on 6 medications (OR = 0.76; 95% CI, 0.65 to 0.89). Patients' age, years of schooling, and household income were not independently associated with medication understanding. Results were similar using data without multiple imputation.

Figure 2

DISCUSSION

We used a novel four‐component medication understanding questionnaire, developed for this study, to assess patients' understanding of up to 5 drugs selected randomly from the participant's preadmission medication list. The MUQ proved to be easy to administer by nonmedical staff within a short period of time (approximately 5 minutes per patient). It was well understood by patients. By limiting the assessment to 5 or fewer medications, the MUQ has a distinct advantage over existing measures of medication understanding that require testing the entire regimen. We did not find any limitations related to cutting off the assessment at 5 medications. In addition, this tool affords assessment of medication understanding without requiring medication bottles be present, enhancing its utility in the inpatient setting.

MUQ scores were associated with health literacy and other patient characteristics in an expected manner. We demonstrated that inadequate or marginal health literacy, as well as impaired cognitive function, were associated with low medication understanding. We also were able to demonstrate a relationship between increasing number of medications and lower medication understanding. Interestingly, in our patient population, understanding continued to decrease until reaching 10 medications, beyond which further increases in the number of medications had no additional detrimental effect on medication understanding. This nonlinear relationship between number of medications and medication understanding has potential implications for prescribing practice.

Our findings which utilize the MUQ among inpatients are consistent with prior literature in other settings.2, 7, 8 In a previous outpatient study, we identified that health literacy plays an important role in a patient's ability to successfully report and manage their daily medications.2 Other studies have also shown that patients with low health literacy have more difficulty understanding prescription drug information, and that they often experience medication‐related problems after hospital discharge.15, 16 The number and often the types of medications an individual takes have also been shown to increase the risk for adverse events and nonadherence to the treatment plan.1720 We postulate that this risk of adverse drug events is related at least in part to a patient's understanding of their medication regimen.

There are several limitations to this study. First, the MUQ did not assess certain aspects of medication understanding, such as knowledge of pill appearance or side effects, nor did it assess components of patients' actual drug‐taking behavior, such as organization of medications or behavioral cues. Thus, adaptive behaviors that patients may perform to improve their medication management, such as writing on labels or memory cues, are not captured by this test. Second, in administering and scoring the MUQ, we used the patient's preadmission medication list documented in the EHR as the reference standard. This was the best available reference list, and was generally accurate, as both hospitals had medication reconciliation systems in use at the time of the study21; nevertheless, it may contain inaccuracies. Documentation for certain medications, such as warfarin, in which dose can change frequently, often did not reflect the latest prescribed dose. In such cases, we scored the patient's answer as correct if the dose appeared reasonable and appropriate to the clinical pharmacist. As a result, a patient's MUQ score may have been overestimated in these cases.

Additional research will be needed to further validate the MUQ in other settings. In particular, studies should establish the relationship between the MUQ, serious medication errors after discharge, and potential to benefit from educational interventions. Also, as noted above, the nonlinear relationship between number of medications and medication understanding should be confirmed in other studies.

In conclusion, we demonstrated that patients with low health literacy, impaired cognition, or a higher number of medications had significantly poorer understanding of their preadmission medication regimen. These findings have important clinical implications. It would be appropriate to exercise greater caution when taking a medication history from patients who cannot readily provide the purpose, strength, units, and frequency of their medications. Attempts to validate the information obtained from patients with other sources of data, such as family members, inpatient or outpatient health records, and community pharmacy records should be considered. Patients at high risk for poor medication understanding, either measured directly using the MUQ or identified via risk factors such as polypharmacy, low cognition, or low health literacy, may warrant more intensive medication reconciliation interventions and/or educational counseling and follow‐up to prevent postdischarge adverse drug events. Further research is needed to determine if targeting these populations for interventions improves medication safety during transitions in care.

With the aging of the US population, complex medication regimens to treat multiple comorbidities are increasingly common.1 Nevertheless, patients often do not fully understand the instructions for safe and effective medication use. Aspects of medication understanding include knowledge of the drug indication, dose, frequency, and for certain medications, special instructions.2 Medication understanding is associated with better medication adherence, fewer drug‐related problems, and fewer emergency department visits.3 Among patients with chronic conditions, such as cardiovascular disease (CVD), understanding and adherence to the medication regimen are critical for successful disease control and clinical outcomes.4

Patients' understanding of their medication regimen is also vitally important upon admission to the hospital. Patients often are the main source of information for the admission medication history and subsequent medication reconciliation.5 Poor patient understanding of the preadmission medication regimen can contribute to errors in inpatient and postdischarge medication orders, and adversely affect patient safety.6 However, little research has examined patients' understanding of the preadmission medication regimen and factors that affect it.

In the outpatient setting, previous investigations have suggested that low health literacy, advanced age, and impaired cognitive function adversely affect patients' understanding of medication instructions.2, 7, 8 These studies were limited by a small sample size, single site, or focus on a specific population, such as inner‐city patients. Additionally, the measures used to assess medication understanding were time‐consuming and required patients' medications to be present for testing, thus limiting their utility.2

To address these gaps in the literature, we developed and implemented the Medication Understanding Questionnaire (MUQ), an original and relatively rapid measure that does not require patients' medications be present for testing. In a study of adults at 2 large teaching hospitals, we examined the association of health literacy, age, cognitive function, number of preadmission medications, and other factors on patients' understanding of their preadmission medication regimen. We hypothesized that lower health literacy would be independently associated with lower medication understanding as assessed using the MUQ.

METHODS

The present study was a cross‐sectional assessment conducted using baseline interview data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) Study (ClinicalTrials.gov Registration #NCT00632021; available at: http://clinicaltrials.gov/show/NCT00632021). The PILL‐CVD Study is a randomized controlled trial of a pharmacist‐based intervention, consisting of pharmacist‐assisted medication reconciliation, inpatient counseling, low‐literacy adherence aids, and postdischarge telephone follow‐up. It was conducted at 2 academic medical centersVanderbilt University Hospital (VUH) in Nashville, Tennessee, and Brigham and Women's Hospital (BWH) in Boston, Massachusetts.9 This study was approved by the Institutional Review Board at each site, and all participants provided written informed consent.

RESULTS

Predictors of Medication Understanding

Unadjusted relationships of health literacy, cognition, and number of medications with medication understanding are shown in Figure 1 (panels A, B, and C, respectively). The figure demonstrates a linear relationship with both health literacy (Figure 1A) and cognition (Figure 1B), and a nonlinear relationship between number of preadmission medications and MUQ score (Figure 1C).

Figure 1

Adjusted relationships using imputed data for missing covariates are shown in Figure 2. Lower health literacy, cognitive impairment, male gender, and black race were independently associated with lower understanding of preadmission medications. Each 1 point increase in s‐TOFHLA or Mini‐Cog score led to an increase in medication understanding (OR = 1.04; 95% CI, 1.02 to 1.06; P = 0.0001; and OR = 1.24; 95% CI, 1.1 to 1.4; P = 0.001; respectively). Patients with marginal or inadequate health literacy had lower odds of understanding their regimen (OR = 0.53; 95% CI, 0.34 to 0.84; and OR = 0.49; 95% CI, 0.31 to 0.78, respectively) compared to those with adequate health literacy. Impaired cognitive function (Mini‐Cog score <3, indicating dementia) was also associated with lower odds of medication understanding (OR = 0.57; 95% CI, 0.38 to 0.86) compared to those with no cognitive impairment. An increase in the number of preadmission medications (up to 10) was also strongly associated with lower MUQ scores. For each increase by 1 medication, there was a significant decrease in medication understanding, up to 10 medications, beyond which understanding did not significantly decrease further. Patients on 6 medications were about half as likely to understand their medication regimen as patients on only 1 medication (OR = 0.52; 95% CI, 0.36 to 0.75). For patients on 11 medications, the odds of medication understanding were 24% lower than for patients on 6 medications (OR = 0.76; 95% CI, 0.65 to 0.89). Patients' age, years of schooling, and household income were not independently associated with medication understanding. Results were similar using data without multiple imputation.

Figure 2

DISCUSSION

We used a novel four‐component medication understanding questionnaire, developed for this study, to assess patients' understanding of up to 5 drugs selected randomly from the participant's preadmission medication list. The MUQ proved to be easy to administer by nonmedical staff within a short period of time (approximately 5 minutes per patient). It was well understood by patients. By limiting the assessment to 5 or fewer medications, the MUQ has a distinct advantage over existing measures of medication understanding that require testing the entire regimen. We did not find any limitations related to cutting off the assessment at 5 medications. In addition, this tool affords assessment of medication understanding without requiring medication bottles be present, enhancing its utility in the inpatient setting.

MUQ scores were associated with health literacy and other patient characteristics in an expected manner. We demonstrated that inadequate or marginal health literacy, as well as impaired cognitive function, were associated with low medication understanding. We also were able to demonstrate a relationship between increasing number of medications and lower medication understanding. Interestingly, in our patient population, understanding continued to decrease until reaching 10 medications, beyond which further increases in the number of medications had no additional detrimental effect on medication understanding. This nonlinear relationship between number of medications and medication understanding has potential implications for prescribing practice.

Our findings which utilize the MUQ among inpatients are consistent with prior literature in other settings.2, 7, 8 In a previous outpatient study, we identified that health literacy plays an important role in a patient's ability to successfully report and manage their daily medications.2 Other studies have also shown that patients with low health literacy have more difficulty understanding prescription drug information, and that they often experience medication‐related problems after hospital discharge.15, 16 The number and often the types of medications an individual takes have also been shown to increase the risk for adverse events and nonadherence to the treatment plan.1720 We postulate that this risk of adverse drug events is related at least in part to a patient's understanding of their medication regimen.

There are several limitations to this study. First, the MUQ did not assess certain aspects of medication understanding, such as knowledge of pill appearance or side effects, nor did it assess components of patients' actual drug‐taking behavior, such as organization of medications or behavioral cues. Thus, adaptive behaviors that patients may perform to improve their medication management, such as writing on labels or memory cues, are not captured by this test. Second, in administering and scoring the MUQ, we used the patient's preadmission medication list documented in the EHR as the reference standard. This was the best available reference list, and was generally accurate, as both hospitals had medication reconciliation systems in use at the time of the study21; nevertheless, it may contain inaccuracies. Documentation for certain medications, such as warfarin, in which dose can change frequently, often did not reflect the latest prescribed dose. In such cases, we scored the patient's answer as correct if the dose appeared reasonable and appropriate to the clinical pharmacist. As a result, a patient's MUQ score may have been overestimated in these cases.

Additional research will be needed to further validate the MUQ in other settings. In particular, studies should establish the relationship between the MUQ, serious medication errors after discharge, and potential to benefit from educational interventions. Also, as noted above, the nonlinear relationship between number of medications and medication understanding should be confirmed in other studies.

In conclusion, we demonstrated that patients with low health literacy, impaired cognition, or a higher number of medications had significantly poorer understanding of their preadmission medication regimen. These findings have important clinical implications. It would be appropriate to exercise greater caution when taking a medication history from patients who cannot readily provide the purpose, strength, units, and frequency of their medications. Attempts to validate the information obtained from patients with other sources of data, such as family members, inpatient or outpatient health records, and community pharmacy records should be considered. Patients at high risk for poor medication understanding, either measured directly using the MUQ or identified via risk factors such as polypharmacy, low cognition, or low health literacy, may warrant more intensive medication reconciliation interventions and/or educational counseling and follow‐up to prevent postdischarge adverse drug events. Further research is needed to determine if targeting these populations for interventions improves medication safety during transitions in care.

Recent studies have shown that a patient's discharge from the hospital is a vulnerable period for patient safety.14 With the reduction in length of stay (LOS) and the increase in patient acuity over the past decade, patients are discharged from acute care settings quicker and sicker, resulting in management of ongoing illness in a less‐monitored environment.5, 6 In addition, in teaching hospitals, residents are supervised by hospital‐based physicians who are rarely the primary care physician (PCP) for the residents' patients, which creates discontinuity of care.

One in 5 medical discharges is complicated by an adverse event believed, in part, to be due to poor communication between caregivers during this transition time.2 Discharge summaries, a key form of that communication, are not always done in a timely fashion and may lack key pieces of information.7, 8 For approximately 68% of patient discharges, the PCP will not have a discharge summary available for the patient's first follow‐up visit.911 In a survey of PCPs whose patients were in the hospital, only 23% reported direct communication with the hospital care team.12 This leaves PCPs unaware of pending test results or recommended follow‐up evaluations.10, 11, 13, 14 All of these factors are believed to contribute to adverse events, emergency department (ED) visits, and readmissions.

A recently published consensus statement on transitions of care by 6 major medical societies emphasizes the need for timely communication and transfer of information.15 These important processes are especially challenging to meet at academic medical centers, where discharge summaries and transition communication are done by residents in a hectic and challenging work environment, with multiple simultaneous and competing demands including outpatient clinic and required conferences.12 Residents have little formal training in how to write an effective discharge summary or how to systematically approach discharge planning. One study found higher error rates in discharge summaries written by residents compared with attending physicians.16 While the Accreditation Council for Graduate Medical Education (ACGME) limits the number of admissions per intern for both patient safety and educational reasons, the number of discharges per day is not limited despite the considerable amount of time required for appropriate discharge planning and communication.

Many interventions have been tried to improve the discharge process and reduce patient adverse events.17 Arranging early follow‐up appointments to reduce emergency department visits and readmissions has shown mixed results.13, 1820 Interventions that focus on specific populations, such as the elderly or patients with congestive heart failure, have been more successful.2123 Some interventions employed additional resources, such as a discharge form, transition coach, or discharge advocate, again with varying impact on results.18, 2427 A recent study by Jack et al. used nurse discharge advocates (DAs) to help with discharge planning and communication at an academic medical center.25 These DAs were independent of the care team, and focused on patient education and follow‐up plans, and reduced hospital reutilization in a selected population.

No studies have assessed the potential benefit of helping residents with the physician components of the discharge process. Prior studies have mainly focused on patient communication and follow‐up appointments, yet safe transitions also involve timely discharge summaries, physician‐to‐physician communication, physician‐to‐nurse communication, and medication reconciliation. Without support and time, these tasks can be very challenging for resident physicians with work‐hour limitations. We undertook a randomized, controlled trial to evaluate the impact on the discharge process of embedding a discharge facilitator in a resident medical team to help with the physician discharge process. We studied the effect for all the patients discharged from the resident team, rather than focusing on a select group or patients with a single diagnosis.

METHODS

RESULTS

Study Sample

During the 5‐month intervention (November 12, 2008 to April 14, 2009), a combined total of 999 patients were admitted to the intervention and control general medical teams. We excluded 96 patients who were not discharged but transferred to another service or intensive care units, and 24 patients who died. We also excluded 7 patients who were discharged from both teams the first day of the study, because the DF was not involved with the patients' discharge planning. That left 872 patients discharged to either home, a facility, or having left against medical advice (AMA) included in the study: 440 patients on the intervention team and 432 patients on the control team (Figure 1). Baseline patient demographic and clinical characteristics were similar across both teams with only gender being significantly different (Table 1). The mean age was 63 years (range, 1896) and the mean comorbidity score was 2.3 (range, 012). Of note, about a quarter of patients were discharged to facilities, about half were Medicare recipients, and approximately 80% had a PCP. The DF participated in the discharge process for nearly all of the intervention patients; she reported contributing approximately 50% of the content to the discharge summaries.

Figure 1

Table 1.Baseline Participant Characteristics

Characteristics	Intervention Team	Control Team
	n = 440	n = 432
Abbreviations: AMA, against medical advice; COPD, chronic obstructive pulmonary disease; PCP, primary care physician; SD, standard deviation. * P < 0.05; no other comparisons were statistically significant. Deyo Modification of the Charlson Comorbidity Index.
Mean age (SD), year	63 (18)	63 (18)
Women, n (%)*	181 (41)	207 (48)
Race, n (%)
White non‐Hispanic	267 (61)	243 (56)
Black non‐Hispanic	24 (5)	33 (8)
Hispanic	21 (5)	17 (4)
Unknown/other	128 (29)	139 (32)
Health insurance, n (%)
Medicare	213 (48)	226 (52)
Medicaid	85 (19)	81 (19)
Private	110 (25)	91 (21)
Other	32 (7)	34 (8)
PCP on admission, n (%)	370 (84)	356 (82)
Discharge disposition, n (%)
AMA	12 (3)	14 (3)
Home	305 (69)	315 (73)
Facility	123 (28)	103 (24)
Mean comorbidity index score (SD)	2.3 (2.4)	2.3 (2.4)
Diagnoses
Congestive heart failure	30 (6%)	27 (5%)
COPD/asthma	34 (7%)	47 (9%)
Cardiovascular disease	54 (11%)	50 (8%)
Alcohol/substance abuse	29 (6%)	34 (7%)
Gastrointestinal bleeds/ulcers	38 (8%)	41 (8%)
Hepatobiliary disease	30 (6%)	36 (7%)
Renal failure/kidney disease	33 (7%)	37 (7%)
Pneumonia	36 (7%)	22 (4%)
Musculoskeletal disease	26 (5%)	23 (5%)
Neurologic disease	22 (4%)	25 (5%)
Other	163 (33%)	172 (35%)

DISCUSSION

Our study embedded a nurse practitioner on a busy resident general medical team to help with all aspects of the discharge process for which physicians are responsible. Previous studies have been limited to patients with specific diagnoses, age, or disposition plans.1825 In this study, we included all general medical patients. Our intervention improved several important quality of care elements: the timeliness of completion of discharge summaries; and increased number of early follow‐up appointments, with more patients seen within 2 and 4 weeks after discharge. Patients reported better understanding of their follow‐up plans and more satisfaction with the discharge process. While not statistically significant, there was a trend towards better communication with nurses. For residents with work‐hour limitations, there was time savings with a trend towards finishing attending rounds on time and statistically significant earlier sign‐out rounds (46 minutes earlier). This intervention had no effect on patient length of stay, readmissions, or emergency department visits in the 30 days after discharge.

Despite improving many aspects of the discharge process and communication that have previously been raised as areas of concern for patient safety, there was no improvement in readmissions rates and ED utilization which are often used as the quality indicators for effective discharge planning. Similar types of interventions on general medical patients have generally also failed to show improvement in readmission rates.1820, 25 Weinberger et al. arranged follow‐up appointments within 1 week for patients discharged from a Veterans Administrative hospital; while patients were seen more often, the intervention actually increased readmission rates.20 Fitzgerald et al. had a case manager contact patients at home and encourage follow‐up, which increased follow‐up visits, but again had no effect on readmission.19 Einstadter et al. had a nurse case manager coordinate outpatient follow‐up on a resident team and also did not effect readmission rates or ED visits.18 Jack et al. in project reengineered discharge (RED) did show a significant reduction in combined hospital utilization measures. However, their study focused on a more limited patient population, and employed both a discharge advocate to arrange follow‐up and improve patient education, and a pharmacist to make postdischarge phone calls.25

So why did readmissions rates and ED visits not change in our study? It would be reasonable to think that having earlier follow‐up appointments, better and timely physician‐to‐physician communication, and a facilitator for patient questions should improve the quality of the discharge process. In a recent study, Jha et al. found there was no association between chart‐based measures of discharge quality and readmissions rates, and only a modest association for patient‐reported measures of discharge quality and readmission rates.28 The authors suggest readmission rates are driven by many factors beyond just improved discharge safety. Perhaps readmission rates are too complex a measure to use to assess discharge process improvement. For fiscal reasons, it is understandable that hospitals, insurance companies, and the Centers for Medicare and Medicaid want to reduce readmission rates and ED utilization. Jencks et al. noted the cost of readmissions in 2004 was 17.4 billion dollars.29 However, sweeping efforts to improve the discharge process for all general medical patients may not yield significant reductions in readmissions, as this study suggests. We may need to focus aggressive intervention on smaller target populations, as prior studies on focused groups suggest.2123

There are no evidence‐based studies to suggest when optimal follow‐up should occur after discharge.26 Several medical society guidelines recommend 2 weeks. More patients on the intervention team were seen within 2 weeks, but readmission rates were not affected. The University Health System Consortium recently reported that the majority of readmissions occurred within 6 days, with the average being about 2 to 3 days.30 In this study, the median days to readmit were 12 for the intervention team and 10 for the control. It is possible that even with our improved 2‐week follow‐up, this was not early enough to reduce readmissions. Follow‐up may need to be within 13 days of discharge for highly vulnerable patients, to significantly change readmission rates. Further studies focusing on this question would be helpful.

Finally, with ACGME limitation of work hours, many residency programs are looking for ways to reduce residents' workload and increase time for education. With a significant trend towards finishing attending rounds on time, it is likely that more residents on the intervention team were able to attend the noon‐time educational conferences. We speculate that this was due to fewer interruptions during rounds because the DF was available for nurses' questions. Sign‐out rounds occurred significantly earlier, possibly because of improved resident efficiency due to the DF's help with the discharge process. While residents may lose some educational experience from not performing all discharge tasks, they gain experience working in interdisciplinary teams, have increased time for education, and reduced work hours. Since the ACGME limits the number of residents per program and increasing the residency size is not an option, a DF should be considered as a possible solution to ACGME work‐hour restrictions.

This study had several limitations. First, the intervention team had 1 specific person embedded, and therefore the results of this study may have limited generalizability. Second, the limited number of residents working with the DF could have biased the intervention, as not all residents worked equally well with the DF. However, this may represent the real‐world experience on any teaching service, given variation in working styles and learning curves of residents over their training. Third, this study was done at 1 university‐affiliated urban Academic Medical Center, making it potentially less generalizable to resident teams in community hospitals. Fourth, we were not able to capture readmissions and ED visits at institutions outside the MGHPartners Healthcare System. However, given that patients were assigned at random to either team, this factor should have impacted both teams equally. Fifth, the study occurred during Massachusetts healthcare reform which requires everyone to have health insurance. This may have affected the rates of ED visits and readmission rates, especially with a shortage of primary care physicians and office visits. Finally, this intervention was not cost‐neutral. Paying for a nurse practitioner to help residents with the work of discharge and providing patients with additional services had many advantages, but this quality improvement project did not pay for itself through shorter LOS, or decreases in ED visits or readmissions.

While readmission rates and ED utilization are important patient outcomes, especially in the current healthcare climate, what determines readmissions and ED visits is likely complex and multifactorial. This study suggests that, in the nationwide effort to reduce readmissions, solely improving the discharge process for all general medical patients may not produce the hoped‐for financial savings. Improving the discharge process, however, is something valuable in its own right. Adding a DF to a resident team does improve some quality markers of the discharge process and decreases work hours for residents.