Sunil Kripalani, MD, MSc

Containing the rise of healthcare costs has taken on a new sense of urgency in the wake of the recent economic recession and continued growth in the cost of healthcare. Accordingly, many stakeholders seek solutions to improve value (reducing costs while improving care)[1]; hospital readmissions, which are common and costly,[2] have emerged as a key target. The Centers for Medicare and Medicaid Services (CMS) have instituted several programs intended to reduce readmissions, including funding for community‐based, care‐transition programs; penalties for hospitals with elevated risk‐adjusted readmission rates for selected diagnoses; pioneer Accountable Care Organizations (ACOs) with incentives to reduce global costs of care; and Hospital Engagement Networks (HENs) through the Partnership for Patients.[3] A primary aim of these initiatives is to enhance the quality of care transitions as patients are discharged from the hospital.

Though the recent focus on hospital readmissions has appropriately drawn attention to transitions in care, some have expressed concerns. Among these are questions about: 1) the extent to which readmissions truly reflect the quality of hospital care[4]; 2) the preventability of readmissions[5]; 3) limitations in risk‐adjustment techniques[6]; and 4) best practices for preventing readmissions.[7] We believe these concerns stem in part from deficiencies in the state of the science of transitional care, and that future efforts in this area will be hindered without a clear vision of an ideal transition in care. We propose the key components of an ideal transition in care and discuss the implications of this concept as it pertains to hospital readmissions.

THE IDEAL TRANSITION IN CARE

We propose the key components of an ideal transition in care in Figure 1 and Table 1. Figure 1 represents 10 domains described more fully below as structural supports of the bridge patients must cross from one care environment to another during a care transition. This figure highlights key domains and suggests that lack of a domain makes the bridge weaker and more prone to gaps in care and poor outcomes. It also implies that the more components are missing, the less safe is the bridge or transition. Those domains that mainly take place prior to discharge are placed closer to the hospital side of the bridge, those that mainly take place after discharge are placed closer to the community side of the bridge, while those that take place both prior to and after discharge are in the middle. Table 1 provides descriptions of the key content for each of these domains, as well as guidance about which personnel might be involved and where in the transition process that domain should be implemented. We support these domains with supporting evidence where available.

Figure 1

Our concept of an ideal transition in care began with work by Naylor, who described several important components of a safe transition in care, including complete communication of information, patient education, enlisting the help of social and community supports, ensuring continuity of care, and coordinating care among team members.[8] It is supplemented by the Transitions of Care Consensus Policy Statement proposed by representatives from hospital medicine, primary care, and emergency medicine, which emphasized aspects of timeliness and content of communication between providers.[9] Our present articulation of these key components includes 10 organizing domains.

The Discharge Planning domain highlights the important principle of planning ahead for hospital discharge while the patient is still being treated in the hospital, a paradigm espoused by Project RED[10] and other successful care transitions interventions.[11, 12] Collaborating with the outpatient provider and taking the patient and caregiver's preferences for appointment scheduling into account can help ensure optimal outpatient follow‐up.

Complete Communication of Information refers to the content that should be included in discharge summaries and other means of information transfer from hospital to postdischarge care. The specific content areas are based on the Society of Hospital Medicine and Society of General Internal Medicine Continuity of Care Task Force systematic review and recommendations,[13] which takes into account information requested by primary care physicians after discharge.

Availability, Timeliness, Clarity, and Organization of that information is as important as the content because postdischarge providers must be able to access and quickly understand the information they have been provided before assuming care of the patient.[14, 15]

The Medication Safety domain is of central importance because medications are responsible for most postdischarge adverse events.[16] Taking an accurate medication history,[17] reconciling changes throughout the hospitalization,[18] and communicating the reconciled medication regimen to patients and providers across transitions of care can reduce medication errors and improve patient safety.[19, 20, 21, 22]

The Patient Education and Promotion of Self‐Management domain involves teaching patients and their caregivers about the main hospital diagnoses and instructions for self‐care, including medication changes, appointments, and whom to contact if issues arise. Confirming comprehension of instructions through assessments of acute (delirium) and chronic (dementia) cognitive impairments[23, 24, 25, 26] and teach‐back from the patient (or caregiver) is an important aspect of such counseling, as is providing patients and caregivers with educational materials that are appropriate for their level of health literacy and preferred language.[14] High‐risk patients may benefit from patient coaching to improve their self‐management skills.[12] These recommendations are based on years of health literacy research,[27, 28, 29] and such elements are generally included in effective interventions (including Project RED,[10] Naylor and colleagues' Transitional Care Model,[11] and Coleman and colleagues' Care Transitions Intervention[12]).

Enlisting the help of Social and Community Supports is an important adjunct to medical care and is the rationale for the recent increase in CMS funding for community‐based, care‐transition programs. These programs are crucial for assisting patients with household activities, meals, and other necessities during the period of recovery, though they should be distinguished from care management or care coordination interventions, which have not been found to be helpful in preventing readmissions unless high touch in nature.[30, 31]

The Advanced Care Planning domain may begin in the hospital or outpatient setting, and involves establishing goals of care and healthcare proxies, as well as engaging with palliative care or hospice services if appropriate. Emerging evidence supports the intuitive conclusion that this approach prevents readmissions, particularly in patients who do not benefit from hospital readmission.[32, 33]

Attention to the Coordinating Care Among Team Members domain is needed to synchronize efforts across settings and providers. Clearly, many healthcare professionals as well as other involved parties can be involved in helping a single patient during transitions in care. It is vital that they coordinate information, assessments, and plans as a team.[34]

We recognize the domain of Monitoring and Managing Symptoms After Discharge as increasingly crucial as reflected in our growing understanding of the reasons for readmission, especially among patients with fragile conditions such as heart failure, chronic lung disease, gastrointestinal disorders, dementia,[23, 24, 25, 26] and vascular disease.[35] Monitoring for new or worsening symptoms; medication side effects, discrepancies, or nonadherence; and other self‐management challenges will allow problems to be detected and addressed early, before they result in unplanned healthcare utilization. It is noteworthy that successful interventions in this regard rely on in‐home evaluation[13, 14, 29] by nurses rather than telemonitoring, which in isolation has not been effective to date.[36, 37]

Finally, optimal Outpatient Follow‐Up with appropriate postdischarge providers is crucial for providing ideal transitions. These appointments need to be prompt[38, 39] (eg, within 7 days if not sooner for high‐risk patients) and with providers who have a longitudinal relationship to the patient, as prior work has shown increased readmissions when the provider is unfamiliar with the patient.[40] The advantages and disadvantages of hospitalist‐run postdischarge clinics as one way to increase access and expedite follow‐up are currently being explored. Although the optimal content of a postdischarge visit has not been defined, logical tasks to be completed are myriad and imply the need for checklists, adequate time, and a multidisciplinary team of providers.[41]

IMPLICATIONS OF THE IDEAL TRANSITION IN CARE

Our conceptualization of an ideal transition in care provides insight for hospital and healthcare system leadership, policymakers, researchers, clinicians, and educators seeking to improve transitions of care and reduce hospital readmissions. In the sections below, we briefly review commonly cited concerns about the recent focus on readmissions as a quality measure, illustrate how the Ideal Transition in Care addresses these concerns, and propose fruitful areas for future work.

NEXT STEPS

For hospital and healthcare system leaders, who need to take action now to avoid financial penalties, we recommend starting with proven, high‐touch interventions such as Project RED and the Care Transitions Intervention, which are durable, cost‐effective, robustly address multiple domains of the Ideal Transition in Care, and have been implemented at numerous sites.[54, 55] Each hospital or group will need to decide on a bundle of interventions and customize them based on local workflow, resources, and culture.

Risk‐stratification, to match the intensity of the intervention to the risk of readmission of the patient, will undoubtedly be a key component for the efficient use of resources. We anticipate future research will allow risk stratification to be a robust part of any implementation plan. However, as noted above, current risk prediction models are imperfect,[6] and more work is needed to determine which patients benefit most from which interventions. Few if any studies have described interventions tailored to risk for this reason.

Based on our ideal transition in care, our collective experience, and published evidence,[7, 10, 11, 12] potential elements to start with include: early discharge planning; medication reconciliation[56]; patient/caregiver education using health literacy principles, cognitive assessments, and teach‐back to confirm understanding; synchronous communication (eg, by phone) between inpatient and postdischarge providers; follow‐up phone calls to patients within 72 hours of discharge; 24/7 availability of a responsible inpatient provider to address questions and problems (both from the patient/caregiver and from postdischarge providers); and prompt appointments for patients discharged home. High‐risk patients will likely require additional interventions, including in‐home assessments, disease‐monitoring programs, and/or patient coaching. Lastly, patients with certain conditions prone to readmission (such as heart failure and chronic obstructive pulmonary disease) may benefit from disease‐specific programs, including patient education, outpatient disease management, and monitoring protocols.

It is likely that the most effective interventions are those that come from combined, coordinated interventions shared between inpatient and outpatient settings, and are intensive in nature. We expect that the more domains in the framework that are addressed, the safer and more seamless transitions in care will be, with improvement in patient outcomes. To the extent that fragmentation of care has been a barrier to the implementation of these types of interventions in the past, ACOs, perhaps with imbedded Patient‐Centered Medical Homes, may be in the best position to take advantage of newly aligned financial incentives to design comprehensive transitional care. Indeed, we anticipate that Figure 1 may provide substrate for a discussion of postdischarge care and division of responsibilities between inpatient and outpatient care teams at the time of transition, so effort is not duplicated and multiple domains are addressed.

Other barriers to implementation of ideal transitions in care will continue to be an issue for most healthcare systems. Financial constraints that have been a barrier up until now will be partially overcome by penalties for high readmission rates and by ACOs, bundled payments, and alternative care contracts (ie, global payments), but the extent to which each institution feels rewarded for investing in transitional interventions will vary greatly. Healthcare leadership that sees the value of improving transitions in care will be critical to overcoming this barrier. Competing demands (such as lowering hospital length of stay and carrying out other patient care responsibilities),[57] lack of coordination and diffusion of responsibility among various clinical personnel, and lack of standards are other barriers[58] that will require clear prioritization from leadership, policy changes, team‐based care, provider education and feedback, and adequate allocation of personnel resources. In short, process redesign using continuous quality improvement efforts and effective tools will be required to maximize the possibility of success.

CONCLUSIONS

Readmissions are costly and undesirable. Intuition suggests they are a marker of poor care and that hospitals should be capable of reducing them, thereby improving care and decreasing costs. In a potential future world of ACOs based on global payments, financial incentives would be aligned for each system to reduce readmissions below their current baseline, therefore obviating the need for external financial rewards and penalties. In the meantime, financial penalties do exist, and controversy exists over their fairness and likelihood of driving appropriate behavior. To address these controversies and promote better transitional care, we call for the development and use of multifaceted, collaborative transitions interventions that span settings, risk‐adjustment models that allow for fairer comparisons among hospitals, better and more widespread measurement of processes of transitional care, a better understanding of what interventions are most effective and in whom, and better guidance in how to implement these interventions. Our conceptualization of an ideal transition of care serves as a guide and provides a common vocabulary for these efforts. Such research is likely to produce the knowledge needed for healthcare systems to improve transitions in care, reduce readmissions, and reduce costs.

Containing the rise of healthcare costs has taken on a new sense of urgency in the wake of the recent economic recession and continued growth in the cost of healthcare. Accordingly, many stakeholders seek solutions to improve value (reducing costs while improving care)[1]; hospital readmissions, which are common and costly,[2] have emerged as a key target. The Centers for Medicare and Medicaid Services (CMS) have instituted several programs intended to reduce readmissions, including funding for community‐based, care‐transition programs; penalties for hospitals with elevated risk‐adjusted readmission rates for selected diagnoses; pioneer Accountable Care Organizations (ACOs) with incentives to reduce global costs of care; and Hospital Engagement Networks (HENs) through the Partnership for Patients.[3] A primary aim of these initiatives is to enhance the quality of care transitions as patients are discharged from the hospital.

Though the recent focus on hospital readmissions has appropriately drawn attention to transitions in care, some have expressed concerns. Among these are questions about: 1) the extent to which readmissions truly reflect the quality of hospital care[4]; 2) the preventability of readmissions[5]; 3) limitations in risk‐adjustment techniques[6]; and 4) best practices for preventing readmissions.[7] We believe these concerns stem in part from deficiencies in the state of the science of transitional care, and that future efforts in this area will be hindered without a clear vision of an ideal transition in care. We propose the key components of an ideal transition in care and discuss the implications of this concept as it pertains to hospital readmissions.

THE IDEAL TRANSITION IN CARE

We propose the key components of an ideal transition in care in Figure 1 and Table 1. Figure 1 represents 10 domains described more fully below as structural supports of the bridge patients must cross from one care environment to another during a care transition. This figure highlights key domains and suggests that lack of a domain makes the bridge weaker and more prone to gaps in care and poor outcomes. It also implies that the more components are missing, the less safe is the bridge or transition. Those domains that mainly take place prior to discharge are placed closer to the hospital side of the bridge, those that mainly take place after discharge are placed closer to the community side of the bridge, while those that take place both prior to and after discharge are in the middle. Table 1 provides descriptions of the key content for each of these domains, as well as guidance about which personnel might be involved and where in the transition process that domain should be implemented. We support these domains with supporting evidence where available.

Figure 1

Our concept of an ideal transition in care began with work by Naylor, who described several important components of a safe transition in care, including complete communication of information, patient education, enlisting the help of social and community supports, ensuring continuity of care, and coordinating care among team members.[8] It is supplemented by the Transitions of Care Consensus Policy Statement proposed by representatives from hospital medicine, primary care, and emergency medicine, which emphasized aspects of timeliness and content of communication between providers.[9] Our present articulation of these key components includes 10 organizing domains.

The Discharge Planning domain highlights the important principle of planning ahead for hospital discharge while the patient is still being treated in the hospital, a paradigm espoused by Project RED[10] and other successful care transitions interventions.[11, 12] Collaborating with the outpatient provider and taking the patient and caregiver's preferences for appointment scheduling into account can help ensure optimal outpatient follow‐up.

Complete Communication of Information refers to the content that should be included in discharge summaries and other means of information transfer from hospital to postdischarge care. The specific content areas are based on the Society of Hospital Medicine and Society of General Internal Medicine Continuity of Care Task Force systematic review and recommendations,[13] which takes into account information requested by primary care physicians after discharge.

Availability, Timeliness, Clarity, and Organization of that information is as important as the content because postdischarge providers must be able to access and quickly understand the information they have been provided before assuming care of the patient.[14, 15]

The Medication Safety domain is of central importance because medications are responsible for most postdischarge adverse events.[16] Taking an accurate medication history,[17] reconciling changes throughout the hospitalization,[18] and communicating the reconciled medication regimen to patients and providers across transitions of care can reduce medication errors and improve patient safety.[19, 20, 21, 22]

The Patient Education and Promotion of Self‐Management domain involves teaching patients and their caregivers about the main hospital diagnoses and instructions for self‐care, including medication changes, appointments, and whom to contact if issues arise. Confirming comprehension of instructions through assessments of acute (delirium) and chronic (dementia) cognitive impairments[23, 24, 25, 26] and teach‐back from the patient (or caregiver) is an important aspect of such counseling, as is providing patients and caregivers with educational materials that are appropriate for their level of health literacy and preferred language.[14] High‐risk patients may benefit from patient coaching to improve their self‐management skills.[12] These recommendations are based on years of health literacy research,[27, 28, 29] and such elements are generally included in effective interventions (including Project RED,[10] Naylor and colleagues' Transitional Care Model,[11] and Coleman and colleagues' Care Transitions Intervention[12]).

Enlisting the help of Social and Community Supports is an important adjunct to medical care and is the rationale for the recent increase in CMS funding for community‐based, care‐transition programs. These programs are crucial for assisting patients with household activities, meals, and other necessities during the period of recovery, though they should be distinguished from care management or care coordination interventions, which have not been found to be helpful in preventing readmissions unless high touch in nature.[30, 31]

The Advanced Care Planning domain may begin in the hospital or outpatient setting, and involves establishing goals of care and healthcare proxies, as well as engaging with palliative care or hospice services if appropriate. Emerging evidence supports the intuitive conclusion that this approach prevents readmissions, particularly in patients who do not benefit from hospital readmission.[32, 33]

Attention to the Coordinating Care Among Team Members domain is needed to synchronize efforts across settings and providers. Clearly, many healthcare professionals as well as other involved parties can be involved in helping a single patient during transitions in care. It is vital that they coordinate information, assessments, and plans as a team.[34]

We recognize the domain of Monitoring and Managing Symptoms After Discharge as increasingly crucial as reflected in our growing understanding of the reasons for readmission, especially among patients with fragile conditions such as heart failure, chronic lung disease, gastrointestinal disorders, dementia,[23, 24, 25, 26] and vascular disease.[35] Monitoring for new or worsening symptoms; medication side effects, discrepancies, or nonadherence; and other self‐management challenges will allow problems to be detected and addressed early, before they result in unplanned healthcare utilization. It is noteworthy that successful interventions in this regard rely on in‐home evaluation[13, 14, 29] by nurses rather than telemonitoring, which in isolation has not been effective to date.[36, 37]

Finally, optimal Outpatient Follow‐Up with appropriate postdischarge providers is crucial for providing ideal transitions. These appointments need to be prompt[38, 39] (eg, within 7 days if not sooner for high‐risk patients) and with providers who have a longitudinal relationship to the patient, as prior work has shown increased readmissions when the provider is unfamiliar with the patient.[40] The advantages and disadvantages of hospitalist‐run postdischarge clinics as one way to increase access and expedite follow‐up are currently being explored. Although the optimal content of a postdischarge visit has not been defined, logical tasks to be completed are myriad and imply the need for checklists, adequate time, and a multidisciplinary team of providers.[41]

IMPLICATIONS OF THE IDEAL TRANSITION IN CARE

Our conceptualization of an ideal transition in care provides insight for hospital and healthcare system leadership, policymakers, researchers, clinicians, and educators seeking to improve transitions of care and reduce hospital readmissions. In the sections below, we briefly review commonly cited concerns about the recent focus on readmissions as a quality measure, illustrate how the Ideal Transition in Care addresses these concerns, and propose fruitful areas for future work.

NEXT STEPS

For hospital and healthcare system leaders, who need to take action now to avoid financial penalties, we recommend starting with proven, high‐touch interventions such as Project RED and the Care Transitions Intervention, which are durable, cost‐effective, robustly address multiple domains of the Ideal Transition in Care, and have been implemented at numerous sites.[54, 55] Each hospital or group will need to decide on a bundle of interventions and customize them based on local workflow, resources, and culture.

Risk‐stratification, to match the intensity of the intervention to the risk of readmission of the patient, will undoubtedly be a key component for the efficient use of resources. We anticipate future research will allow risk stratification to be a robust part of any implementation plan. However, as noted above, current risk prediction models are imperfect,[6] and more work is needed to determine which patients benefit most from which interventions. Few if any studies have described interventions tailored to risk for this reason.

Based on our ideal transition in care, our collective experience, and published evidence,[7, 10, 11, 12] potential elements to start with include: early discharge planning; medication reconciliation[56]; patient/caregiver education using health literacy principles, cognitive assessments, and teach‐back to confirm understanding; synchronous communication (eg, by phone) between inpatient and postdischarge providers; follow‐up phone calls to patients within 72 hours of discharge; 24/7 availability of a responsible inpatient provider to address questions and problems (both from the patient/caregiver and from postdischarge providers); and prompt appointments for patients discharged home. High‐risk patients will likely require additional interventions, including in‐home assessments, disease‐monitoring programs, and/or patient coaching. Lastly, patients with certain conditions prone to readmission (such as heart failure and chronic obstructive pulmonary disease) may benefit from disease‐specific programs, including patient education, outpatient disease management, and monitoring protocols.

It is likely that the most effective interventions are those that come from combined, coordinated interventions shared between inpatient and outpatient settings, and are intensive in nature. We expect that the more domains in the framework that are addressed, the safer and more seamless transitions in care will be, with improvement in patient outcomes. To the extent that fragmentation of care has been a barrier to the implementation of these types of interventions in the past, ACOs, perhaps with imbedded Patient‐Centered Medical Homes, may be in the best position to take advantage of newly aligned financial incentives to design comprehensive transitional care. Indeed, we anticipate that Figure 1 may provide substrate for a discussion of postdischarge care and division of responsibilities between inpatient and outpatient care teams at the time of transition, so effort is not duplicated and multiple domains are addressed.

Other barriers to implementation of ideal transitions in care will continue to be an issue for most healthcare systems. Financial constraints that have been a barrier up until now will be partially overcome by penalties for high readmission rates and by ACOs, bundled payments, and alternative care contracts (ie, global payments), but the extent to which each institution feels rewarded for investing in transitional interventions will vary greatly. Healthcare leadership that sees the value of improving transitions in care will be critical to overcoming this barrier. Competing demands (such as lowering hospital length of stay and carrying out other patient care responsibilities),[57] lack of coordination and diffusion of responsibility among various clinical personnel, and lack of standards are other barriers[58] that will require clear prioritization from leadership, policy changes, team‐based care, provider education and feedback, and adequate allocation of personnel resources. In short, process redesign using continuous quality improvement efforts and effective tools will be required to maximize the possibility of success.

CONCLUSIONS

Readmissions are costly and undesirable. Intuition suggests they are a marker of poor care and that hospitals should be capable of reducing them, thereby improving care and decreasing costs. In a potential future world of ACOs based on global payments, financial incentives would be aligned for each system to reduce readmissions below their current baseline, therefore obviating the need for external financial rewards and penalties. In the meantime, financial penalties do exist, and controversy exists over their fairness and likelihood of driving appropriate behavior. To address these controversies and promote better transitional care, we call for the development and use of multifaceted, collaborative transitions interventions that span settings, risk‐adjustment models that allow for fairer comparisons among hospitals, better and more widespread measurement of processes of transitional care, a better understanding of what interventions are most effective and in whom, and better guidance in how to implement these interventions. Our conceptualization of an ideal transition of care serves as a guide and provides a common vocabulary for these efforts. Such research is likely to produce the knowledge needed for healthcare systems to improve transitions in care, reduce readmissions, and reduce costs.

Containing the rise of healthcare costs has taken on a new sense of urgency in the wake of the recent economic recession and continued growth in the cost of healthcare. Accordingly, many stakeholders seek solutions to improve value (reducing costs while improving care)[1]; hospital readmissions, which are common and costly,[2] have emerged as a key target. The Centers for Medicare and Medicaid Services (CMS) have instituted several programs intended to reduce readmissions, including funding for community‐based, care‐transition programs; penalties for hospitals with elevated risk‐adjusted readmission rates for selected diagnoses; pioneer Accountable Care Organizations (ACOs) with incentives to reduce global costs of care; and Hospital Engagement Networks (HENs) through the Partnership for Patients.[3] A primary aim of these initiatives is to enhance the quality of care transitions as patients are discharged from the hospital.

Though the recent focus on hospital readmissions has appropriately drawn attention to transitions in care, some have expressed concerns. Among these are questions about: 1) the extent to which readmissions truly reflect the quality of hospital care[4]; 2) the preventability of readmissions[5]; 3) limitations in risk‐adjustment techniques[6]; and 4) best practices for preventing readmissions.[7] We believe these concerns stem in part from deficiencies in the state of the science of transitional care, and that future efforts in this area will be hindered without a clear vision of an ideal transition in care. We propose the key components of an ideal transition in care and discuss the implications of this concept as it pertains to hospital readmissions.

THE IDEAL TRANSITION IN CARE

We propose the key components of an ideal transition in care in Figure 1 and Table 1. Figure 1 represents 10 domains described more fully below as structural supports of the bridge patients must cross from one care environment to another during a care transition. This figure highlights key domains and suggests that lack of a domain makes the bridge weaker and more prone to gaps in care and poor outcomes. It also implies that the more components are missing, the less safe is the bridge or transition. Those domains that mainly take place prior to discharge are placed closer to the hospital side of the bridge, those that mainly take place after discharge are placed closer to the community side of the bridge, while those that take place both prior to and after discharge are in the middle. Table 1 provides descriptions of the key content for each of these domains, as well as guidance about which personnel might be involved and where in the transition process that domain should be implemented. We support these domains with supporting evidence where available.

Figure 1

Our concept of an ideal transition in care began with work by Naylor, who described several important components of a safe transition in care, including complete communication of information, patient education, enlisting the help of social and community supports, ensuring continuity of care, and coordinating care among team members.[8] It is supplemented by the Transitions of Care Consensus Policy Statement proposed by representatives from hospital medicine, primary care, and emergency medicine, which emphasized aspects of timeliness and content of communication between providers.[9] Our present articulation of these key components includes 10 organizing domains.

The Discharge Planning domain highlights the important principle of planning ahead for hospital discharge while the patient is still being treated in the hospital, a paradigm espoused by Project RED[10] and other successful care transitions interventions.[11, 12] Collaborating with the outpatient provider and taking the patient and caregiver's preferences for appointment scheduling into account can help ensure optimal outpatient follow‐up.

Complete Communication of Information refers to the content that should be included in discharge summaries and other means of information transfer from hospital to postdischarge care. The specific content areas are based on the Society of Hospital Medicine and Society of General Internal Medicine Continuity of Care Task Force systematic review and recommendations,[13] which takes into account information requested by primary care physicians after discharge.

Availability, Timeliness, Clarity, and Organization of that information is as important as the content because postdischarge providers must be able to access and quickly understand the information they have been provided before assuming care of the patient.[14, 15]

The Medication Safety domain is of central importance because medications are responsible for most postdischarge adverse events.[16] Taking an accurate medication history,[17] reconciling changes throughout the hospitalization,[18] and communicating the reconciled medication regimen to patients and providers across transitions of care can reduce medication errors and improve patient safety.[19, 20, 21, 22]

The Patient Education and Promotion of Self‐Management domain involves teaching patients and their caregivers about the main hospital diagnoses and instructions for self‐care, including medication changes, appointments, and whom to contact if issues arise. Confirming comprehension of instructions through assessments of acute (delirium) and chronic (dementia) cognitive impairments[23, 24, 25, 26] and teach‐back from the patient (or caregiver) is an important aspect of such counseling, as is providing patients and caregivers with educational materials that are appropriate for their level of health literacy and preferred language.[14] High‐risk patients may benefit from patient coaching to improve their self‐management skills.[12] These recommendations are based on years of health literacy research,[27, 28, 29] and such elements are generally included in effective interventions (including Project RED,[10] Naylor and colleagues' Transitional Care Model,[11] and Coleman and colleagues' Care Transitions Intervention[12]).

Enlisting the help of Social and Community Supports is an important adjunct to medical care and is the rationale for the recent increase in CMS funding for community‐based, care‐transition programs. These programs are crucial for assisting patients with household activities, meals, and other necessities during the period of recovery, though they should be distinguished from care management or care coordination interventions, which have not been found to be helpful in preventing readmissions unless high touch in nature.[30, 31]

The Advanced Care Planning domain may begin in the hospital or outpatient setting, and involves establishing goals of care and healthcare proxies, as well as engaging with palliative care or hospice services if appropriate. Emerging evidence supports the intuitive conclusion that this approach prevents readmissions, particularly in patients who do not benefit from hospital readmission.[32, 33]

Attention to the Coordinating Care Among Team Members domain is needed to synchronize efforts across settings and providers. Clearly, many healthcare professionals as well as other involved parties can be involved in helping a single patient during transitions in care. It is vital that they coordinate information, assessments, and plans as a team.[34]

We recognize the domain of Monitoring and Managing Symptoms After Discharge as increasingly crucial as reflected in our growing understanding of the reasons for readmission, especially among patients with fragile conditions such as heart failure, chronic lung disease, gastrointestinal disorders, dementia,[23, 24, 25, 26] and vascular disease.[35] Monitoring for new or worsening symptoms; medication side effects, discrepancies, or nonadherence; and other self‐management challenges will allow problems to be detected and addressed early, before they result in unplanned healthcare utilization. It is noteworthy that successful interventions in this regard rely on in‐home evaluation[13, 14, 29] by nurses rather than telemonitoring, which in isolation has not been effective to date.[36, 37]

Finally, optimal Outpatient Follow‐Up with appropriate postdischarge providers is crucial for providing ideal transitions. These appointments need to be prompt[38, 39] (eg, within 7 days if not sooner for high‐risk patients) and with providers who have a longitudinal relationship to the patient, as prior work has shown increased readmissions when the provider is unfamiliar with the patient.[40] The advantages and disadvantages of hospitalist‐run postdischarge clinics as one way to increase access and expedite follow‐up are currently being explored. Although the optimal content of a postdischarge visit has not been defined, logical tasks to be completed are myriad and imply the need for checklists, adequate time, and a multidisciplinary team of providers.[41]

IMPLICATIONS OF THE IDEAL TRANSITION IN CARE

Our conceptualization of an ideal transition in care provides insight for hospital and healthcare system leadership, policymakers, researchers, clinicians, and educators seeking to improve transitions of care and reduce hospital readmissions. In the sections below, we briefly review commonly cited concerns about the recent focus on readmissions as a quality measure, illustrate how the Ideal Transition in Care addresses these concerns, and propose fruitful areas for future work.

NEXT STEPS

For hospital and healthcare system leaders, who need to take action now to avoid financial penalties, we recommend starting with proven, high‐touch interventions such as Project RED and the Care Transitions Intervention, which are durable, cost‐effective, robustly address multiple domains of the Ideal Transition in Care, and have been implemented at numerous sites.[54, 55] Each hospital or group will need to decide on a bundle of interventions and customize them based on local workflow, resources, and culture.

Risk‐stratification, to match the intensity of the intervention to the risk of readmission of the patient, will undoubtedly be a key component for the efficient use of resources. We anticipate future research will allow risk stratification to be a robust part of any implementation plan. However, as noted above, current risk prediction models are imperfect,[6] and more work is needed to determine which patients benefit most from which interventions. Few if any studies have described interventions tailored to risk for this reason.

Based on our ideal transition in care, our collective experience, and published evidence,[7, 10, 11, 12] potential elements to start with include: early discharge planning; medication reconciliation[56]; patient/caregiver education using health literacy principles, cognitive assessments, and teach‐back to confirm understanding; synchronous communication (eg, by phone) between inpatient and postdischarge providers; follow‐up phone calls to patients within 72 hours of discharge; 24/7 availability of a responsible inpatient provider to address questions and problems (both from the patient/caregiver and from postdischarge providers); and prompt appointments for patients discharged home. High‐risk patients will likely require additional interventions, including in‐home assessments, disease‐monitoring programs, and/or patient coaching. Lastly, patients with certain conditions prone to readmission (such as heart failure and chronic obstructive pulmonary disease) may benefit from disease‐specific programs, including patient education, outpatient disease management, and monitoring protocols.

It is likely that the most effective interventions are those that come from combined, coordinated interventions shared between inpatient and outpatient settings, and are intensive in nature. We expect that the more domains in the framework that are addressed, the safer and more seamless transitions in care will be, with improvement in patient outcomes. To the extent that fragmentation of care has been a barrier to the implementation of these types of interventions in the past, ACOs, perhaps with imbedded Patient‐Centered Medical Homes, may be in the best position to take advantage of newly aligned financial incentives to design comprehensive transitional care. Indeed, we anticipate that Figure 1 may provide substrate for a discussion of postdischarge care and division of responsibilities between inpatient and outpatient care teams at the time of transition, so effort is not duplicated and multiple domains are addressed.

Other barriers to implementation of ideal transitions in care will continue to be an issue for most healthcare systems. Financial constraints that have been a barrier up until now will be partially overcome by penalties for high readmission rates and by ACOs, bundled payments, and alternative care contracts (ie, global payments), but the extent to which each institution feels rewarded for investing in transitional interventions will vary greatly. Healthcare leadership that sees the value of improving transitions in care will be critical to overcoming this barrier. Competing demands (such as lowering hospital length of stay and carrying out other patient care responsibilities),[57] lack of coordination and diffusion of responsibility among various clinical personnel, and lack of standards are other barriers[58] that will require clear prioritization from leadership, policy changes, team‐based care, provider education and feedback, and adequate allocation of personnel resources. In short, process redesign using continuous quality improvement efforts and effective tools will be required to maximize the possibility of success.

CONCLUSIONS

Readmissions are costly and undesirable. Intuition suggests they are a marker of poor care and that hospitals should be capable of reducing them, thereby improving care and decreasing costs. In a potential future world of ACOs based on global payments, financial incentives would be aligned for each system to reduce readmissions below their current baseline, therefore obviating the need for external financial rewards and penalties. In the meantime, financial penalties do exist, and controversy exists over their fairness and likelihood of driving appropriate behavior. To address these controversies and promote better transitional care, we call for the development and use of multifaceted, collaborative transitions interventions that span settings, risk‐adjustment models that allow for fairer comparisons among hospitals, better and more widespread measurement of processes of transitional care, a better understanding of what interventions are most effective and in whom, and better guidance in how to implement these interventions. Our conceptualization of an ideal transition of care serves as a guide and provides a common vocabulary for these efforts. Such research is likely to produce the knowledge needed for healthcare systems to improve transitions in care, reduce readmissions, and reduce costs.

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this activity, participants will be better able to:

• Assess the factors associated with reduced medication adherence.

• Distinguish which components of medication understanding are assessed by the Medication Understanding Questionnaire.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

Instructions on Receiving Credit

For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.

Follow these steps to earn credit:

• Log on to www.wileyblackwellcme.com

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.

If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this activity, participants will be better able to:

• Assess the factors associated with reduced medication adherence.

• Distinguish which components of medication understanding are assessed by the Medication Understanding Questionnaire.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

Instructions on Receiving Credit

For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.

Follow these steps to earn credit:

• Log on to www.wileyblackwellcme.com

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.

If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this activity, participants will be better able to:

• Assess the factors associated with reduced medication adherence.

• Distinguish which components of medication understanding are assessed by the Medication Understanding Questionnaire.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

Instructions on Receiving Credit

For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.

Follow these steps to earn credit:

• Log on to www.wileyblackwellcme.com

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.

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.