Hospitalized patients are often cared for by physicians who do not follow them in the community, creating a discontinuity of care that must be bridged through communication. This communication between inpatient and outpatient physicians occurs, in part via a discharge summary, which is intended to summarize events during hospitalization and prepare the outpatient physician to resume care of the patient. Yet, this form of communication has long been problematic.[1, 2, 3] In a 1960 study, only 30% of discharge letters were received by the primary care physician within 48 hours of discharge.[1]

More recent studies have shown little improvement. Direct communication between hospital and outpatient physicians is rare, and discharge summaries are still largely unavailable at the time of follow‐up.[4] In 1 study, primary care physicians were unaware of 62% of laboratory tests or study results that were pending on discharge,[5] in part because this information is missing from most discharge summaries.[6] Deficits such as these persist despite the fact that the rate of postdischarge completion of recommended tests, referrals, or procedures is significantly increased when the recommendation is included in the discharge summary.[7]

Regulatory mandates for discharge summaries from the Centers for Medicare and Medicaid Services[8] and from The Joint Commission[9] appear to be generally met[10, 11]; however, these mandates have no requirements for timeliness stricter than 30 days, do not require that summaries be transmitted to outpatient physicians, and do not require several content elements that might be useful to outside physicians such as condition of the patient at discharge, cognitive and functional status, goals of care, or pending studies. Expert opinion guidelines have more comprehensive recommendations,[12, 13] but it is uncertain how widely they are followed.

The existence of a discharge summary does not necessarily mean it serves a patient well in the transitional period.[11, 14, 15] Discharge summaries are a complex intervention, and we do not yet understand the best ways discharge summaries may fulfill needs specific to transitional care. Furthermore, it is uncertain what factors improve aspects of discharge summary quality as defined by timeliness, transmission, and content.[6, 16]

The goal of the DIagnosing Systemic failures, Complexities and HARm in GEriatric discharges study (DISCHARGE) was to comprehensively assess the discharge process for older patients discharged to the community. In this article we examine discharge summaries of patients enrolled in the study to determine the timeliness, transmission to outside physicians, and content of the summaries. We further examine the effect of provider training level and timeliness of dictation on discharge summary quality.

METHODS

RESULTS

Enrollment and Study Sample

A total of 3743 patients over 64 years old were discharged home from the medical service at YNHH during the study period; 3028 patients were screened for eligibility within 24 hours of admission. We identified 635 eligible admissions and enrolled 395 patients (62.2%) in the study. Of these, 377 granted permission for chart review and were included in this analysis (Figure 1).

Figure 1

The study sample had a mean age of 77.1 years (standard deviation: 7.8); 205 (54.4%) were male and 310 (82.5%) were non‐Hispanic white. A total of 195 (51.7%) had ACS, 91 (24.1%) had pneumonia, and 146 (38.7%) had HF; 54 (14.3%) patients had more than 1 qualifying condition. There were similar numbers of patients on the cardiology, medicine housestaff, and medicine hospitalist teams (Table 1).

Table 1.Study Sample Characteristics (N=377)

Characteristic	N (%) or Mean (SD)
NOTE: Abbreviations: APRN, advanced practice registered nurse; N=number of study participants; GED, general educational development; SD=standard deviation.
Condition
Acute coronary syndrome	195 (51.7)
Community‐acquired pneumonia	91 (24.1)
Heart failure	146 (38.7)
Training level of summary dictator
APRN	140 (37.1)
House staff	123 (32.6)
Hospitalist	114 (30.2)
Length of stay, mean, d	3.5 (2.5)
Total number of medications	8.9 (3.3)
Identify a usual source of care	360 (96.0)
Age, mean, y	77.1 (7.8)
Male	205 (54.4)
English‐speaking	366 (98.1)
Race/ethnicity
Non‐Hispanic white	310 (82.5)
Non‐Hispanic black	44 (11.7)
Hispanic	15 (4.0)
Other	7 (1.9)
High school graduate or GED Admission source	268 (73.4)
Emergency department	248 (66.0)
Direct transfer from hospital or nursing facility	94 (25.0)
Direct admission from office	34 (9.0)

DISCUSSION

In this prospective single‐site study of medical patients with 3 common conditions, we found that discharge summaries were completed relatively promptly, but were often not sent to the appropriate outpatient physicians. We also found that summaries were uniformly excellent at providing details of the hospitalization, but less reliable at providing details relevant to transitional care such as the patient's condition on discharge or existence of pending tests. On average, summaries included 57% of the elements included in consensus guidelines by 6 major medical societies. The content of discharge summaries dictated by hospitalists was slightly more comprehensive than that of APRNs and trainees, but no group exhibited high performance. In fact, not one discharge summary fully met all 3 quality criteria of timeliness, transmission, and content.

Our study, unlike most in the field, focused on multiple dimensions of discharge summary quality simultaneously. For instance, previous studies have found that timely receipt of a discharge summary does not reduce readmission rates.[11, 14, 15] Yet, if the content of the discharge summary is inadequate for postdischarge care, the summary may not be useful even if it is received by the follow‐up visit. Conversely, high‐quality content is ineffective if the summary is not sent to the outpatient physician.

This study suggests several avenues for improving summary quality. Timely discharge summaries in this study were more likely to include key content and to be transmitted to the appropriate physician. Strategies to improve discharge summary quality should therefore prioritize timely summaries, which can be expected to have downstream benefits for other aspects of quality. Some studies have found that templates improve discharge summary content.[22] In our institution, a template exists, but it favors a hospitalization‐focused rather than transition‐focused approach to the discharge summary. For instance, it includes instructions to dictate the admission exam, but not the discharge exam. Thus, designing templates specifically for transitional care is key. Maximizing capabilities of electronic records may help; many content elements that were commonly missing (e.g., pending results, discharge vitals, discharge weight) could be automatically inserted from electronic records. Likewise, automatic transmission of the summary to care providers listed in the electronic record might ameliorate many transmission failures. Some efforts have been made to convert existing electronic data into discharge summaries.[23, 24, 25] However, these activities are very preliminary, and some studies have found the quality of electronic summaries to be lower than dictated or handwritten summaries.[26] As with all automated or electronic applications, it will be essential to consider workflow, readability, and ability to synthesize information prior to adoption.

Hospitalists consistently produced highest‐quality summaries, even though they did not receive explicit training, suggesting experience may be beneficial,[27, 28, 29] or that the hospitalist community focus on transitional care has been effective. In addition, hospitalists at our institution explicitly prioritize timely and comprehensive discharge dictations, because their business relies on maintaining good relationships with outpatient physicians who contract for their services. Housestaff and APRNs have no such incentives or policies; rather, they typically consider discharge summaries to be a useful source of patient history at the time of an admission or readmission. Other academic centers have found similar results.[6, 16] Nonetheless, even though hospitalists had slightly better performance in our study, large gaps in the quality of summaries remained for all groups including hospitalists.

This study has several limitations. First, as a single‐site study at an academic hospital, it may not be generalizable to other hospitals or other settings. It is noteworthy, however, that the average time to dictation in this study was much lower than that of other studies,[4, 14, 30, 31, 32] suggesting that practices at this institution are at least no worse and possibly better than elsewhere. Second, although there are some mandates and expert opinion‐based guidelines for discharge summary content, there is no validated evidence base to confirm what content ought to be present in discharge summaries to improve patient outcomes. Third, we had too few readmissions in the dataset to have enough power to determine whether discharge summary content, timeliness, or transmission predicts readmission. Fourth, we did not determine whether the information in discharge summaries was accurate or complete; we merely assessed whether it was present. For example, we gave every discharge summary full credit for including discharge medications because they are automatically appended. Yet medication reconciliation errors at discharge are common.[33, 34] In fact, in the DISCHARGE study cohort, more than a quarter of discharge medication lists contained a suspected error.[35]

In summary, this study demonstrated the inadequacy of the contemporary discharge summary for conveying information that is critical to the transition from hospital to home. It may be that hospital culture treats hospitalizations as discrete and self‐contained events rather than as components of a larger episode of care. As interest in reducing readmissions rises, reframing the discharge summary to serve as a transitional tool and targeting it for quality assessment will likely be necessary.

Disclosures

At the time this study was conducted, Dr. Horwitz was supported by the CTSA Grant UL1 RR024139 and KL2 RR024138 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and NIH roadmap for Medical Research, and was a Centers of Excellence Scholar in Geriatric Medicine by the John A. Hartford Foundation and the American Federation for Aging Research. Dr. Horwitz is now supported by the National Institute on Aging (K08 AG038336) and by the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. This work was also supported by a grant from the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (P30AG021342 NIH/NIA). Dr. Krumholz is supported by grant U01 HL105270‐01 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute. No funding source had any role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging, the National Center for Advancing Translational Sciences, the National Institutes of Health, The John A. Hartford Foundation, the National Heart, Lung, and Blood Institute, or the American Federation for Aging Research. Dr. Horwitz had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. An earlier version of this work was presented as an oral presentation at the Society of General Internal Medicine Annual Meeting in Orlando, Florida on May 12, 2012. Dr. Krumholz chairs a cardiac scientific advisory board for UnitedHealth. Dr. Krumholz receives support from the Centers of Medicare and Medicaid Services (CMS) to develop and maintain performance measures that are used for public reporting, including readmission measures.

Histogram of days between discharge and dictation

Hospitalized patients are often cared for by physicians who do not follow them in the community, creating a discontinuity of care that must be bridged through communication. This communication between inpatient and outpatient physicians occurs, in part via a discharge summary, which is intended to summarize events during hospitalization and prepare the outpatient physician to resume care of the patient. Yet, this form of communication has long been problematic.[1, 2, 3] In a 1960 study, only 30% of discharge letters were received by the primary care physician within 48 hours of discharge.[1]

More recent studies have shown little improvement. Direct communication between hospital and outpatient physicians is rare, and discharge summaries are still largely unavailable at the time of follow‐up.[4] In 1 study, primary care physicians were unaware of 62% of laboratory tests or study results that were pending on discharge,[5] in part because this information is missing from most discharge summaries.[6] Deficits such as these persist despite the fact that the rate of postdischarge completion of recommended tests, referrals, or procedures is significantly increased when the recommendation is included in the discharge summary.[7]

Regulatory mandates for discharge summaries from the Centers for Medicare and Medicaid Services[8] and from The Joint Commission[9] appear to be generally met[10, 11]; however, these mandates have no requirements for timeliness stricter than 30 days, do not require that summaries be transmitted to outpatient physicians, and do not require several content elements that might be useful to outside physicians such as condition of the patient at discharge, cognitive and functional status, goals of care, or pending studies. Expert opinion guidelines have more comprehensive recommendations,[12, 13] but it is uncertain how widely they are followed.

The existence of a discharge summary does not necessarily mean it serves a patient well in the transitional period.[11, 14, 15] Discharge summaries are a complex intervention, and we do not yet understand the best ways discharge summaries may fulfill needs specific to transitional care. Furthermore, it is uncertain what factors improve aspects of discharge summary quality as defined by timeliness, transmission, and content.[6, 16]

The goal of the DIagnosing Systemic failures, Complexities and HARm in GEriatric discharges study (DISCHARGE) was to comprehensively assess the discharge process for older patients discharged to the community. In this article we examine discharge summaries of patients enrolled in the study to determine the timeliness, transmission to outside physicians, and content of the summaries. We further examine the effect of provider training level and timeliness of dictation on discharge summary quality.

METHODS

RESULTS

Enrollment and Study Sample

A total of 3743 patients over 64 years old were discharged home from the medical service at YNHH during the study period; 3028 patients were screened for eligibility within 24 hours of admission. We identified 635 eligible admissions and enrolled 395 patients (62.2%) in the study. Of these, 377 granted permission for chart review and were included in this analysis (Figure 1).

Figure 1

The study sample had a mean age of 77.1 years (standard deviation: 7.8); 205 (54.4%) were male and 310 (82.5%) were non‐Hispanic white. A total of 195 (51.7%) had ACS, 91 (24.1%) had pneumonia, and 146 (38.7%) had HF; 54 (14.3%) patients had more than 1 qualifying condition. There were similar numbers of patients on the cardiology, medicine housestaff, and medicine hospitalist teams (Table 1).

Table 1.Study Sample Characteristics (N=377)

Characteristic	N (%) or Mean (SD)
NOTE: Abbreviations: APRN, advanced practice registered nurse; N=number of study participants; GED, general educational development; SD=standard deviation.
Condition
Acute coronary syndrome	195 (51.7)
Community‐acquired pneumonia	91 (24.1)
Heart failure	146 (38.7)
Training level of summary dictator
APRN	140 (37.1)
House staff	123 (32.6)
Hospitalist	114 (30.2)
Length of stay, mean, d	3.5 (2.5)
Total number of medications	8.9 (3.3)
Identify a usual source of care	360 (96.0)
Age, mean, y	77.1 (7.8)
Male	205 (54.4)
English‐speaking	366 (98.1)
Race/ethnicity
Non‐Hispanic white	310 (82.5)
Non‐Hispanic black	44 (11.7)
Hispanic	15 (4.0)
Other	7 (1.9)
High school graduate or GED Admission source	268 (73.4)
Emergency department	248 (66.0)
Direct transfer from hospital or nursing facility	94 (25.0)
Direct admission from office	34 (9.0)

DISCUSSION

In this prospective single‐site study of medical patients with 3 common conditions, we found that discharge summaries were completed relatively promptly, but were often not sent to the appropriate outpatient physicians. We also found that summaries were uniformly excellent at providing details of the hospitalization, but less reliable at providing details relevant to transitional care such as the patient's condition on discharge or existence of pending tests. On average, summaries included 57% of the elements included in consensus guidelines by 6 major medical societies. The content of discharge summaries dictated by hospitalists was slightly more comprehensive than that of APRNs and trainees, but no group exhibited high performance. In fact, not one discharge summary fully met all 3 quality criteria of timeliness, transmission, and content.

Our study, unlike most in the field, focused on multiple dimensions of discharge summary quality simultaneously. For instance, previous studies have found that timely receipt of a discharge summary does not reduce readmission rates.[11, 14, 15] Yet, if the content of the discharge summary is inadequate for postdischarge care, the summary may not be useful even if it is received by the follow‐up visit. Conversely, high‐quality content is ineffective if the summary is not sent to the outpatient physician.

This study suggests several avenues for improving summary quality. Timely discharge summaries in this study were more likely to include key content and to be transmitted to the appropriate physician. Strategies to improve discharge summary quality should therefore prioritize timely summaries, which can be expected to have downstream benefits for other aspects of quality. Some studies have found that templates improve discharge summary content.[22] In our institution, a template exists, but it favors a hospitalization‐focused rather than transition‐focused approach to the discharge summary. For instance, it includes instructions to dictate the admission exam, but not the discharge exam. Thus, designing templates specifically for transitional care is key. Maximizing capabilities of electronic records may help; many content elements that were commonly missing (e.g., pending results, discharge vitals, discharge weight) could be automatically inserted from electronic records. Likewise, automatic transmission of the summary to care providers listed in the electronic record might ameliorate many transmission failures. Some efforts have been made to convert existing electronic data into discharge summaries.[23, 24, 25] However, these activities are very preliminary, and some studies have found the quality of electronic summaries to be lower than dictated or handwritten summaries.[26] As with all automated or electronic applications, it will be essential to consider workflow, readability, and ability to synthesize information prior to adoption.

Hospitalists consistently produced highest‐quality summaries, even though they did not receive explicit training, suggesting experience may be beneficial,[27, 28, 29] or that the hospitalist community focus on transitional care has been effective. In addition, hospitalists at our institution explicitly prioritize timely and comprehensive discharge dictations, because their business relies on maintaining good relationships with outpatient physicians who contract for their services. Housestaff and APRNs have no such incentives or policies; rather, they typically consider discharge summaries to be a useful source of patient history at the time of an admission or readmission. Other academic centers have found similar results.[6, 16] Nonetheless, even though hospitalists had slightly better performance in our study, large gaps in the quality of summaries remained for all groups including hospitalists.

This study has several limitations. First, as a single‐site study at an academic hospital, it may not be generalizable to other hospitals or other settings. It is noteworthy, however, that the average time to dictation in this study was much lower than that of other studies,[4, 14, 30, 31, 32] suggesting that practices at this institution are at least no worse and possibly better than elsewhere. Second, although there are some mandates and expert opinion‐based guidelines for discharge summary content, there is no validated evidence base to confirm what content ought to be present in discharge summaries to improve patient outcomes. Third, we had too few readmissions in the dataset to have enough power to determine whether discharge summary content, timeliness, or transmission predicts readmission. Fourth, we did not determine whether the information in discharge summaries was accurate or complete; we merely assessed whether it was present. For example, we gave every discharge summary full credit for including discharge medications because they are automatically appended. Yet medication reconciliation errors at discharge are common.[33, 34] In fact, in the DISCHARGE study cohort, more than a quarter of discharge medication lists contained a suspected error.[35]

In summary, this study demonstrated the inadequacy of the contemporary discharge summary for conveying information that is critical to the transition from hospital to home. It may be that hospital culture treats hospitalizations as discrete and self‐contained events rather than as components of a larger episode of care. As interest in reducing readmissions rises, reframing the discharge summary to serve as a transitional tool and targeting it for quality assessment will likely be necessary.

Disclosures

At the time this study was conducted, Dr. Horwitz was supported by the CTSA Grant UL1 RR024139 and KL2 RR024138 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and NIH roadmap for Medical Research, and was a Centers of Excellence Scholar in Geriatric Medicine by the John A. Hartford Foundation and the American Federation for Aging Research. Dr. Horwitz is now supported by the National Institute on Aging (K08 AG038336) and by the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. This work was also supported by a grant from the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (P30AG021342 NIH/NIA). Dr. Krumholz is supported by grant U01 HL105270‐01 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute. No funding source had any role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging, the National Center for Advancing Translational Sciences, the National Institutes of Health, The John A. Hartford Foundation, the National Heart, Lung, and Blood Institute, or the American Federation for Aging Research. Dr. Horwitz had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. An earlier version of this work was presented as an oral presentation at the Society of General Internal Medicine Annual Meeting in Orlando, Florida on May 12, 2012. Dr. Krumholz chairs a cardiac scientific advisory board for UnitedHealth. Dr. Krumholz receives support from the Centers of Medicare and Medicaid Services (CMS) to develop and maintain performance measures that are used for public reporting, including readmission measures.

Histogram of days between discharge and dictation

Hospitalized patients are often cared for by physicians who do not follow them in the community, creating a discontinuity of care that must be bridged through communication. This communication between inpatient and outpatient physicians occurs, in part via a discharge summary, which is intended to summarize events during hospitalization and prepare the outpatient physician to resume care of the patient. Yet, this form of communication has long been problematic.[1, 2, 3] In a 1960 study, only 30% of discharge letters were received by the primary care physician within 48 hours of discharge.[1]

More recent studies have shown little improvement. Direct communication between hospital and outpatient physicians is rare, and discharge summaries are still largely unavailable at the time of follow‐up.[4] In 1 study, primary care physicians were unaware of 62% of laboratory tests or study results that were pending on discharge,[5] in part because this information is missing from most discharge summaries.[6] Deficits such as these persist despite the fact that the rate of postdischarge completion of recommended tests, referrals, or procedures is significantly increased when the recommendation is included in the discharge summary.[7]

Regulatory mandates for discharge summaries from the Centers for Medicare and Medicaid Services[8] and from The Joint Commission[9] appear to be generally met[10, 11]; however, these mandates have no requirements for timeliness stricter than 30 days, do not require that summaries be transmitted to outpatient physicians, and do not require several content elements that might be useful to outside physicians such as condition of the patient at discharge, cognitive and functional status, goals of care, or pending studies. Expert opinion guidelines have more comprehensive recommendations,[12, 13] but it is uncertain how widely they are followed.

The existence of a discharge summary does not necessarily mean it serves a patient well in the transitional period.[11, 14, 15] Discharge summaries are a complex intervention, and we do not yet understand the best ways discharge summaries may fulfill needs specific to transitional care. Furthermore, it is uncertain what factors improve aspects of discharge summary quality as defined by timeliness, transmission, and content.[6, 16]

The goal of the DIagnosing Systemic failures, Complexities and HARm in GEriatric discharges study (DISCHARGE) was to comprehensively assess the discharge process for older patients discharged to the community. In this article we examine discharge summaries of patients enrolled in the study to determine the timeliness, transmission to outside physicians, and content of the summaries. We further examine the effect of provider training level and timeliness of dictation on discharge summary quality.

METHODS

RESULTS

Enrollment and Study Sample

A total of 3743 patients over 64 years old were discharged home from the medical service at YNHH during the study period; 3028 patients were screened for eligibility within 24 hours of admission. We identified 635 eligible admissions and enrolled 395 patients (62.2%) in the study. Of these, 377 granted permission for chart review and were included in this analysis (Figure 1).

Figure 1

The study sample had a mean age of 77.1 years (standard deviation: 7.8); 205 (54.4%) were male and 310 (82.5%) were non‐Hispanic white. A total of 195 (51.7%) had ACS, 91 (24.1%) had pneumonia, and 146 (38.7%) had HF; 54 (14.3%) patients had more than 1 qualifying condition. There were similar numbers of patients on the cardiology, medicine housestaff, and medicine hospitalist teams (Table 1).

Table 1.Study Sample Characteristics (N=377)

Characteristic	N (%) or Mean (SD)
NOTE: Abbreviations: APRN, advanced practice registered nurse; N=number of study participants; GED, general educational development; SD=standard deviation.
Condition
Acute coronary syndrome	195 (51.7)
Community‐acquired pneumonia	91 (24.1)
Heart failure	146 (38.7)
Training level of summary dictator
APRN	140 (37.1)
House staff	123 (32.6)
Hospitalist	114 (30.2)
Length of stay, mean, d	3.5 (2.5)
Total number of medications	8.9 (3.3)
Identify a usual source of care	360 (96.0)
Age, mean, y	77.1 (7.8)
Male	205 (54.4)
English‐speaking	366 (98.1)
Race/ethnicity
Non‐Hispanic white	310 (82.5)
Non‐Hispanic black	44 (11.7)
Hispanic	15 (4.0)
Other	7 (1.9)
High school graduate or GED Admission source	268 (73.4)
Emergency department	248 (66.0)
Direct transfer from hospital or nursing facility	94 (25.0)
Direct admission from office	34 (9.0)

DISCUSSION

In this prospective single‐site study of medical patients with 3 common conditions, we found that discharge summaries were completed relatively promptly, but were often not sent to the appropriate outpatient physicians. We also found that summaries were uniformly excellent at providing details of the hospitalization, but less reliable at providing details relevant to transitional care such as the patient's condition on discharge or existence of pending tests. On average, summaries included 57% of the elements included in consensus guidelines by 6 major medical societies. The content of discharge summaries dictated by hospitalists was slightly more comprehensive than that of APRNs and trainees, but no group exhibited high performance. In fact, not one discharge summary fully met all 3 quality criteria of timeliness, transmission, and content.

Our study, unlike most in the field, focused on multiple dimensions of discharge summary quality simultaneously. For instance, previous studies have found that timely receipt of a discharge summary does not reduce readmission rates.[11, 14, 15] Yet, if the content of the discharge summary is inadequate for postdischarge care, the summary may not be useful even if it is received by the follow‐up visit. Conversely, high‐quality content is ineffective if the summary is not sent to the outpatient physician.

This study suggests several avenues for improving summary quality. Timely discharge summaries in this study were more likely to include key content and to be transmitted to the appropriate physician. Strategies to improve discharge summary quality should therefore prioritize timely summaries, which can be expected to have downstream benefits for other aspects of quality. Some studies have found that templates improve discharge summary content.[22] In our institution, a template exists, but it favors a hospitalization‐focused rather than transition‐focused approach to the discharge summary. For instance, it includes instructions to dictate the admission exam, but not the discharge exam. Thus, designing templates specifically for transitional care is key. Maximizing capabilities of electronic records may help; many content elements that were commonly missing (e.g., pending results, discharge vitals, discharge weight) could be automatically inserted from electronic records. Likewise, automatic transmission of the summary to care providers listed in the electronic record might ameliorate many transmission failures. Some efforts have been made to convert existing electronic data into discharge summaries.[23, 24, 25] However, these activities are very preliminary, and some studies have found the quality of electronic summaries to be lower than dictated or handwritten summaries.[26] As with all automated or electronic applications, it will be essential to consider workflow, readability, and ability to synthesize information prior to adoption.

Hospitalists consistently produced highest‐quality summaries, even though they did not receive explicit training, suggesting experience may be beneficial,[27, 28, 29] or that the hospitalist community focus on transitional care has been effective. In addition, hospitalists at our institution explicitly prioritize timely and comprehensive discharge dictations, because their business relies on maintaining good relationships with outpatient physicians who contract for their services. Housestaff and APRNs have no such incentives or policies; rather, they typically consider discharge summaries to be a useful source of patient history at the time of an admission or readmission. Other academic centers have found similar results.[6, 16] Nonetheless, even though hospitalists had slightly better performance in our study, large gaps in the quality of summaries remained for all groups including hospitalists.

This study has several limitations. First, as a single‐site study at an academic hospital, it may not be generalizable to other hospitals or other settings. It is noteworthy, however, that the average time to dictation in this study was much lower than that of other studies,[4, 14, 30, 31, 32] suggesting that practices at this institution are at least no worse and possibly better than elsewhere. Second, although there are some mandates and expert opinion‐based guidelines for discharge summary content, there is no validated evidence base to confirm what content ought to be present in discharge summaries to improve patient outcomes. Third, we had too few readmissions in the dataset to have enough power to determine whether discharge summary content, timeliness, or transmission predicts readmission. Fourth, we did not determine whether the information in discharge summaries was accurate or complete; we merely assessed whether it was present. For example, we gave every discharge summary full credit for including discharge medications because they are automatically appended. Yet medication reconciliation errors at discharge are common.[33, 34] In fact, in the DISCHARGE study cohort, more than a quarter of discharge medication lists contained a suspected error.[35]

In summary, this study demonstrated the inadequacy of the contemporary discharge summary for conveying information that is critical to the transition from hospital to home. It may be that hospital culture treats hospitalizations as discrete and self‐contained events rather than as components of a larger episode of care. As interest in reducing readmissions rises, reframing the discharge summary to serve as a transitional tool and targeting it for quality assessment will likely be necessary.

Disclosures

At the time this study was conducted, Dr. Horwitz was supported by the CTSA Grant UL1 RR024139 and KL2 RR024138 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and NIH roadmap for Medical Research, and was a Centers of Excellence Scholar in Geriatric Medicine by the John A. Hartford Foundation and the American Federation for Aging Research. Dr. Horwitz is now supported by the National Institute on Aging (K08 AG038336) and by the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. This work was also supported by a grant from the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (P30AG021342 NIH/NIA). Dr. Krumholz is supported by grant U01 HL105270‐01 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute. No funding source had any role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging, the National Center for Advancing Translational Sciences, the National Institutes of Health, The John A. Hartford Foundation, the National Heart, Lung, and Blood Institute, or the American Federation for Aging Research. Dr. Horwitz had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. An earlier version of this work was presented as an oral presentation at the Society of General Internal Medicine Annual Meeting in Orlando, Florida on May 12, 2012. Dr. Krumholz chairs a cardiac scientific advisory board for UnitedHealth. Dr. Krumholz receives support from the Centers of Medicare and Medicaid Services (CMS) to develop and maintain performance measures that are used for public reporting, including readmission measures.

Histogram of days between discharge and dictation

Lack of sleep is a common problem in hospitalized patients and is associated with poorer health outcomes, especially in older patients.[1, 2, 3] Prior studies highlight a multitude of factors that can result in sleep loss in the hospital[3, 4, 5, 6] with 1 of the most common causes of sleep disruption in the hospital being noise.[7, 8, 9]

In addition to external factors, such as hospital noise, there may be inherent characteristics that predispose certain patients to greater sleep loss when hospitalized. One such measure is the construct of perceived control or the psychological measure of how much individuals expect themselves to be capable of bringing about desired outcomes.[10] Among older patients, low perceived control is associated with increased rates of physician visits, hospitalizations, and death.[11, 12] In contrast, patients who feel more in control of their environment may experience positive health benefits.[13]

Yet, when patients are placed in a hospital setting, they experience a significant reduction in control over their environment along with an increase in dependency on medical staff and therapies.[14, 15] For example, hospitalized patients are restricted in their personal decisions, such as what clothes they can wear and what they can eat and are not in charge of their own schedules, including their sleep time.

Although prior studies suggest that perceived control over sleep is related to actual sleep among community‐dwelling adults,[16, 17] no study has examined this relationship in hospitalized adults. Therefore, the aim of our study was to examine the possible association between perceived control, noise levels, and sleep in hospitalized middle‐aged and older patients.

METHODS

RESULTS

From April 2010 to May 2012, 1134 patients were screened by study personnel for this study via an ongoing study of hospitalized patients on the inpatient general medicine ward. Of the 361 (31.8%) eligible patients, 206 (57.1%) consented to participate. Of the subjects enrolled in the study, 118 were able to complete at least 1 night of actigraphy, sound monitoring, and subjective assessment for a total of 185 patient nights (Figure 1).

Figure 1

The majority of patients were female (57%), African American (67%), and non‐Hispanic (97%). The mean age was 65 years (standard deviation [SD], 11.6 years), and the median length of stay was 4 days (interquartile range [IQR], 36). The majority of patients also had hypertension (67%), with chronic obstructive pulmonary disease [COPD] (31%) and congestive heart failure (31%) being the next most common comorbidities. About two‐thirds of subjects (64%) were characterized as average or above average sleepers with Epworth Sleepiness Scale scores 9[20] (Table 1). Only 5% of patients received pharmacological sleep aids.

The mean baseline SLOC score was 30.4 (SD, 6.7), with a median of 31 (IQR, 2735). The mean baseline SSE score was 32.1 (SD, 9.4), with a median of 34 (IQR, 2441). Fifty‐four patients were categorized as having high sleep self‐efficacy (high SSE), which we defined as scoring above the median of 34.

Average in‐hospital sleep was 5.5 hours (333 minutes; SD, 128 minutes) which was significantly shorter than the self‐reported sleep duration of 6.5 hours prior to admission (387 minutes, SD, 125 minutes; P=0.0001). The mean sleep efficiency was 73% (SD, 19%) with 55% of actigraphy nights below the normal range of 80% efficiency for adults.[19] Median KSQI was 3.5 (IQR, 2.254.75), with 41% of the patients with a KSQI 3, putting them in the insomniac range.[32] The median score on the noise disruptiveness question was 1 (IQR, 14) with 42% of reports coded as disruptive defined as a score >1 on the 5‐point scale. The median score on the roommate disruptiveness question was 1 (IQR, 11) with 77% of responses coded as not disruptive defined as a score of 1 on the 5‐point scale.

A 2‐sample t test with equal variances showed that those patients reporting high SSE were more likely to sleep longer in the hospital than those reporting low SSE (364 minutes 95% confidence interval [CI]: 340, 388 vs 309 minutes 95% CI: 283, 336; P=0.003) (Figure 2). Patients with high SSE were also more likely to have a normal sleep efficiency (above 80%) compared to those with low SSE (54% 95% CI: 43, 65 vs 38% 95% CI: 28,47; P=0.028). Last, there was a trend toward patients reporting higher SSE to also report less noise disruption compared to those patients with low SSE ([42%] 95% CI: 31, 53 vs [56%] 95% CI: 46, 65; P=0.063) (Figure 3).

Figure 2

Figure 3

Linear regression clustered by subject showed that high SSE was associated with longer sleep duration (55 minutes 95% CI: 14, 97; P=0.010). Furthermore, high SSE was significantly associated with longer sleep duration after controlling for both objective noise level and patient demographics in the model using stepwise forward regression (50 minutes 95% CI: 11, 90; P=0.014) (Table 2).

Logistic regression clustered by subject demonstrated that patients with high SSE had 2 times higher odds of having a KSQI score above 3 (95% CI: 1.12, 3.71; P=0.020). This association was still significant after controlling for noise and patient demographics (OR: 2.01; 95% CI: 1.06, 3.79; P=0.032). After controlling for noise levels and patient demographics, there was a statistically significant association between high SSE and lower odds of noise complaints (OR: 0.49; 95% CI: 0.25, 0.96; P=0.039) (Table 2). Although demographic characteristics were not associated with high SSE, those patients with high SSE had lower odds of being in the loudest tertile rooms (OR: 0.34; 95% CI: 0.15, 0.74; P=0.007).

In multivariate linear regression analyses, there were no significant relationships between SLOC scores and KSQI, reported noise disruptiveness, and markers of sleep (sleep duration or sleep efficiency).

DISCUSSION

This study is the first to examine the relationship between perceived control, noise levels, and objective measurements of sleep in a hospital setting. One measure of perceived control, namely SSE, was associated with objective sleep duration, subjective and objective sleep quality, noise levels in patient rooms, and perhaps also patient complaints of noise. These associations remained significant after controlling for objective noise levels and patient demographics, suggesting that SSE is independently related to sleep.

In contrast to SSE, SLOC was not found to be significantly associated with either subjective or objective measures of sleep quality. The lack of association may be due to the fact that the SLOC questionnaire does not translate as well to the inpatient setting as the SSE questionnaire. The SLOC questionnaire focuses on general beliefs about sleep whereas the SSE questionnaire focuses on personal beliefs about one's own ability sleep in the immediate future, which may make it more relevant in the inpatient setting (see Supporting Information, Appendix 1 and 2, in the online version of this article).

Given our findings, it is important to identify why patients with high SSE have better sleep and fewer noise complaints. One possibility is that sleep self‐efficacy is an inherited trait unique to each person that is also predictive of a patient's sleep patterns. However, is it also possible that those patients with high SSE feel more empowered to take control of their environment, allowing them to advocate for better sleep? This hypothesis is further strengthened by the finding that those patients with high SSE on study entry were less likely to be in the noisiest rooms. This raises the possibility that at least 1 of the mechanisms by which high SSE may be protective against sleep loss is through patients taking an active role in noise reduction, such as closing the door or advocating for their sleep with staff. However, we did not directly observe or ask patients whether doors of patient rooms were open or closed or whether the patients took other measures to advocate for their own sleep. Thus, further work is necessary to understand the mechanisms by which sleep self‐efficacy may influence sleep.

One potential avenue for future research is to explore possible interventions for boosting sleep self‐efficacy in the hospital. Although most interventions have focused on environmental noise and staff‐based education, empowering patients through boosting SSE may be a helpful adjunct to improving hospital sleep.[33, 34] Currently, the SSE scale is not commonly used in the inpatient setting. Motivational interviewing and patient coaching could be explored as potential tools for boosting SSE. Furthermore, even if SSE is not easily changed, measuring SSE in patients newly admitted to the hospital may be useful in identifying patients most susceptible to sleep disruptions. Efforts to identify patients with low SSE should go hand‐in‐hand with measures to reduce noise. Addressing both patient‐level and environmental factors simultaneously may be the best strategy for improving sleep in an inpatient hospital setting.

In contrast to our prior study, it is worth noting that we did not find any significant relationships between overall noise levels and sleep.[9] In this dataset, nighttime noise is still a predictor of sleep loss in the hospital. However, when we restrict our sample to those who answered the SSE questionnaire and had nighttime noise recorded, we lose a significant number of observations. Because of our interest in testing the relationship between SSE and sleep, we chose to control for overall noise (which enabled us to retain more observations). We also did not find any interactions between SSE and noise in our regression models. Further work is warranted with larger sample sizes to better understand the role of SSE in the context of sleep and noise levels. In addition, females also received more sleep than males in our study.

There are several limitations to this study. This study was carried out at a single service at a single institution, limiting the ability to generalize the findings to other hospital settings. This study had a relatively high rate of patients who were unable to complete at least 1 night of data collection (42%), often due to watch removal for imaging or procedures, which may also affect the representativeness of our sample. Moreover, we can only examine associations and not causal relationships. The SSE scale has never been used in hospitalized patients, making comparisons between scores from hospitalized patients and population controls difficult. In addition, the SSE scale also has not been dichotomized in previous studies into high and low SSE. However, a sensitivity analysis with raw SSE scores did not change the results of our study. It can be difficult to perform actigraphy measurements in the hospital because many patients spend most of their time in bed. Because we chose a relatively healthy cohort of patients without significant limitations in mobility, actigraphy could still be used to differentiate time spent awake from time spent sleeping. Because we did not perform polysomnography, we cannot explore the role of sleep architecture which is an important component of sleep quality. Although the use of pharmacologic sleep aids is a potential confounding factor, the rate of use was very low in our cohort and unlikely to significantly affect our results. Continued study of this patient population is warranted to further develop the findings.

In conclusion, patients with high SSE sleep better in the hospital, tend to be in quieter rooms, and may report fewer noise complaints. Our findings suggest that a greater confidence in the ability to sleep may be beneficial in hospitalized adults. In addition to noise control, hospitals should also consider targeting patients with low SSE when designing novel interventions to improve in‐hospital sleep.

Disclosures

This work was supported by funding from the National Institute on Aging through a Short‐Term Aging‐Related Research Program (1 T35 AG029795), National Institute on Aging career development award (K23AG033763), a midcareer career development award (1K24AG031326), a program project (P01AG‐11412), an Agency for Healthcare Research and Quality Centers for Education and Research on Therapeutics grant (1U18HS016967), and a National Institute on Aging Clinical Translational Sciences award (UL1 RR024999). Dr. Arora had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the statistical analysis. The funding agencies had no role in the design of the study; the collection, analysis, and interpretation of the data; or the decision to approve publication of the finished manuscript. The authors report no conflicts of interest.

Lack of sleep is a common problem in hospitalized patients and is associated with poorer health outcomes, especially in older patients.[1, 2, 3] Prior studies highlight a multitude of factors that can result in sleep loss in the hospital[3, 4, 5, 6] with 1 of the most common causes of sleep disruption in the hospital being noise.[7, 8, 9]

In addition to external factors, such as hospital noise, there may be inherent characteristics that predispose certain patients to greater sleep loss when hospitalized. One such measure is the construct of perceived control or the psychological measure of how much individuals expect themselves to be capable of bringing about desired outcomes.[10] Among older patients, low perceived control is associated with increased rates of physician visits, hospitalizations, and death.[11, 12] In contrast, patients who feel more in control of their environment may experience positive health benefits.[13]

Yet, when patients are placed in a hospital setting, they experience a significant reduction in control over their environment along with an increase in dependency on medical staff and therapies.[14, 15] For example, hospitalized patients are restricted in their personal decisions, such as what clothes they can wear and what they can eat and are not in charge of their own schedules, including their sleep time.

Although prior studies suggest that perceived control over sleep is related to actual sleep among community‐dwelling adults,[16, 17] no study has examined this relationship in hospitalized adults. Therefore, the aim of our study was to examine the possible association between perceived control, noise levels, and sleep in hospitalized middle‐aged and older patients.

METHODS

RESULTS

From April 2010 to May 2012, 1134 patients were screened by study personnel for this study via an ongoing study of hospitalized patients on the inpatient general medicine ward. Of the 361 (31.8%) eligible patients, 206 (57.1%) consented to participate. Of the subjects enrolled in the study, 118 were able to complete at least 1 night of actigraphy, sound monitoring, and subjective assessment for a total of 185 patient nights (Figure 1).

Figure 1

The majority of patients were female (57%), African American (67%), and non‐Hispanic (97%). The mean age was 65 years (standard deviation [SD], 11.6 years), and the median length of stay was 4 days (interquartile range [IQR], 36). The majority of patients also had hypertension (67%), with chronic obstructive pulmonary disease [COPD] (31%) and congestive heart failure (31%) being the next most common comorbidities. About two‐thirds of subjects (64%) were characterized as average or above average sleepers with Epworth Sleepiness Scale scores 9[20] (Table 1). Only 5% of patients received pharmacological sleep aids.

The mean baseline SLOC score was 30.4 (SD, 6.7), with a median of 31 (IQR, 2735). The mean baseline SSE score was 32.1 (SD, 9.4), with a median of 34 (IQR, 2441). Fifty‐four patients were categorized as having high sleep self‐efficacy (high SSE), which we defined as scoring above the median of 34.

Average in‐hospital sleep was 5.5 hours (333 minutes; SD, 128 minutes) which was significantly shorter than the self‐reported sleep duration of 6.5 hours prior to admission (387 minutes, SD, 125 minutes; P=0.0001). The mean sleep efficiency was 73% (SD, 19%) with 55% of actigraphy nights below the normal range of 80% efficiency for adults.[19] Median KSQI was 3.5 (IQR, 2.254.75), with 41% of the patients with a KSQI 3, putting them in the insomniac range.[32] The median score on the noise disruptiveness question was 1 (IQR, 14) with 42% of reports coded as disruptive defined as a score >1 on the 5‐point scale. The median score on the roommate disruptiveness question was 1 (IQR, 11) with 77% of responses coded as not disruptive defined as a score of 1 on the 5‐point scale.

A 2‐sample t test with equal variances showed that those patients reporting high SSE were more likely to sleep longer in the hospital than those reporting low SSE (364 minutes 95% confidence interval [CI]: 340, 388 vs 309 minutes 95% CI: 283, 336; P=0.003) (Figure 2). Patients with high SSE were also more likely to have a normal sleep efficiency (above 80%) compared to those with low SSE (54% 95% CI: 43, 65 vs 38% 95% CI: 28,47; P=0.028). Last, there was a trend toward patients reporting higher SSE to also report less noise disruption compared to those patients with low SSE ([42%] 95% CI: 31, 53 vs [56%] 95% CI: 46, 65; P=0.063) (Figure 3).

Figure 2

Figure 3

Linear regression clustered by subject showed that high SSE was associated with longer sleep duration (55 minutes 95% CI: 14, 97; P=0.010). Furthermore, high SSE was significantly associated with longer sleep duration after controlling for both objective noise level and patient demographics in the model using stepwise forward regression (50 minutes 95% CI: 11, 90; P=0.014) (Table 2).

Logistic regression clustered by subject demonstrated that patients with high SSE had 2 times higher odds of having a KSQI score above 3 (95% CI: 1.12, 3.71; P=0.020). This association was still significant after controlling for noise and patient demographics (OR: 2.01; 95% CI: 1.06, 3.79; P=0.032). After controlling for noise levels and patient demographics, there was a statistically significant association between high SSE and lower odds of noise complaints (OR: 0.49; 95% CI: 0.25, 0.96; P=0.039) (Table 2). Although demographic characteristics were not associated with high SSE, those patients with high SSE had lower odds of being in the loudest tertile rooms (OR: 0.34; 95% CI: 0.15, 0.74; P=0.007).

In multivariate linear regression analyses, there were no significant relationships between SLOC scores and KSQI, reported noise disruptiveness, and markers of sleep (sleep duration or sleep efficiency).

DISCUSSION

This study is the first to examine the relationship between perceived control, noise levels, and objective measurements of sleep in a hospital setting. One measure of perceived control, namely SSE, was associated with objective sleep duration, subjective and objective sleep quality, noise levels in patient rooms, and perhaps also patient complaints of noise. These associations remained significant after controlling for objective noise levels and patient demographics, suggesting that SSE is independently related to sleep.

In contrast to SSE, SLOC was not found to be significantly associated with either subjective or objective measures of sleep quality. The lack of association may be due to the fact that the SLOC questionnaire does not translate as well to the inpatient setting as the SSE questionnaire. The SLOC questionnaire focuses on general beliefs about sleep whereas the SSE questionnaire focuses on personal beliefs about one's own ability sleep in the immediate future, which may make it more relevant in the inpatient setting (see Supporting Information, Appendix 1 and 2, in the online version of this article).

Given our findings, it is important to identify why patients with high SSE have better sleep and fewer noise complaints. One possibility is that sleep self‐efficacy is an inherited trait unique to each person that is also predictive of a patient's sleep patterns. However, is it also possible that those patients with high SSE feel more empowered to take control of their environment, allowing them to advocate for better sleep? This hypothesis is further strengthened by the finding that those patients with high SSE on study entry were less likely to be in the noisiest rooms. This raises the possibility that at least 1 of the mechanisms by which high SSE may be protective against sleep loss is through patients taking an active role in noise reduction, such as closing the door or advocating for their sleep with staff. However, we did not directly observe or ask patients whether doors of patient rooms were open or closed or whether the patients took other measures to advocate for their own sleep. Thus, further work is necessary to understand the mechanisms by which sleep self‐efficacy may influence sleep.

One potential avenue for future research is to explore possible interventions for boosting sleep self‐efficacy in the hospital. Although most interventions have focused on environmental noise and staff‐based education, empowering patients through boosting SSE may be a helpful adjunct to improving hospital sleep.[33, 34] Currently, the SSE scale is not commonly used in the inpatient setting. Motivational interviewing and patient coaching could be explored as potential tools for boosting SSE. Furthermore, even if SSE is not easily changed, measuring SSE in patients newly admitted to the hospital may be useful in identifying patients most susceptible to sleep disruptions. Efforts to identify patients with low SSE should go hand‐in‐hand with measures to reduce noise. Addressing both patient‐level and environmental factors simultaneously may be the best strategy for improving sleep in an inpatient hospital setting.

In contrast to our prior study, it is worth noting that we did not find any significant relationships between overall noise levels and sleep.[9] In this dataset, nighttime noise is still a predictor of sleep loss in the hospital. However, when we restrict our sample to those who answered the SSE questionnaire and had nighttime noise recorded, we lose a significant number of observations. Because of our interest in testing the relationship between SSE and sleep, we chose to control for overall noise (which enabled us to retain more observations). We also did not find any interactions between SSE and noise in our regression models. Further work is warranted with larger sample sizes to better understand the role of SSE in the context of sleep and noise levels. In addition, females also received more sleep than males in our study.

There are several limitations to this study. This study was carried out at a single service at a single institution, limiting the ability to generalize the findings to other hospital settings. This study had a relatively high rate of patients who were unable to complete at least 1 night of data collection (42%), often due to watch removal for imaging or procedures, which may also affect the representativeness of our sample. Moreover, we can only examine associations and not causal relationships. The SSE scale has never been used in hospitalized patients, making comparisons between scores from hospitalized patients and population controls difficult. In addition, the SSE scale also has not been dichotomized in previous studies into high and low SSE. However, a sensitivity analysis with raw SSE scores did not change the results of our study. It can be difficult to perform actigraphy measurements in the hospital because many patients spend most of their time in bed. Because we chose a relatively healthy cohort of patients without significant limitations in mobility, actigraphy could still be used to differentiate time spent awake from time spent sleeping. Because we did not perform polysomnography, we cannot explore the role of sleep architecture which is an important component of sleep quality. Although the use of pharmacologic sleep aids is a potential confounding factor, the rate of use was very low in our cohort and unlikely to significantly affect our results. Continued study of this patient population is warranted to further develop the findings.

In conclusion, patients with high SSE sleep better in the hospital, tend to be in quieter rooms, and may report fewer noise complaints. Our findings suggest that a greater confidence in the ability to sleep may be beneficial in hospitalized adults. In addition to noise control, hospitals should also consider targeting patients with low SSE when designing novel interventions to improve in‐hospital sleep.

Disclosures

This work was supported by funding from the National Institute on Aging through a Short‐Term Aging‐Related Research Program (1 T35 AG029795), National Institute on Aging career development award (K23AG033763), a midcareer career development award (1K24AG031326), a program project (P01AG‐11412), an Agency for Healthcare Research and Quality Centers for Education and Research on Therapeutics grant (1U18HS016967), and a National Institute on Aging Clinical Translational Sciences award (UL1 RR024999). Dr. Arora had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the statistical analysis. The funding agencies had no role in the design of the study; the collection, analysis, and interpretation of the data; or the decision to approve publication of the finished manuscript. The authors report no conflicts of interest.

Lack of sleep is a common problem in hospitalized patients and is associated with poorer health outcomes, especially in older patients.[1, 2, 3] Prior studies highlight a multitude of factors that can result in sleep loss in the hospital[3, 4, 5, 6] with 1 of the most common causes of sleep disruption in the hospital being noise.[7, 8, 9]

In addition to external factors, such as hospital noise, there may be inherent characteristics that predispose certain patients to greater sleep loss when hospitalized. One such measure is the construct of perceived control or the psychological measure of how much individuals expect themselves to be capable of bringing about desired outcomes.[10] Among older patients, low perceived control is associated with increased rates of physician visits, hospitalizations, and death.[11, 12] In contrast, patients who feel more in control of their environment may experience positive health benefits.[13]

Yet, when patients are placed in a hospital setting, they experience a significant reduction in control over their environment along with an increase in dependency on medical staff and therapies.[14, 15] For example, hospitalized patients are restricted in their personal decisions, such as what clothes they can wear and what they can eat and are not in charge of their own schedules, including their sleep time.

Although prior studies suggest that perceived control over sleep is related to actual sleep among community‐dwelling adults,[16, 17] no study has examined this relationship in hospitalized adults. Therefore, the aim of our study was to examine the possible association between perceived control, noise levels, and sleep in hospitalized middle‐aged and older patients.

METHODS

RESULTS

From April 2010 to May 2012, 1134 patients were screened by study personnel for this study via an ongoing study of hospitalized patients on the inpatient general medicine ward. Of the 361 (31.8%) eligible patients, 206 (57.1%) consented to participate. Of the subjects enrolled in the study, 118 were able to complete at least 1 night of actigraphy, sound monitoring, and subjective assessment for a total of 185 patient nights (Figure 1).

Figure 1

The majority of patients were female (57%), African American (67%), and non‐Hispanic (97%). The mean age was 65 years (standard deviation [SD], 11.6 years), and the median length of stay was 4 days (interquartile range [IQR], 36). The majority of patients also had hypertension (67%), with chronic obstructive pulmonary disease [COPD] (31%) and congestive heart failure (31%) being the next most common comorbidities. About two‐thirds of subjects (64%) were characterized as average or above average sleepers with Epworth Sleepiness Scale scores 9[20] (Table 1). Only 5% of patients received pharmacological sleep aids.

The mean baseline SLOC score was 30.4 (SD, 6.7), with a median of 31 (IQR, 2735). The mean baseline SSE score was 32.1 (SD, 9.4), with a median of 34 (IQR, 2441). Fifty‐four patients were categorized as having high sleep self‐efficacy (high SSE), which we defined as scoring above the median of 34.

Average in‐hospital sleep was 5.5 hours (333 minutes; SD, 128 minutes) which was significantly shorter than the self‐reported sleep duration of 6.5 hours prior to admission (387 minutes, SD, 125 minutes; P=0.0001). The mean sleep efficiency was 73% (SD, 19%) with 55% of actigraphy nights below the normal range of 80% efficiency for adults.[19] Median KSQI was 3.5 (IQR, 2.254.75), with 41% of the patients with a KSQI 3, putting them in the insomniac range.[32] The median score on the noise disruptiveness question was 1 (IQR, 14) with 42% of reports coded as disruptive defined as a score >1 on the 5‐point scale. The median score on the roommate disruptiveness question was 1 (IQR, 11) with 77% of responses coded as not disruptive defined as a score of 1 on the 5‐point scale.

A 2‐sample t test with equal variances showed that those patients reporting high SSE were more likely to sleep longer in the hospital than those reporting low SSE (364 minutes 95% confidence interval [CI]: 340, 388 vs 309 minutes 95% CI: 283, 336; P=0.003) (Figure 2). Patients with high SSE were also more likely to have a normal sleep efficiency (above 80%) compared to those with low SSE (54% 95% CI: 43, 65 vs 38% 95% CI: 28,47; P=0.028). Last, there was a trend toward patients reporting higher SSE to also report less noise disruption compared to those patients with low SSE ([42%] 95% CI: 31, 53 vs [56%] 95% CI: 46, 65; P=0.063) (Figure 3).

Figure 2

Figure 3

Linear regression clustered by subject showed that high SSE was associated with longer sleep duration (55 minutes 95% CI: 14, 97; P=0.010). Furthermore, high SSE was significantly associated with longer sleep duration after controlling for both objective noise level and patient demographics in the model using stepwise forward regression (50 minutes 95% CI: 11, 90; P=0.014) (Table 2).

Logistic regression clustered by subject demonstrated that patients with high SSE had 2 times higher odds of having a KSQI score above 3 (95% CI: 1.12, 3.71; P=0.020). This association was still significant after controlling for noise and patient demographics (OR: 2.01; 95% CI: 1.06, 3.79; P=0.032). After controlling for noise levels and patient demographics, there was a statistically significant association between high SSE and lower odds of noise complaints (OR: 0.49; 95% CI: 0.25, 0.96; P=0.039) (Table 2). Although demographic characteristics were not associated with high SSE, those patients with high SSE had lower odds of being in the loudest tertile rooms (OR: 0.34; 95% CI: 0.15, 0.74; P=0.007).

In multivariate linear regression analyses, there were no significant relationships between SLOC scores and KSQI, reported noise disruptiveness, and markers of sleep (sleep duration or sleep efficiency).

DISCUSSION

This study is the first to examine the relationship between perceived control, noise levels, and objective measurements of sleep in a hospital setting. One measure of perceived control, namely SSE, was associated with objective sleep duration, subjective and objective sleep quality, noise levels in patient rooms, and perhaps also patient complaints of noise. These associations remained significant after controlling for objective noise levels and patient demographics, suggesting that SSE is independently related to sleep.

In contrast to SSE, SLOC was not found to be significantly associated with either subjective or objective measures of sleep quality. The lack of association may be due to the fact that the SLOC questionnaire does not translate as well to the inpatient setting as the SSE questionnaire. The SLOC questionnaire focuses on general beliefs about sleep whereas the SSE questionnaire focuses on personal beliefs about one's own ability sleep in the immediate future, which may make it more relevant in the inpatient setting (see Supporting Information, Appendix 1 and 2, in the online version of this article).

Given our findings, it is important to identify why patients with high SSE have better sleep and fewer noise complaints. One possibility is that sleep self‐efficacy is an inherited trait unique to each person that is also predictive of a patient's sleep patterns. However, is it also possible that those patients with high SSE feel more empowered to take control of their environment, allowing them to advocate for better sleep? This hypothesis is further strengthened by the finding that those patients with high SSE on study entry were less likely to be in the noisiest rooms. This raises the possibility that at least 1 of the mechanisms by which high SSE may be protective against sleep loss is through patients taking an active role in noise reduction, such as closing the door or advocating for their sleep with staff. However, we did not directly observe or ask patients whether doors of patient rooms were open or closed or whether the patients took other measures to advocate for their own sleep. Thus, further work is necessary to understand the mechanisms by which sleep self‐efficacy may influence sleep.

One potential avenue for future research is to explore possible interventions for boosting sleep self‐efficacy in the hospital. Although most interventions have focused on environmental noise and staff‐based education, empowering patients through boosting SSE may be a helpful adjunct to improving hospital sleep.[33, 34] Currently, the SSE scale is not commonly used in the inpatient setting. Motivational interviewing and patient coaching could be explored as potential tools for boosting SSE. Furthermore, even if SSE is not easily changed, measuring SSE in patients newly admitted to the hospital may be useful in identifying patients most susceptible to sleep disruptions. Efforts to identify patients with low SSE should go hand‐in‐hand with measures to reduce noise. Addressing both patient‐level and environmental factors simultaneously may be the best strategy for improving sleep in an inpatient hospital setting.

In contrast to our prior study, it is worth noting that we did not find any significant relationships between overall noise levels and sleep.[9] In this dataset, nighttime noise is still a predictor of sleep loss in the hospital. However, when we restrict our sample to those who answered the SSE questionnaire and had nighttime noise recorded, we lose a significant number of observations. Because of our interest in testing the relationship between SSE and sleep, we chose to control for overall noise (which enabled us to retain more observations). We also did not find any interactions between SSE and noise in our regression models. Further work is warranted with larger sample sizes to better understand the role of SSE in the context of sleep and noise levels. In addition, females also received more sleep than males in our study.

There are several limitations to this study. This study was carried out at a single service at a single institution, limiting the ability to generalize the findings to other hospital settings. This study had a relatively high rate of patients who were unable to complete at least 1 night of data collection (42%), often due to watch removal for imaging or procedures, which may also affect the representativeness of our sample. Moreover, we can only examine associations and not causal relationships. The SSE scale has never been used in hospitalized patients, making comparisons between scores from hospitalized patients and population controls difficult. In addition, the SSE scale also has not been dichotomized in previous studies into high and low SSE. However, a sensitivity analysis with raw SSE scores did not change the results of our study. It can be difficult to perform actigraphy measurements in the hospital because many patients spend most of their time in bed. Because we chose a relatively healthy cohort of patients without significant limitations in mobility, actigraphy could still be used to differentiate time spent awake from time spent sleeping. Because we did not perform polysomnography, we cannot explore the role of sleep architecture which is an important component of sleep quality. Although the use of pharmacologic sleep aids is a potential confounding factor, the rate of use was very low in our cohort and unlikely to significantly affect our results. Continued study of this patient population is warranted to further develop the findings.

In conclusion, patients with high SSE sleep better in the hospital, tend to be in quieter rooms, and may report fewer noise complaints. Our findings suggest that a greater confidence in the ability to sleep may be beneficial in hospitalized adults. In addition to noise control, hospitals should also consider targeting patients with low SSE when designing novel interventions to improve in‐hospital sleep.

Disclosures

This work was supported by funding from the National Institute on Aging through a Short‐Term Aging‐Related Research Program (1 T35 AG029795), National Institute on Aging career development award (K23AG033763), a midcareer career development award (1K24AG031326), a program project (P01AG‐11412), an Agency for Healthcare Research and Quality Centers for Education and Research on Therapeutics grant (1U18HS016967), and a National Institute on Aging Clinical Translational Sciences award (UL1 RR024999). Dr. Arora had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the statistical analysis. The funding agencies had no role in the design of the study; the collection, analysis, and interpretation of the data; or the decision to approve publication of the finished manuscript. The authors report no conflicts of interest.