Hospitalized patients are often debilitated, either from their admitting illness or from the deconditioning that occurs with inactivity. Functional decline, which appears to progress in a hierarchical pattern,1 occurs in 24% to 50% of geriatric patients during hospitalization and is poorly documented.2 Such a decline is associated not only with longer hospital stays and increased health care costs but also with higher mortality.3 The American College of Physicians, through its Assessing Care of Vulnerable Elders project, expressly endorsed gait and mobility evaluation as a quality indicator, and examination insufficiency is well documented.4

Of the several existing mobility assessment tools, few are used routinely in hospital. Some require complex scoring; others require timing and/or a trained occupational therapist.5 We created a simplified tool named Independent Mobility Validation Examination (I‐MOVE) for use by bedside caregivers. We evaluated the tool's face validity and interobserver agreement.

I‐MOVE

I‐MOVE, represented schematically in Figure 1, is a performance test that assesses the patient's ability to perform a sequence of 6 basic tasks: rolling over in bed, sitting up, standing, transferring to a chair, walking in the room, and walking in the hallway. Most motor functions can be assumed to be hierarchical in nature; any patient who can perform at the highest level, such as walking safely, also would be expected to perform at the lowest level.

Figure 1

Instructions for administering I‐MOVE are as follows:

• 1

  Review current orders. Exclude patients ordered on bed rest or non‐weight‐bearing or other orders precluding any of the 6 requested actions.

• 2

  Prepare environment.
• a

  Chair at bedside.

• b

  Lower side bed rail closest to chair.

• c

  Clear path for patient to ambulate.

• d

  Ensure patient dons slippers.

• e

  Flatten bed.

• f

  Ensure any gait assistive device, if generally used by the patient, is within reach from the bedside.

• 3

  Requests for patient action (for steps c through f, make available and within reach any appropriate gait‐assistance device such as walker or cane, if such is customarily used at home or newly prescribed):
• a

  With patient lying supine in bed, with close supervision, ask patient to turn from side to side in bed (request when both bed rails are up).

• b

  Lower side rail closer to chair and ask the patient to rise up to a sitting position and turn to sit up with legs dangling off the bed.

• c

  Ask the patient to stand.

• d

  Ask the patient to take a seat in the chair next to the bed.

• e

  Ask the patient to ambulate in the room.

• f

  Ask the patient to ambulate in the hallway.

• 4

  At any point if the patient seems incapable, unsteady, or unsafe to accomplish the requested task, render hands‐on assistance and immediately end the test.

• 5

  Document, by number (1‐12), the activity level successfully accomplished independently by the patient (even number levels) or accomplished with assistance (odd number levels).

• 6

  Patient may be considered independent if able to perform the activity with a normal assistive device (cane, walker, brace, or crutches) but not using furniture.

• 7

  Assistance is defined as any physical contact with the patient.

Findings

Face Validity

We sent surveys to 6 experienced practicing clinicians at our hospital: a geriatrician, a physiatrist, an exercise physiologist, an occupational therapist, a physical therapist, and a registered nurse. We asked each clinician to rate the 6 I‐MOVE elements (requested actions) for clinical relevance to mobility independence. Relevance of each element was measured on an ordinal scale with scores ranging from 1 to 4, with: 1 not relevant; 2 somewhat relevant; 3 quite relevant; and 4 very relevant. From the 5 responses we received, 4 evaluators ranked all 6 I‐MOVE requested actions as very relevant. The fifth evaluator ranked 5 of the 6 actions as very relevant and 1 action (walking in the room) as quite relevant. These results demonstrate general agreement that I‐MOVE is, at face value, a reasonable measure of independent mobility.

Interrater Reliability

The protocol was approved by the hospital's institutional review board. On a general medical unita non‐electrocardiographic telemetry, nonsurgical unit of an acute care hospital, where patients are assigned the primary service of an internal medicine physicianwe instructed 2 registered nurse (RN) volunteers (RN1 and RN2) in the I‐MOVE protocol. Each RN administered I‐MOVE independently to 41 consecutive, cognitively intact patients in a blinded fashion (ie, neither nurse was aware of the other's scoring of each patient) and within 1 hour of each other's assessment.

After administering I‐MOVE to each patient, the nurse judged and scored the patient's performance using the 12‐level I‐MOVE ordinal scale, ranging from a low value of 1, complete dependence, to the highest value of 12, complete independence. The patients' I‐MOVE score pairs recorded by RN1 and RN2 were statistically compared. Interrater reliability, a comparison of the 41 patients' score pairs, is graphically represented in Figure 2. The calculated intraclass correlation coefficient (r) was 0.90, indicating excellent agreement (r > 0.75).

Figure 2

Discussion

Traditional physical examinations by physicians and assessments by nurses do not routinely extend to standardized mobility testing and may fail to recognize disability. Of the existing mobility assessment tools, we believe that most are not suited to patients hospitalized on general medical units. I‐MOVE has been designed to address this need, with an emphasis on practicality and brevity to allow repetition at appropriate intervals (tracking), as is done for vital signs. In this initial study, I‐MOVE was found to have face‐valid content and excellent interrater agreement.

Our study had several limitations. Only 1 pair of test administrators was involved; the sample population was chosen by convenience; clustering of outcomes occurred at level 12, which may have augmented the agreement; and the study was limited to cognitively intact patients. Note that we chose to use the intraclass correlation coefficient rather than the statistic because the weighting between the ordinal I‐MOVE scores has not yet been studied and defined. Also, the weighted is asymptotically equivalent to the intraclass correlation coefficient.

I‐MOVE is intended to aid caregivers in the recognition of debility so that appropriate interventions such as physical therapy may be prescribed. It was designed to complement, not replace, specialized evaluations such as those performed by physical therapists, occupational therapists, or comprehensive geriatric assessments. This practical assessment of basic functioning may enhance communication among caregivers, patients, and patients' family members, especially with regard to discharge planning. Further study is needed to validate I‐MOVE against existing tools, evaluate I‐MOVE's utility as a vital sign, and discern whether a sharp or unexpected decline portends a medical complication.

Hospitalized patients are often debilitated, either from their admitting illness or from the deconditioning that occurs with inactivity. Functional decline, which appears to progress in a hierarchical pattern,1 occurs in 24% to 50% of geriatric patients during hospitalization and is poorly documented.2 Such a decline is associated not only with longer hospital stays and increased health care costs but also with higher mortality.3 The American College of Physicians, through its Assessing Care of Vulnerable Elders project, expressly endorsed gait and mobility evaluation as a quality indicator, and examination insufficiency is well documented.4

Of the several existing mobility assessment tools, few are used routinely in hospital. Some require complex scoring; others require timing and/or a trained occupational therapist.5 We created a simplified tool named Independent Mobility Validation Examination (I‐MOVE) for use by bedside caregivers. We evaluated the tool's face validity and interobserver agreement.

I‐MOVE

I‐MOVE, represented schematically in Figure 1, is a performance test that assesses the patient's ability to perform a sequence of 6 basic tasks: rolling over in bed, sitting up, standing, transferring to a chair, walking in the room, and walking in the hallway. Most motor functions can be assumed to be hierarchical in nature; any patient who can perform at the highest level, such as walking safely, also would be expected to perform at the lowest level.

Figure 1

Instructions for administering I‐MOVE are as follows:

• 1

  Review current orders. Exclude patients ordered on bed rest or non‐weight‐bearing or other orders precluding any of the 6 requested actions.

• 2

  Prepare environment.
• a

  Chair at bedside.

• b

  Lower side bed rail closest to chair.

• c

  Clear path for patient to ambulate.

• d

  Ensure patient dons slippers.

• e

  Flatten bed.

• f

  Ensure any gait assistive device, if generally used by the patient, is within reach from the bedside.

• 3

  Requests for patient action (for steps c through f, make available and within reach any appropriate gait‐assistance device such as walker or cane, if such is customarily used at home or newly prescribed):
• a

  With patient lying supine in bed, with close supervision, ask patient to turn from side to side in bed (request when both bed rails are up).

• b

  Lower side rail closer to chair and ask the patient to rise up to a sitting position and turn to sit up with legs dangling off the bed.

• c

  Ask the patient to stand.

• d

  Ask the patient to take a seat in the chair next to the bed.

• e

  Ask the patient to ambulate in the room.

• f

  Ask the patient to ambulate in the hallway.

• 4

  At any point if the patient seems incapable, unsteady, or unsafe to accomplish the requested task, render hands‐on assistance and immediately end the test.

• 5

  Document, by number (1‐12), the activity level successfully accomplished independently by the patient (even number levels) or accomplished with assistance (odd number levels).

• 6

  Patient may be considered independent if able to perform the activity with a normal assistive device (cane, walker, brace, or crutches) but not using furniture.

• 7

  Assistance is defined as any physical contact with the patient.

Findings

Face Validity

We sent surveys to 6 experienced practicing clinicians at our hospital: a geriatrician, a physiatrist, an exercise physiologist, an occupational therapist, a physical therapist, and a registered nurse. We asked each clinician to rate the 6 I‐MOVE elements (requested actions) for clinical relevance to mobility independence. Relevance of each element was measured on an ordinal scale with scores ranging from 1 to 4, with: 1 not relevant; 2 somewhat relevant; 3 quite relevant; and 4 very relevant. From the 5 responses we received, 4 evaluators ranked all 6 I‐MOVE requested actions as very relevant. The fifth evaluator ranked 5 of the 6 actions as very relevant and 1 action (walking in the room) as quite relevant. These results demonstrate general agreement that I‐MOVE is, at face value, a reasonable measure of independent mobility.

Interrater Reliability

The protocol was approved by the hospital's institutional review board. On a general medical unita non‐electrocardiographic telemetry, nonsurgical unit of an acute care hospital, where patients are assigned the primary service of an internal medicine physicianwe instructed 2 registered nurse (RN) volunteers (RN1 and RN2) in the I‐MOVE protocol. Each RN administered I‐MOVE independently to 41 consecutive, cognitively intact patients in a blinded fashion (ie, neither nurse was aware of the other's scoring of each patient) and within 1 hour of each other's assessment.

After administering I‐MOVE to each patient, the nurse judged and scored the patient's performance using the 12‐level I‐MOVE ordinal scale, ranging from a low value of 1, complete dependence, to the highest value of 12, complete independence. The patients' I‐MOVE score pairs recorded by RN1 and RN2 were statistically compared. Interrater reliability, a comparison of the 41 patients' score pairs, is graphically represented in Figure 2. The calculated intraclass correlation coefficient (r) was 0.90, indicating excellent agreement (r > 0.75).

Figure 2

Discussion

Traditional physical examinations by physicians and assessments by nurses do not routinely extend to standardized mobility testing and may fail to recognize disability. Of the existing mobility assessment tools, we believe that most are not suited to patients hospitalized on general medical units. I‐MOVE has been designed to address this need, with an emphasis on practicality and brevity to allow repetition at appropriate intervals (tracking), as is done for vital signs. In this initial study, I‐MOVE was found to have face‐valid content and excellent interrater agreement.

Our study had several limitations. Only 1 pair of test administrators was involved; the sample population was chosen by convenience; clustering of outcomes occurred at level 12, which may have augmented the agreement; and the study was limited to cognitively intact patients. Note that we chose to use the intraclass correlation coefficient rather than the statistic because the weighting between the ordinal I‐MOVE scores has not yet been studied and defined. Also, the weighted is asymptotically equivalent to the intraclass correlation coefficient.

I‐MOVE is intended to aid caregivers in the recognition of debility so that appropriate interventions such as physical therapy may be prescribed. It was designed to complement, not replace, specialized evaluations such as those performed by physical therapists, occupational therapists, or comprehensive geriatric assessments. This practical assessment of basic functioning may enhance communication among caregivers, patients, and patients' family members, especially with regard to discharge planning. Further study is needed to validate I‐MOVE against existing tools, evaluate I‐MOVE's utility as a vital sign, and discern whether a sharp or unexpected decline portends a medical complication.

Hospitalized patients are often debilitated, either from their admitting illness or from the deconditioning that occurs with inactivity. Functional decline, which appears to progress in a hierarchical pattern,1 occurs in 24% to 50% of geriatric patients during hospitalization and is poorly documented.2 Such a decline is associated not only with longer hospital stays and increased health care costs but also with higher mortality.3 The American College of Physicians, through its Assessing Care of Vulnerable Elders project, expressly endorsed gait and mobility evaluation as a quality indicator, and examination insufficiency is well documented.4

Of the several existing mobility assessment tools, few are used routinely in hospital. Some require complex scoring; others require timing and/or a trained occupational therapist.5 We created a simplified tool named Independent Mobility Validation Examination (I‐MOVE) for use by bedside caregivers. We evaluated the tool's face validity and interobserver agreement.

I‐MOVE

I‐MOVE, represented schematically in Figure 1, is a performance test that assesses the patient's ability to perform a sequence of 6 basic tasks: rolling over in bed, sitting up, standing, transferring to a chair, walking in the room, and walking in the hallway. Most motor functions can be assumed to be hierarchical in nature; any patient who can perform at the highest level, such as walking safely, also would be expected to perform at the lowest level.

Figure 1

Instructions for administering I‐MOVE are as follows:

• 1

  Review current orders. Exclude patients ordered on bed rest or non‐weight‐bearing or other orders precluding any of the 6 requested actions.

• 2

  Prepare environment.
• a

  Chair at bedside.

• b

  Lower side bed rail closest to chair.

• c

  Clear path for patient to ambulate.

• d

  Ensure patient dons slippers.

• e

  Flatten bed.

• f

  Ensure any gait assistive device, if generally used by the patient, is within reach from the bedside.

• 3

  Requests for patient action (for steps c through f, make available and within reach any appropriate gait‐assistance device such as walker or cane, if such is customarily used at home or newly prescribed):
• a

  With patient lying supine in bed, with close supervision, ask patient to turn from side to side in bed (request when both bed rails are up).

• b

  Lower side rail closer to chair and ask the patient to rise up to a sitting position and turn to sit up with legs dangling off the bed.

• c

  Ask the patient to stand.

• d

  Ask the patient to take a seat in the chair next to the bed.

• e

  Ask the patient to ambulate in the room.

• f

  Ask the patient to ambulate in the hallway.

• 4

  At any point if the patient seems incapable, unsteady, or unsafe to accomplish the requested task, render hands‐on assistance and immediately end the test.

• 5

  Document, by number (1‐12), the activity level successfully accomplished independently by the patient (even number levels) or accomplished with assistance (odd number levels).

• 6

  Patient may be considered independent if able to perform the activity with a normal assistive device (cane, walker, brace, or crutches) but not using furniture.

• 7

  Assistance is defined as any physical contact with the patient.

Findings

Face Validity

We sent surveys to 6 experienced practicing clinicians at our hospital: a geriatrician, a physiatrist, an exercise physiologist, an occupational therapist, a physical therapist, and a registered nurse. We asked each clinician to rate the 6 I‐MOVE elements (requested actions) for clinical relevance to mobility independence. Relevance of each element was measured on an ordinal scale with scores ranging from 1 to 4, with: 1 not relevant; 2 somewhat relevant; 3 quite relevant; and 4 very relevant. From the 5 responses we received, 4 evaluators ranked all 6 I‐MOVE requested actions as very relevant. The fifth evaluator ranked 5 of the 6 actions as very relevant and 1 action (walking in the room) as quite relevant. These results demonstrate general agreement that I‐MOVE is, at face value, a reasonable measure of independent mobility.

Interrater Reliability

The protocol was approved by the hospital's institutional review board. On a general medical unita non‐electrocardiographic telemetry, nonsurgical unit of an acute care hospital, where patients are assigned the primary service of an internal medicine physicianwe instructed 2 registered nurse (RN) volunteers (RN1 and RN2) in the I‐MOVE protocol. Each RN administered I‐MOVE independently to 41 consecutive, cognitively intact patients in a blinded fashion (ie, neither nurse was aware of the other's scoring of each patient) and within 1 hour of each other's assessment.

After administering I‐MOVE to each patient, the nurse judged and scored the patient's performance using the 12‐level I‐MOVE ordinal scale, ranging from a low value of 1, complete dependence, to the highest value of 12, complete independence. The patients' I‐MOVE score pairs recorded by RN1 and RN2 were statistically compared. Interrater reliability, a comparison of the 41 patients' score pairs, is graphically represented in Figure 2. The calculated intraclass correlation coefficient (r) was 0.90, indicating excellent agreement (r > 0.75).

Figure 2

Discussion

Traditional physical examinations by physicians and assessments by nurses do not routinely extend to standardized mobility testing and may fail to recognize disability. Of the existing mobility assessment tools, we believe that most are not suited to patients hospitalized on general medical units. I‐MOVE has been designed to address this need, with an emphasis on practicality and brevity to allow repetition at appropriate intervals (tracking), as is done for vital signs. In this initial study, I‐MOVE was found to have face‐valid content and excellent interrater agreement.

Our study had several limitations. Only 1 pair of test administrators was involved; the sample population was chosen by convenience; clustering of outcomes occurred at level 12, which may have augmented the agreement; and the study was limited to cognitively intact patients. Note that we chose to use the intraclass correlation coefficient rather than the statistic because the weighting between the ordinal I‐MOVE scores has not yet been studied and defined. Also, the weighted is asymptotically equivalent to the intraclass correlation coefficient.

I‐MOVE is intended to aid caregivers in the recognition of debility so that appropriate interventions such as physical therapy may be prescribed. It was designed to complement, not replace, specialized evaluations such as those performed by physical therapists, occupational therapists, or comprehensive geriatric assessments. This practical assessment of basic functioning may enhance communication among caregivers, patients, and patients' family members, especially with regard to discharge planning. Further study is needed to validate I‐MOVE against existing tools, evaluate I‐MOVE's utility as a vital sign, and discern whether a sharp or unexpected decline portends a medical complication.

The hospitalist is not typically the hero in contemporary narratives about medical practice. More often, the hospitalist is portrayed as an interloper, a doctor who works for the hospital and not the patient, an employee focused on efficiency and rapid discharge rather than continuous medical care. Elsewhere in this issue, Mai Pham1 offers an updated story in which a hospitalist organizes the loose ends of a patient's medical history and contributes significantly to healthcare coordination.

Hospitalists acknowledge that an admission to the hospital disrupts established outpatient continuity and that discharge can be a perilous event, with potential for medical errors. The Society of Hospital Medicine has recognized discontinuity as enough of a concern that care transitions are considered a core competency for hospital physicians.2 This competency requires hospitalists to be able to move a patient safely from the outpatient setting through the hospital wards and back home again.

As our specialty approaches two decades of practice experience, the work that we do in coordinating medical care and ensuring continuity has evolved and deepened. Initial efforts to coordinate care from the inpatient setting focused on how key hospital events could be best communicated to the patient's primary physician.3, 4 Communication at admission and at critical junctures was encouraged, and research demonstrated that a timely discharge summary sent to the primary care office could decrease hospital readmission.5

Experienced hospitalists recognize, however, that not every inpatient can identify a primary care doctor; sometimes, it is this very lack of established outpatient care that triggers a patient's admission. Reasons for discontinuous prehospital care include disrupted outpatient relationships, particularly as provider networks and insurance status are re‐evaluated, as well as cultural and social barriers. Complex, overcrowded outpatient health systems can be challenging to navigate even for the savviest of patients.

These concerns have helped us to focus on the hospital as a critical setting for delivering continuity of care. The mechanisms for ensuring continuity include, harnessing the inpatient capability for real‐time diagnosis and treatment synthesis, which, in Mai Pham's case,1 enabled decision‐making and timely care coordination for her dying grandmother. Hospitals typically offer an array of tools needed to assist physicians in coordinating a patient's care, including rapid diagnostic testing and simultaneous multidisciplinary evaluation with consulting physicians; nurses; case managers; physical, occupational, and speech therapists; pharmacists; nutritionists; social workers; and palliative care teams. The patient's family members and friends are frequently present in the inpatient setting and can provide additional data points that are not always available in a timely manner in the ambulatory setting. Each of these inpatient interactions can help patients to develop routes of access to healthcare after they are discharged from the hospital.

Despite the advantages of the hospital setting, however, the knock on hospitalists is that we are just on the clock. Frequent handoffs, both when physician shifts change and when a fresh hospitalist rotates on service, present a significant concern to seamless care.6 Increasing fragmentation in hospital staffing may correlate with lengthened hospital stay and increased difficulty in receiving follow‐up outpatient care.7 A new narrative for hospitalists, one focused on enhancing continuity, requires mindfulness toward schedule fragmentation and balances personal desires with the need to maintain a continued presence and availability for patients.

Enhancing continuity and care coordination in the hospital also means continually working to improve provider‐to‐provider communications. Solutions may include well‐executed chart documentation, with active concerns flagged for the oncoming physician, and an electronic medical record that is easy to access from various locations. Computerized templates may enable more thorough handoffs in certain settings.8 As the use of systems and checklists gains traction for their ability to reduce iatrogenic complications and save money,9 hospitalists may come to rely more widely on systems that improve continuity, especially for aspects of inpatient care such as medication reconciliation.10

We believe that the most critical way in which hospitalists can ensure continuous care involves increasing physician efforts to engage with patients during their hospitalization. Hospitalists meet patients at particularly intense and vulnerable times of life, and we have all observed how patients can lose autonomy simply by being hospitalized. In the hospital, things happen to patients, sometimes because of the sheer size and force of the inpatient team and the momentum of a hospital stay.

Yet hospitalists can quickly develop a rapport with their patients through the number and intensity of their patient interactions. The free‐form structure of the inpatient schedule means a flexibility to be present with patients on short notice, to respond to acute events in real time, and to be available to talk with family members and other caregivers at their convenience. Hospitalists can take part in multiple bedside interactions in a single day and on consecutive days. Because of this flexibility, hospitalists can bond with their patients in a short time frame11 as they access critical social and clinical contexts, often more efficiently than possible elsewhere. As one primary care physician wrote when she gave up caring for her hospitalized patients, I know what happened to my patient, but I didn't really experience it with my patient.12 Hospitalists do get to share in this drama.

The medical community has been slow to recognize that hospitalists, as much as any generalist physician, can and do engage patients actively in their medical care. The hospital can be an ideal setting to ensure continuity through real‐time diagnostics and therapeutics and even more so through the intense bonding that can happen between physicians and patients on the wards. The old story of an outpatient provider single‐handedly managing a patient's care is rapidly disappearing in many locales. However, the story of the hospitalist is more than that of the hero in waiting. The story is a cautionary tale, one in which the relationship between the hospitalist and his or her patients is still under development, a tale for which much work remains. As hospitalists, we must continue to refine our skills and systems to deliver continuous care for patients in transition. We must also continue to focus on experiences with our patients and their families and, when called upon, to engage in those challenging conversations that Mai Pham1 says force us to align our expectations of one another. Forging this human connection will always be part of seamless healthcare for every physician, not least for the hospitalist.

The hospitalist is not typically the hero in contemporary narratives about medical practice. More often, the hospitalist is portrayed as an interloper, a doctor who works for the hospital and not the patient, an employee focused on efficiency and rapid discharge rather than continuous medical care. Elsewhere in this issue, Mai Pham1 offers an updated story in which a hospitalist organizes the loose ends of a patient's medical history and contributes significantly to healthcare coordination.

Hospitalists acknowledge that an admission to the hospital disrupts established outpatient continuity and that discharge can be a perilous event, with potential for medical errors. The Society of Hospital Medicine has recognized discontinuity as enough of a concern that care transitions are considered a core competency for hospital physicians.2 This competency requires hospitalists to be able to move a patient safely from the outpatient setting through the hospital wards and back home again.

As our specialty approaches two decades of practice experience, the work that we do in coordinating medical care and ensuring continuity has evolved and deepened. Initial efforts to coordinate care from the inpatient setting focused on how key hospital events could be best communicated to the patient's primary physician.3, 4 Communication at admission and at critical junctures was encouraged, and research demonstrated that a timely discharge summary sent to the primary care office could decrease hospital readmission.5

Experienced hospitalists recognize, however, that not every inpatient can identify a primary care doctor; sometimes, it is this very lack of established outpatient care that triggers a patient's admission. Reasons for discontinuous prehospital care include disrupted outpatient relationships, particularly as provider networks and insurance status are re‐evaluated, as well as cultural and social barriers. Complex, overcrowded outpatient health systems can be challenging to navigate even for the savviest of patients.

These concerns have helped us to focus on the hospital as a critical setting for delivering continuity of care. The mechanisms for ensuring continuity include, harnessing the inpatient capability for real‐time diagnosis and treatment synthesis, which, in Mai Pham's case,1 enabled decision‐making and timely care coordination for her dying grandmother. Hospitals typically offer an array of tools needed to assist physicians in coordinating a patient's care, including rapid diagnostic testing and simultaneous multidisciplinary evaluation with consulting physicians; nurses; case managers; physical, occupational, and speech therapists; pharmacists; nutritionists; social workers; and palliative care teams. The patient's family members and friends are frequently present in the inpatient setting and can provide additional data points that are not always available in a timely manner in the ambulatory setting. Each of these inpatient interactions can help patients to develop routes of access to healthcare after they are discharged from the hospital.

Despite the advantages of the hospital setting, however, the knock on hospitalists is that we are just on the clock. Frequent handoffs, both when physician shifts change and when a fresh hospitalist rotates on service, present a significant concern to seamless care.6 Increasing fragmentation in hospital staffing may correlate with lengthened hospital stay and increased difficulty in receiving follow‐up outpatient care.7 A new narrative for hospitalists, one focused on enhancing continuity, requires mindfulness toward schedule fragmentation and balances personal desires with the need to maintain a continued presence and availability for patients.

Enhancing continuity and care coordination in the hospital also means continually working to improve provider‐to‐provider communications. Solutions may include well‐executed chart documentation, with active concerns flagged for the oncoming physician, and an electronic medical record that is easy to access from various locations. Computerized templates may enable more thorough handoffs in certain settings.8 As the use of systems and checklists gains traction for their ability to reduce iatrogenic complications and save money,9 hospitalists may come to rely more widely on systems that improve continuity, especially for aspects of inpatient care such as medication reconciliation.10

We believe that the most critical way in which hospitalists can ensure continuous care involves increasing physician efforts to engage with patients during their hospitalization. Hospitalists meet patients at particularly intense and vulnerable times of life, and we have all observed how patients can lose autonomy simply by being hospitalized. In the hospital, things happen to patients, sometimes because of the sheer size and force of the inpatient team and the momentum of a hospital stay.

Yet hospitalists can quickly develop a rapport with their patients through the number and intensity of their patient interactions. The free‐form structure of the inpatient schedule means a flexibility to be present with patients on short notice, to respond to acute events in real time, and to be available to talk with family members and other caregivers at their convenience. Hospitalists can take part in multiple bedside interactions in a single day and on consecutive days. Because of this flexibility, hospitalists can bond with their patients in a short time frame11 as they access critical social and clinical contexts, often more efficiently than possible elsewhere. As one primary care physician wrote when she gave up caring for her hospitalized patients, I know what happened to my patient, but I didn't really experience it with my patient.12 Hospitalists do get to share in this drama.

The medical community has been slow to recognize that hospitalists, as much as any generalist physician, can and do engage patients actively in their medical care. The hospital can be an ideal setting to ensure continuity through real‐time diagnostics and therapeutics and even more so through the intense bonding that can happen between physicians and patients on the wards. The old story of an outpatient provider single‐handedly managing a patient's care is rapidly disappearing in many locales. However, the story of the hospitalist is more than that of the hero in waiting. The story is a cautionary tale, one in which the relationship between the hospitalist and his or her patients is still under development, a tale for which much work remains. As hospitalists, we must continue to refine our skills and systems to deliver continuous care for patients in transition. We must also continue to focus on experiences with our patients and their families and, when called upon, to engage in those challenging conversations that Mai Pham1 says force us to align our expectations of one another. Forging this human connection will always be part of seamless healthcare for every physician, not least for the hospitalist.

The hospitalist is not typically the hero in contemporary narratives about medical practice. More often, the hospitalist is portrayed as an interloper, a doctor who works for the hospital and not the patient, an employee focused on efficiency and rapid discharge rather than continuous medical care. Elsewhere in this issue, Mai Pham1 offers an updated story in which a hospitalist organizes the loose ends of a patient's medical history and contributes significantly to healthcare coordination.

Hospitalists acknowledge that an admission to the hospital disrupts established outpatient continuity and that discharge can be a perilous event, with potential for medical errors. The Society of Hospital Medicine has recognized discontinuity as enough of a concern that care transitions are considered a core competency for hospital physicians.2 This competency requires hospitalists to be able to move a patient safely from the outpatient setting through the hospital wards and back home again.

As our specialty approaches two decades of practice experience, the work that we do in coordinating medical care and ensuring continuity has evolved and deepened. Initial efforts to coordinate care from the inpatient setting focused on how key hospital events could be best communicated to the patient's primary physician.3, 4 Communication at admission and at critical junctures was encouraged, and research demonstrated that a timely discharge summary sent to the primary care office could decrease hospital readmission.5

Experienced hospitalists recognize, however, that not every inpatient can identify a primary care doctor; sometimes, it is this very lack of established outpatient care that triggers a patient's admission. Reasons for discontinuous prehospital care include disrupted outpatient relationships, particularly as provider networks and insurance status are re‐evaluated, as well as cultural and social barriers. Complex, overcrowded outpatient health systems can be challenging to navigate even for the savviest of patients.

These concerns have helped us to focus on the hospital as a critical setting for delivering continuity of care. The mechanisms for ensuring continuity include, harnessing the inpatient capability for real‐time diagnosis and treatment synthesis, which, in Mai Pham's case,1 enabled decision‐making and timely care coordination for her dying grandmother. Hospitals typically offer an array of tools needed to assist physicians in coordinating a patient's care, including rapid diagnostic testing and simultaneous multidisciplinary evaluation with consulting physicians; nurses; case managers; physical, occupational, and speech therapists; pharmacists; nutritionists; social workers; and palliative care teams. The patient's family members and friends are frequently present in the inpatient setting and can provide additional data points that are not always available in a timely manner in the ambulatory setting. Each of these inpatient interactions can help patients to develop routes of access to healthcare after they are discharged from the hospital.

Despite the advantages of the hospital setting, however, the knock on hospitalists is that we are just on the clock. Frequent handoffs, both when physician shifts change and when a fresh hospitalist rotates on service, present a significant concern to seamless care.6 Increasing fragmentation in hospital staffing may correlate with lengthened hospital stay and increased difficulty in receiving follow‐up outpatient care.7 A new narrative for hospitalists, one focused on enhancing continuity, requires mindfulness toward schedule fragmentation and balances personal desires with the need to maintain a continued presence and availability for patients.

Enhancing continuity and care coordination in the hospital also means continually working to improve provider‐to‐provider communications. Solutions may include well‐executed chart documentation, with active concerns flagged for the oncoming physician, and an electronic medical record that is easy to access from various locations. Computerized templates may enable more thorough handoffs in certain settings.8 As the use of systems and checklists gains traction for their ability to reduce iatrogenic complications and save money,9 hospitalists may come to rely more widely on systems that improve continuity, especially for aspects of inpatient care such as medication reconciliation.10

We believe that the most critical way in which hospitalists can ensure continuous care involves increasing physician efforts to engage with patients during their hospitalization. Hospitalists meet patients at particularly intense and vulnerable times of life, and we have all observed how patients can lose autonomy simply by being hospitalized. In the hospital, things happen to patients, sometimes because of the sheer size and force of the inpatient team and the momentum of a hospital stay.

Yet hospitalists can quickly develop a rapport with their patients through the number and intensity of their patient interactions. The free‐form structure of the inpatient schedule means a flexibility to be present with patients on short notice, to respond to acute events in real time, and to be available to talk with family members and other caregivers at their convenience. Hospitalists can take part in multiple bedside interactions in a single day and on consecutive days. Because of this flexibility, hospitalists can bond with their patients in a short time frame11 as they access critical social and clinical contexts, often more efficiently than possible elsewhere. As one primary care physician wrote when she gave up caring for her hospitalized patients, I know what happened to my patient, but I didn't really experience it with my patient.12 Hospitalists do get to share in this drama.

The medical community has been slow to recognize that hospitalists, as much as any generalist physician, can and do engage patients actively in their medical care. The hospital can be an ideal setting to ensure continuity through real‐time diagnostics and therapeutics and even more so through the intense bonding that can happen between physicians and patients on the wards. The old story of an outpatient provider single‐handedly managing a patient's care is rapidly disappearing in many locales. However, the story of the hospitalist is more than that of the hero in waiting. The story is a cautionary tale, one in which the relationship between the hospitalist and his or her patients is still under development, a tale for which much work remains. As hospitalists, we must continue to refine our skills and systems to deliver continuous care for patients in transition. We must also continue to focus on experiences with our patients and their families and, when called upon, to engage in those challenging conversations that Mai Pham1 says force us to align our expectations of one another. Forging this human connection will always be part of seamless healthcare for every physician, not least for the hospitalist.

Elderly patients (aged 65 years and older) consume a disproportionate amount of acute health care resources, composing up to 20% of emergency department (ED) visits,1, 2 having a 2‐fold to 5‐fold increase in likelihood of hospital admission,1 and frequently incurring lengths of hospital stay (LOS) approximately 15% higher than the national averages.3 In addition, they are at increased risk for hospital readmission in the 90‐day interval following hospital discharge.1, 4, 5 Specific risk factors for readmission include age above 80 years, discharge within the previous 30 days, the presence of 3 or more comorbid diagnoses, use of 5 or more prescription medications, difficulty with at least 1 activity of daily living (ADL), and lack of discharge education.6 These risk factors can translate into adverse drug events,79 exacerbations of chronic diseases,10 or functional decline4, 5 that can trigger ED visits or hospital readmission.

Hospital‐based care coordinationdefined as a multidisciplinary interaction between inpatients and providers that focuses on education, communication, and discharge planning with the primary aim of improving outcomeshas demonstrated inconsistent results as a mechanism to reduce LOS, postdischarge ED visits, or hospital readmission rates. While disease‐specific care coordination programs for congestive heart failure and chronic obstructive pulmonary disease have been effective in reducing rehospitalization rates,1015 the benefits of comprehensive care coordination for elderly general medical inpatients with a broader range of diagnoses are less clear. In a group of 750 elderly patients with 1 of 11 common inpatient diagnoses (such as stroke or hip fracture) likely to ultimately require a high level of home support, Coleman et al.16 found that a structured transitional care program centered on a personal coach decreased rehospitalization rates at 30 and 90 days. Preen et al.17 found improved patient involvement and perceived quality of life with care coordination focused on discharge planning, but no impact on LOS. Likewise, a recent meta‐analysis18 failed to demonstrate statistically significant differences in mortality, LOS, or readmission rates in hospitalized patients who received intensive care coordination versus usual care; however, variation in the components of the care coordination intervention and reported outcomes restricted the ability to pool data in this study.

Care coordination programs demonstrating efficacy in reducing health care utilization in elderly medical patients have generally included an outpatient transitional component with out‐of‐hospital postacute care visits by health care personnel such as a nurse, pharmacist, or physician.1923 These offsite interventions generate additional expenses and resource demands that may not be practical for smaller hospitals to implement.24, 25 In contrast, hospital‐based care coordination programs have clear ownership and thus may be more practical to disseminate. Individual elements of hospital‐based care coordination such as pharmacist counseling, discharge education, and telephone follow‐up have been shown to reduce ED visitation and readmission rates in high‐risk elderly patients. Less information is available regarding the impact of these interventions delivered in an aggregate bundle by hospital staff in the absence of bridging transitional visits.2629

The objective of this pilot study was to determine whether a supplemental elderly care bundle, targeted to high‐risk inpatients by hospital staff as an enhancement to existing care coordination, would affect postdischarge readmission and ED visit rates. The intervention was designed to capitalize on existing resources, and focused specifically on elderly inpatients who were hospitalized with diagnoses commonly encountered in a general medical unit and predisposed to recidivism.

Patients and Methods

Delivery of the Supplemental Care Bundle

Starting no later than 24 hours after enrollment and continuing up to 1 week following hospital discharge, intervention group patients received a targeted care bundle provided by 1 of 3 care coordinators (CCs) and 1 of 4 clinical pharmacists (CPs) working with the study team. The care bundle was designed as an intensive patient‐centered educational program that would augment BUMC's existing care coordination processes (delivered to all patients regardless of study participation); specific elements are displayed in Figure 1. Study CCs saw patients daily throughout their hospital stay, and instructed patients on specific health conditions, with an emphasis on optimizing home self‐care and contingency plans if problems arose. CP visits focused on medication reconciliation and education regarding any new agents started during the hospitalization. The personal health record (PHR) provided a tool to engage patients in self‐care, and as discussed by Coleman et al.,7, 16, 33 promoted information transfer from the hospital to outpatient settings. During the postdischarge phone call, CCs followed a basic script to confirm receipt of medical equipment, medications, home health arrangements, and scheduling of follow‐up appointments. They also used this contact as an opportunity to reinforce patient education on managing their conditions. CPs reviewed medication use (type, schedule, dose), and spoke with patients about any symptoms they may have experienced as medication side effects. If indicated based on their phone discussions, both CCs and CPs could recommend an action plan to the patient.

Figure 1

The study CCs and CPs were existing hospital staff and performed their research activities in addition to their usual duties. Study CCs were highly experienced (averaging 8 years of inpatient floor nursing plus 10 years as CCs) and all had advanced nursing certifications (ACM, BSN, or MSN). The CPs were upper‐level pharmacy residents completing their inpatient clinical rotations. Additional training for both study CCs and CPs was limited to a series of 3 meetings (each 45 minutes in duration) regarding the intent and delivery of the supplemental care bundle, including use of study forms.

At the time of the trial, the particular CCs and CPs chosen to deliver the supplemental care bundle had work assignments ensuring that crossover between intervention and usual care groups would not occur. For example, 1 of the study CCs normally covered a surgical floor such that her normal scope of responsibilities would not influence the medical patients in the study (their baseline care coordination was provided by nonstudy personnel). Medication reconciliation and medication education is generally performed by floor nursing staff rather than CPs at BUMC.

Results

The final sample size for this pilot was small, with 41 total patients (21 controls, 20 interventions). The main reason for enrollment failure of patients meeting study criteria was an inability to obtain informed consent. Sixty patients declined participation after being approached, and another 56 patients were unable to give their informed consent due to impairments (poor cognition, medication induced sedation, severity of illness) with lack of an available proxy to give written consent during the 72‐hour postadmission recruitment window. There were no statistically significant differences in the baseline characteristics of the intervention and control groups (Table 2). A similar proportion of patients (23% in the intervention, 15% in controls; P = 0.70) had preexisting diagnoses of dementia or depression. However, on APR‐DRG measures relating to acuity of illness and mortality risk, patients in the intervention group trended toward higher severity (Table 2). Likewise, although it was not a statistically significant difference, 13 of 20 patients in the intervention group were taking medications from 2 drug classes commonly implicated in adverse drug events (warfarin, insulin, diuretics, sedating agents) as part of their discharge medication regimen compared to 10 of 21 patients in the control group.

Study outcomes are displayed in Table 3. Mean LOS is reported as a descriptive finding; there was insufficient power to compare this outcome statistically between groups. The majority of patients were discharged to home. A similar proportion of patients in the intervention (20%) and control groups (22%) who had lived at home immediately prior to admission were discharged from the hospital to skilled care facilities (P = 0.87). The number of readmissions/ED visits (taken as a composite measure of unplanned healthcare utilization) within 30 days of discharge was lower in the intervention group; by 60 days, there was no longer a statistically significant difference in readmission/ED visit rates between groups. For those patients who had a readmission or ED visit following hospital discharge, the intervention group had a longer time interval to first event compared to controls (36.2 versus 15.7 days, P = 0.05). Of the patients discharged to skilled care, 1 in the intervention group (at 53 days) and 1 in the control group (at 16 days) had a readmission/ED visit event. Figure 2 shows time‐to‐first readmission or ED visit event curves at 30 and 60 days for both intervention and control groups. For patients who had a readmission/ED visit event, LOS for this episode was 2.2 2.1 days in controls and 3.7 2.1 days in the intervention group (insufficient power for statistical comparison). The study's small sample size prevented development of a meaningful regression model.

Figure 2

Resource utilization and the specifics of patient‐study personnel interaction associated with the intervention were tracked. Research assistants spent an average of 50 minutes daily screening charts for potential candidates. For the 20 patients who received the supplemental elderly care bundle, study CCs averaged 20 to 25 minutes per patient daily of additional time counseling patients and families, identifying and attending to discharge barriers, filling out documentation, and faxing the supplemental study discharge form to the patient's primary care physician. Any residual home care needs or issues unresolved at discharge were addressed with the patient in the 5 to 7 day follow‐up phone call. Similarly, study CPs expended approximately 20 minutes daily per patient providing medication education, reconciliation, and optimization of drug therapy. Study pharmacists recommended a change to the medication regimens of 10 patients in the intervention group; physicians acted upon these recommendations for 7 of the patients. The changes included dosage adjustment, discontinuation of medications due to possible drug interaction or duplication of drugs with the same pharmacologic effect, and addition of medications as indicated by patient condition or to reconcile with patients' at‐home medication regimens. Patients contacted via phone by the study pharmacist within 1 week after discharge were able to describe proper use of new medications started in the hospital and confirm that they obtained or had the means to obtain the prescribed drugs.

Discussion

This pilot study examined the effects of a supplemental care bundle involving patient education and discharge planning delivered by hospital‐based CCs and CPs on the rate of readmission/ED visitation in 41 elderly (70 years of age) patients. The study was not adequately powered to detect an impact of the intervention on index LOS. The care bundle did lead to significantly fewer readmissions or ED visits 30 days postdischarge and appeared to increase the time interval to first unplanned readmission or ED visit compared to usual care. This effect was no longer present at 60 days postdischarge. Resource allocations and scope of duties for CCs and CPs (an average of 20 minutes per patient per day) related to delivering the intervention were realistic for broader implementation in the hospitalized elderly population at high risk for readmission or ED visitation following discharge.

Length of stay for the initial hospitalization associated with the care bundle was an original outcome of interest to the study team. However, with the final enrollment of 41 patients and a power of 0.8, the between group difference would have needed to be 2.6 days to be statistically significant. It is likely that any change in LOS related to the care bundle would be much smaller, particularly since 2 key determinants of LOS, severity of illness and physician behavior, were beyond this patient education‐oriented intervention's scope of influence.3437 Furthermore, the diverse range of eligible diagnoses limited the study CCs' ability to reduce variability through use of clinical care pathways. One approach in leveraging an elderly care bundle to reduce LOS may be to focus on a specific disease that has well‐established inpatient benchmarks and treatment algorithms. For example, in patients with community‐acquired pneumonia, the use of care coordination in combination with standardized order sets decreased LOS without compromising safety, mainly by shortening the time from clinical stability to discharge.38

On separation of the readmission/ED visit outcome into 30 and 60 day postdischarge time frames, the intervention group had a lower rate of unplanned acute health care use within 30 days postdischarge; the difference between groups had dissipated by 60 days postdischarge. This convergence suggests that a hospital‐based intervention's influence is strongest closer to the time of the initial hospital stay, and wanes as more time has elapsed. Indeed, interventions that have successfully maintained lower readmission rates beyond 60 and 90 days postdischarge in a high‐risk elderly population (such as the program advocated by Coleman et al.16) have included a transitional care provider engaging patients during the hospitalization and performing subsequent visits to the home or nursing facility.33 An optimal intervention would capitalize on the hospital‐based staff's ability to improve short‐term readmission/ED visit rates while linking patients to longer‐term transitional care to extend these outcomes. Electronic health records could potentially facilitate these care transitions, beginning with an automated screening process for identification of high‐risk inpatients and continuing through postdischarge follow‐up. How to develop these resources in settings where outpatient practices are independent or only loosely affiliated with hospitals is an area for continued investigation.

In a group of elderly patients with multiple comorbidities and complex pharmacotherapy regimens, the study bundle component targeting medication management appears to be a high‐yield intervention to reduce unplanned health care utilization following hospital discharge. These patients are more susceptible to nonadherence and drug‐related adverse events, which may contribute to hospital readmission or ED visitation.7, 9, 39 Consistent with findings at other sites,28, 40 a heightened level of CP involvement in the care of high‐risk elderly patients may have helped reduce these undesirable outcomes. Of the 9 readmission/ED visit events in the control group, 3 were attributable to medication related complications (2 from sedatives, 1 from a diuretic). None of the readmission/ED visit events in intervention group patients stemmed from medication effects.

Correspondingly, the research CCs' provision of daily condition‐specific education, additional time to more thoroughly investigate discharge needs, engagement of patients' families as active partners in self‐care, and the use of a structured discharge form along with follow‐up phone calls may have better prepared patients to manage their health problems once released from the hospital.26, 28, 29 For example, 1 patient in the control group was readmitted less than 24 hours after initial discharge due to inability to perform self‐care at home. Given the study power issues described previously, data on LOS for the second hospitalization for patients who had a readmission event are difficult to interpret, but could suggest the occurrence of some shorter, preventable readmissions in the control group. Conversely, the readmission/ED visit events in intervention patients appeared to be associated with a specific medical condition (eg, failure of diabetic cellulitis to respond to appropriate outpatient treatment) rather than problems that would have been corrected with an educational/self‐management program such as this targeted care bundle.

This pilot study had several limitations. The main issue was a small patient sample size that was primarily due to an inability to obtain informed consent. Design of the study as a randomized controlled trial and plans to disseminate study findings beyond BHCS necessitated IRB approval rather than delivery of the supplemental care bundle as a quality improvement (QI) project. Placing QI initiatives under research regulations can lead to project delays, higher costs, and patient frustrations with the process.41, 42 This tension was evident during study screening and enrollment, as many patients who otherwise met criteria and would potentially benefit from the intervention were hesitant to participate in a research study or refused to sign a multipage consent document. The difficulties of enrolling elderly patients in clinical trials have been well‐described.43, 44 Further research involving a minimal‐risk, educational intervention such as this elderly care‐bundle would likely better fit under the category of expedited IRB review with waiver or modification of the informed consent process.45

Incomplete blinding could have potentially affected our results. At the study site, the team members delivering the care bundle were a regular part of the hospital staff (as opposed to external researchers), and it is not unusual for a CC or a pharmacist to enter a patient's room (eg, to confirm a drug allergy history). In view of this, the impact of imperfect blinding on 30‐day outcomes would likely be minimal. Furthermore, a floor staff perception that a specific patient was being taken care of by the study team resulting in a lower than usual level of care, would tend to bias the result of the intervention toward the null effect.

vThe study cohort did not have enough subjects to perform analyses (ie, modeling or examination of subgroups) beyond basic comparative findings. Issues such as preadmission living situation and the presence of depression or cognitive impairment (Mini‐Mental Status Exams were not performed on these patients) may potentially influence postdischarge recidivism; their effects can not be reliably ascertained from these data. Additionally, to prevent study personnel from engaging patients who would soon be going home, it was felt that the benefits from the care bundle would be recognized only if the intervention could be initiated within 72 hours of admission and delivered in full, a requirement that further reduced the enrollment pool. The intent of this pilot work was to guide future investigations surrounding hospital‐to‐home transitional care. The next phase of research in this area will need an enhanced sample size with more extensive baseline data collection so that potential confounding factors or outcomes in specific populations can be explored.

Another problem restricting applicability of study findings was the use of only 3 different CCs and 3 pharmacists on the research team to deliver the components of patient education, discharge planning, and medication counseling in the elderly care bundle. Personnel for the trial were chosen for their experience and interest in the area of care transitions. To distinguish the benefit of the elderly care bundle in general versus the expertise of these particular CCs and study pharmacists, a larger‐scale, multisite trial would be necessary. Lastly, due to resource constraints, patients who resided in long‐term care (either LTAC, SNFs, or nursing homes) prior to admission with anticipated return to those sites were not eligible for the study. Similar to the patients whose comorbidities or acute severity of illness prevented informed consent, this segment of the elderly population may have derived even more benefit from receipt of the elderly care bundle.10, 15, 46 Despite exclusion of this group (which would be expected to lessen the impact of the intervention), a difference in readmission/ED visits rates at 30 days following discharge was observed.

Conclusions

This pilot randomized clinical trial (RCT) evaluated the effects of a supplemental, aggregate care bundle centered on patient education, discharge planning, and medication counseling and reconciliation compared to usual care in a group of elderly patients at high risk of readmission or ED visitation following an index hospitalization. The intervention was designed to be reproducible and make use of existing hospital resources. Probably through facilitation of patient self‐care and home management, the elderly care bundle reduced the composite outcome of readmission/ED visits at 30 days postdischarge. By 60 days, this effect had waned, demonstrating the short‐term benefit of a hospital‐based educational intervention and stressing the need to incorporate additional outpatient transitional care support to sustain favorable outcomes. The study was not powered to detect small differences (which would be more likely than a change of multiple days) in length of index hospital stay related to the care bundle. There were important study limitations (primarily associated with small sample size), and this work should be viewed as hypothesis‐generating. Future trials should assess the impact of a standardized targeted care bundle delivered across multiple healthcare systems on a larger cohort of high‐risk elderly patients, including analysis of financial and personnel allocations relative to the benefits of the intervention.

Elderly patients (aged 65 years and older) consume a disproportionate amount of acute health care resources, composing up to 20% of emergency department (ED) visits,1, 2 having a 2‐fold to 5‐fold increase in likelihood of hospital admission,1 and frequently incurring lengths of hospital stay (LOS) approximately 15% higher than the national averages.3 In addition, they are at increased risk for hospital readmission in the 90‐day interval following hospital discharge.1, 4, 5 Specific risk factors for readmission include age above 80 years, discharge within the previous 30 days, the presence of 3 or more comorbid diagnoses, use of 5 or more prescription medications, difficulty with at least 1 activity of daily living (ADL), and lack of discharge education.6 These risk factors can translate into adverse drug events,79 exacerbations of chronic diseases,10 or functional decline4, 5 that can trigger ED visits or hospital readmission.

Hospital‐based care coordinationdefined as a multidisciplinary interaction between inpatients and providers that focuses on education, communication, and discharge planning with the primary aim of improving outcomeshas demonstrated inconsistent results as a mechanism to reduce LOS, postdischarge ED visits, or hospital readmission rates. While disease‐specific care coordination programs for congestive heart failure and chronic obstructive pulmonary disease have been effective in reducing rehospitalization rates,1015 the benefits of comprehensive care coordination for elderly general medical inpatients with a broader range of diagnoses are less clear. In a group of 750 elderly patients with 1 of 11 common inpatient diagnoses (such as stroke or hip fracture) likely to ultimately require a high level of home support, Coleman et al.16 found that a structured transitional care program centered on a personal coach decreased rehospitalization rates at 30 and 90 days. Preen et al.17 found improved patient involvement and perceived quality of life with care coordination focused on discharge planning, but no impact on LOS. Likewise, a recent meta‐analysis18 failed to demonstrate statistically significant differences in mortality, LOS, or readmission rates in hospitalized patients who received intensive care coordination versus usual care; however, variation in the components of the care coordination intervention and reported outcomes restricted the ability to pool data in this study.

Care coordination programs demonstrating efficacy in reducing health care utilization in elderly medical patients have generally included an outpatient transitional component with out‐of‐hospital postacute care visits by health care personnel such as a nurse, pharmacist, or physician.1923 These offsite interventions generate additional expenses and resource demands that may not be practical for smaller hospitals to implement.24, 25 In contrast, hospital‐based care coordination programs have clear ownership and thus may be more practical to disseminate. Individual elements of hospital‐based care coordination such as pharmacist counseling, discharge education, and telephone follow‐up have been shown to reduce ED visitation and readmission rates in high‐risk elderly patients. Less information is available regarding the impact of these interventions delivered in an aggregate bundle by hospital staff in the absence of bridging transitional visits.2629

The objective of this pilot study was to determine whether a supplemental elderly care bundle, targeted to high‐risk inpatients by hospital staff as an enhancement to existing care coordination, would affect postdischarge readmission and ED visit rates. The intervention was designed to capitalize on existing resources, and focused specifically on elderly inpatients who were hospitalized with diagnoses commonly encountered in a general medical unit and predisposed to recidivism.

Patients and Methods

Delivery of the Supplemental Care Bundle

Starting no later than 24 hours after enrollment and continuing up to 1 week following hospital discharge, intervention group patients received a targeted care bundle provided by 1 of 3 care coordinators (CCs) and 1 of 4 clinical pharmacists (CPs) working with the study team. The care bundle was designed as an intensive patient‐centered educational program that would augment BUMC's existing care coordination processes (delivered to all patients regardless of study participation); specific elements are displayed in Figure 1. Study CCs saw patients daily throughout their hospital stay, and instructed patients on specific health conditions, with an emphasis on optimizing home self‐care and contingency plans if problems arose. CP visits focused on medication reconciliation and education regarding any new agents started during the hospitalization. The personal health record (PHR) provided a tool to engage patients in self‐care, and as discussed by Coleman et al.,7, 16, 33 promoted information transfer from the hospital to outpatient settings. During the postdischarge phone call, CCs followed a basic script to confirm receipt of medical equipment, medications, home health arrangements, and scheduling of follow‐up appointments. They also used this contact as an opportunity to reinforce patient education on managing their conditions. CPs reviewed medication use (type, schedule, dose), and spoke with patients about any symptoms they may have experienced as medication side effects. If indicated based on their phone discussions, both CCs and CPs could recommend an action plan to the patient.

Figure 1

The study CCs and CPs were existing hospital staff and performed their research activities in addition to their usual duties. Study CCs were highly experienced (averaging 8 years of inpatient floor nursing plus 10 years as CCs) and all had advanced nursing certifications (ACM, BSN, or MSN). The CPs were upper‐level pharmacy residents completing their inpatient clinical rotations. Additional training for both study CCs and CPs was limited to a series of 3 meetings (each 45 minutes in duration) regarding the intent and delivery of the supplemental care bundle, including use of study forms.

At the time of the trial, the particular CCs and CPs chosen to deliver the supplemental care bundle had work assignments ensuring that crossover between intervention and usual care groups would not occur. For example, 1 of the study CCs normally covered a surgical floor such that her normal scope of responsibilities would not influence the medical patients in the study (their baseline care coordination was provided by nonstudy personnel). Medication reconciliation and medication education is generally performed by floor nursing staff rather than CPs at BUMC.

Results

The final sample size for this pilot was small, with 41 total patients (21 controls, 20 interventions). The main reason for enrollment failure of patients meeting study criteria was an inability to obtain informed consent. Sixty patients declined participation after being approached, and another 56 patients were unable to give their informed consent due to impairments (poor cognition, medication induced sedation, severity of illness) with lack of an available proxy to give written consent during the 72‐hour postadmission recruitment window. There were no statistically significant differences in the baseline characteristics of the intervention and control groups (Table 2). A similar proportion of patients (23% in the intervention, 15% in controls; P = 0.70) had preexisting diagnoses of dementia or depression. However, on APR‐DRG measures relating to acuity of illness and mortality risk, patients in the intervention group trended toward higher severity (Table 2). Likewise, although it was not a statistically significant difference, 13 of 20 patients in the intervention group were taking medications from 2 drug classes commonly implicated in adverse drug events (warfarin, insulin, diuretics, sedating agents) as part of their discharge medication regimen compared to 10 of 21 patients in the control group.

Study outcomes are displayed in Table 3. Mean LOS is reported as a descriptive finding; there was insufficient power to compare this outcome statistically between groups. The majority of patients were discharged to home. A similar proportion of patients in the intervention (20%) and control groups (22%) who had lived at home immediately prior to admission were discharged from the hospital to skilled care facilities (P = 0.87). The number of readmissions/ED visits (taken as a composite measure of unplanned healthcare utilization) within 30 days of discharge was lower in the intervention group; by 60 days, there was no longer a statistically significant difference in readmission/ED visit rates between groups. For those patients who had a readmission or ED visit following hospital discharge, the intervention group had a longer time interval to first event compared to controls (36.2 versus 15.7 days, P = 0.05). Of the patients discharged to skilled care, 1 in the intervention group (at 53 days) and 1 in the control group (at 16 days) had a readmission/ED visit event. Figure 2 shows time‐to‐first readmission or ED visit event curves at 30 and 60 days for both intervention and control groups. For patients who had a readmission/ED visit event, LOS for this episode was 2.2 2.1 days in controls and 3.7 2.1 days in the intervention group (insufficient power for statistical comparison). The study's small sample size prevented development of a meaningful regression model.

Figure 2

Resource utilization and the specifics of patient‐study personnel interaction associated with the intervention were tracked. Research assistants spent an average of 50 minutes daily screening charts for potential candidates. For the 20 patients who received the supplemental elderly care bundle, study CCs averaged 20 to 25 minutes per patient daily of additional time counseling patients and families, identifying and attending to discharge barriers, filling out documentation, and faxing the supplemental study discharge form to the patient's primary care physician. Any residual home care needs or issues unresolved at discharge were addressed with the patient in the 5 to 7 day follow‐up phone call. Similarly, study CPs expended approximately 20 minutes daily per patient providing medication education, reconciliation, and optimization of drug therapy. Study pharmacists recommended a change to the medication regimens of 10 patients in the intervention group; physicians acted upon these recommendations for 7 of the patients. The changes included dosage adjustment, discontinuation of medications due to possible drug interaction or duplication of drugs with the same pharmacologic effect, and addition of medications as indicated by patient condition or to reconcile with patients' at‐home medication regimens. Patients contacted via phone by the study pharmacist within 1 week after discharge were able to describe proper use of new medications started in the hospital and confirm that they obtained or had the means to obtain the prescribed drugs.

Discussion

This pilot study examined the effects of a supplemental care bundle involving patient education and discharge planning delivered by hospital‐based CCs and CPs on the rate of readmission/ED visitation in 41 elderly (70 years of age) patients. The study was not adequately powered to detect an impact of the intervention on index LOS. The care bundle did lead to significantly fewer readmissions or ED visits 30 days postdischarge and appeared to increase the time interval to first unplanned readmission or ED visit compared to usual care. This effect was no longer present at 60 days postdischarge. Resource allocations and scope of duties for CCs and CPs (an average of 20 minutes per patient per day) related to delivering the intervention were realistic for broader implementation in the hospitalized elderly population at high risk for readmission or ED visitation following discharge.

Length of stay for the initial hospitalization associated with the care bundle was an original outcome of interest to the study team. However, with the final enrollment of 41 patients and a power of 0.8, the between group difference would have needed to be 2.6 days to be statistically significant. It is likely that any change in LOS related to the care bundle would be much smaller, particularly since 2 key determinants of LOS, severity of illness and physician behavior, were beyond this patient education‐oriented intervention's scope of influence.3437 Furthermore, the diverse range of eligible diagnoses limited the study CCs' ability to reduce variability through use of clinical care pathways. One approach in leveraging an elderly care bundle to reduce LOS may be to focus on a specific disease that has well‐established inpatient benchmarks and treatment algorithms. For example, in patients with community‐acquired pneumonia, the use of care coordination in combination with standardized order sets decreased LOS without compromising safety, mainly by shortening the time from clinical stability to discharge.38

On separation of the readmission/ED visit outcome into 30 and 60 day postdischarge time frames, the intervention group had a lower rate of unplanned acute health care use within 30 days postdischarge; the difference between groups had dissipated by 60 days postdischarge. This convergence suggests that a hospital‐based intervention's influence is strongest closer to the time of the initial hospital stay, and wanes as more time has elapsed. Indeed, interventions that have successfully maintained lower readmission rates beyond 60 and 90 days postdischarge in a high‐risk elderly population (such as the program advocated by Coleman et al.16) have included a transitional care provider engaging patients during the hospitalization and performing subsequent visits to the home or nursing facility.33 An optimal intervention would capitalize on the hospital‐based staff's ability to improve short‐term readmission/ED visit rates while linking patients to longer‐term transitional care to extend these outcomes. Electronic health records could potentially facilitate these care transitions, beginning with an automated screening process for identification of high‐risk inpatients and continuing through postdischarge follow‐up. How to develop these resources in settings where outpatient practices are independent or only loosely affiliated with hospitals is an area for continued investigation.

In a group of elderly patients with multiple comorbidities and complex pharmacotherapy regimens, the study bundle component targeting medication management appears to be a high‐yield intervention to reduce unplanned health care utilization following hospital discharge. These patients are more susceptible to nonadherence and drug‐related adverse events, which may contribute to hospital readmission or ED visitation.7, 9, 39 Consistent with findings at other sites,28, 40 a heightened level of CP involvement in the care of high‐risk elderly patients may have helped reduce these undesirable outcomes. Of the 9 readmission/ED visit events in the control group, 3 were attributable to medication related complications (2 from sedatives, 1 from a diuretic). None of the readmission/ED visit events in intervention group patients stemmed from medication effects.

Correspondingly, the research CCs' provision of daily condition‐specific education, additional time to more thoroughly investigate discharge needs, engagement of patients' families as active partners in self‐care, and the use of a structured discharge form along with follow‐up phone calls may have better prepared patients to manage their health problems once released from the hospital.26, 28, 29 For example, 1 patient in the control group was readmitted less than 24 hours after initial discharge due to inability to perform self‐care at home. Given the study power issues described previously, data on LOS for the second hospitalization for patients who had a readmission event are difficult to interpret, but could suggest the occurrence of some shorter, preventable readmissions in the control group. Conversely, the readmission/ED visit events in intervention patients appeared to be associated with a specific medical condition (eg, failure of diabetic cellulitis to respond to appropriate outpatient treatment) rather than problems that would have been corrected with an educational/self‐management program such as this targeted care bundle.

This pilot study had several limitations. The main issue was a small patient sample size that was primarily due to an inability to obtain informed consent. Design of the study as a randomized controlled trial and plans to disseminate study findings beyond BHCS necessitated IRB approval rather than delivery of the supplemental care bundle as a quality improvement (QI) project. Placing QI initiatives under research regulations can lead to project delays, higher costs, and patient frustrations with the process.41, 42 This tension was evident during study screening and enrollment, as many patients who otherwise met criteria and would potentially benefit from the intervention were hesitant to participate in a research study or refused to sign a multipage consent document. The difficulties of enrolling elderly patients in clinical trials have been well‐described.43, 44 Further research involving a minimal‐risk, educational intervention such as this elderly care‐bundle would likely better fit under the category of expedited IRB review with waiver or modification of the informed consent process.45

Incomplete blinding could have potentially affected our results. At the study site, the team members delivering the care bundle were a regular part of the hospital staff (as opposed to external researchers), and it is not unusual for a CC or a pharmacist to enter a patient's room (eg, to confirm a drug allergy history). In view of this, the impact of imperfect blinding on 30‐day outcomes would likely be minimal. Furthermore, a floor staff perception that a specific patient was being taken care of by the study team resulting in a lower than usual level of care, would tend to bias the result of the intervention toward the null effect.

vThe study cohort did not have enough subjects to perform analyses (ie, modeling or examination of subgroups) beyond basic comparative findings. Issues such as preadmission living situation and the presence of depression or cognitive impairment (Mini‐Mental Status Exams were not performed on these patients) may potentially influence postdischarge recidivism; their effects can not be reliably ascertained from these data. Additionally, to prevent study personnel from engaging patients who would soon be going home, it was felt that the benefits from the care bundle would be recognized only if the intervention could be initiated within 72 hours of admission and delivered in full, a requirement that further reduced the enrollment pool. The intent of this pilot work was to guide future investigations surrounding hospital‐to‐home transitional care. The next phase of research in this area will need an enhanced sample size with more extensive baseline data collection so that potential confounding factors or outcomes in specific populations can be explored.

Another problem restricting applicability of study findings was the use of only 3 different CCs and 3 pharmacists on the research team to deliver the components of patient education, discharge planning, and medication counseling in the elderly care bundle. Personnel for the trial were chosen for their experience and interest in the area of care transitions. To distinguish the benefit of the elderly care bundle in general versus the expertise of these particular CCs and study pharmacists, a larger‐scale, multisite trial would be necessary. Lastly, due to resource constraints, patients who resided in long‐term care (either LTAC, SNFs, or nursing homes) prior to admission with anticipated return to those sites were not eligible for the study. Similar to the patients whose comorbidities or acute severity of illness prevented informed consent, this segment of the elderly population may have derived even more benefit from receipt of the elderly care bundle.10, 15, 46 Despite exclusion of this group (which would be expected to lessen the impact of the intervention), a difference in readmission/ED visits rates at 30 days following discharge was observed.

Conclusions

This pilot randomized clinical trial (RCT) evaluated the effects of a supplemental, aggregate care bundle centered on patient education, discharge planning, and medication counseling and reconciliation compared to usual care in a group of elderly patients at high risk of readmission or ED visitation following an index hospitalization. The intervention was designed to be reproducible and make use of existing hospital resources. Probably through facilitation of patient self‐care and home management, the elderly care bundle reduced the composite outcome of readmission/ED visits at 30 days postdischarge. By 60 days, this effect had waned, demonstrating the short‐term benefit of a hospital‐based educational intervention and stressing the need to incorporate additional outpatient transitional care support to sustain favorable outcomes. The study was not powered to detect small differences (which would be more likely than a change of multiple days) in length of index hospital stay related to the care bundle. There were important study limitations (primarily associated with small sample size), and this work should be viewed as hypothesis‐generating. Future trials should assess the impact of a standardized targeted care bundle delivered across multiple healthcare systems on a larger cohort of high‐risk elderly patients, including analysis of financial and personnel allocations relative to the benefits of the intervention.

Elderly patients (aged 65 years and older) consume a disproportionate amount of acute health care resources, composing up to 20% of emergency department (ED) visits,1, 2 having a 2‐fold to 5‐fold increase in likelihood of hospital admission,1 and frequently incurring lengths of hospital stay (LOS) approximately 15% higher than the national averages.3 In addition, they are at increased risk for hospital readmission in the 90‐day interval following hospital discharge.1, 4, 5 Specific risk factors for readmission include age above 80 years, discharge within the previous 30 days, the presence of 3 or more comorbid diagnoses, use of 5 or more prescription medications, difficulty with at least 1 activity of daily living (ADL), and lack of discharge education.6 These risk factors can translate into adverse drug events,79 exacerbations of chronic diseases,10 or functional decline4, 5 that can trigger ED visits or hospital readmission.

Hospital‐based care coordinationdefined as a multidisciplinary interaction between inpatients and providers that focuses on education, communication, and discharge planning with the primary aim of improving outcomeshas demonstrated inconsistent results as a mechanism to reduce LOS, postdischarge ED visits, or hospital readmission rates. While disease‐specific care coordination programs for congestive heart failure and chronic obstructive pulmonary disease have been effective in reducing rehospitalization rates,1015 the benefits of comprehensive care coordination for elderly general medical inpatients with a broader range of diagnoses are less clear. In a group of 750 elderly patients with 1 of 11 common inpatient diagnoses (such as stroke or hip fracture) likely to ultimately require a high level of home support, Coleman et al.16 found that a structured transitional care program centered on a personal coach decreased rehospitalization rates at 30 and 90 days. Preen et al.17 found improved patient involvement and perceived quality of life with care coordination focused on discharge planning, but no impact on LOS. Likewise, a recent meta‐analysis18 failed to demonstrate statistically significant differences in mortality, LOS, or readmission rates in hospitalized patients who received intensive care coordination versus usual care; however, variation in the components of the care coordination intervention and reported outcomes restricted the ability to pool data in this study.

Care coordination programs demonstrating efficacy in reducing health care utilization in elderly medical patients have generally included an outpatient transitional component with out‐of‐hospital postacute care visits by health care personnel such as a nurse, pharmacist, or physician.1923 These offsite interventions generate additional expenses and resource demands that may not be practical for smaller hospitals to implement.24, 25 In contrast, hospital‐based care coordination programs have clear ownership and thus may be more practical to disseminate. Individual elements of hospital‐based care coordination such as pharmacist counseling, discharge education, and telephone follow‐up have been shown to reduce ED visitation and readmission rates in high‐risk elderly patients. Less information is available regarding the impact of these interventions delivered in an aggregate bundle by hospital staff in the absence of bridging transitional visits.2629

The objective of this pilot study was to determine whether a supplemental elderly care bundle, targeted to high‐risk inpatients by hospital staff as an enhancement to existing care coordination, would affect postdischarge readmission and ED visit rates. The intervention was designed to capitalize on existing resources, and focused specifically on elderly inpatients who were hospitalized with diagnoses commonly encountered in a general medical unit and predisposed to recidivism.

Patients and Methods

Delivery of the Supplemental Care Bundle

Starting no later than 24 hours after enrollment and continuing up to 1 week following hospital discharge, intervention group patients received a targeted care bundle provided by 1 of 3 care coordinators (CCs) and 1 of 4 clinical pharmacists (CPs) working with the study team. The care bundle was designed as an intensive patient‐centered educational program that would augment BUMC's existing care coordination processes (delivered to all patients regardless of study participation); specific elements are displayed in Figure 1. Study CCs saw patients daily throughout their hospital stay, and instructed patients on specific health conditions, with an emphasis on optimizing home self‐care and contingency plans if problems arose. CP visits focused on medication reconciliation and education regarding any new agents started during the hospitalization. The personal health record (PHR) provided a tool to engage patients in self‐care, and as discussed by Coleman et al.,7, 16, 33 promoted information transfer from the hospital to outpatient settings. During the postdischarge phone call, CCs followed a basic script to confirm receipt of medical equipment, medications, home health arrangements, and scheduling of follow‐up appointments. They also used this contact as an opportunity to reinforce patient education on managing their conditions. CPs reviewed medication use (type, schedule, dose), and spoke with patients about any symptoms they may have experienced as medication side effects. If indicated based on their phone discussions, both CCs and CPs could recommend an action plan to the patient.

Figure 1

The study CCs and CPs were existing hospital staff and performed their research activities in addition to their usual duties. Study CCs were highly experienced (averaging 8 years of inpatient floor nursing plus 10 years as CCs) and all had advanced nursing certifications (ACM, BSN, or MSN). The CPs were upper‐level pharmacy residents completing their inpatient clinical rotations. Additional training for both study CCs and CPs was limited to a series of 3 meetings (each 45 minutes in duration) regarding the intent and delivery of the supplemental care bundle, including use of study forms.

At the time of the trial, the particular CCs and CPs chosen to deliver the supplemental care bundle had work assignments ensuring that crossover between intervention and usual care groups would not occur. For example, 1 of the study CCs normally covered a surgical floor such that her normal scope of responsibilities would not influence the medical patients in the study (their baseline care coordination was provided by nonstudy personnel). Medication reconciliation and medication education is generally performed by floor nursing staff rather than CPs at BUMC.

Results

The final sample size for this pilot was small, with 41 total patients (21 controls, 20 interventions). The main reason for enrollment failure of patients meeting study criteria was an inability to obtain informed consent. Sixty patients declined participation after being approached, and another 56 patients were unable to give their informed consent due to impairments (poor cognition, medication induced sedation, severity of illness) with lack of an available proxy to give written consent during the 72‐hour postadmission recruitment window. There were no statistically significant differences in the baseline characteristics of the intervention and control groups (Table 2). A similar proportion of patients (23% in the intervention, 15% in controls; P = 0.70) had preexisting diagnoses of dementia or depression. However, on APR‐DRG measures relating to acuity of illness and mortality risk, patients in the intervention group trended toward higher severity (Table 2). Likewise, although it was not a statistically significant difference, 13 of 20 patients in the intervention group were taking medications from 2 drug classes commonly implicated in adverse drug events (warfarin, insulin, diuretics, sedating agents) as part of their discharge medication regimen compared to 10 of 21 patients in the control group.

Study outcomes are displayed in Table 3. Mean LOS is reported as a descriptive finding; there was insufficient power to compare this outcome statistically between groups. The majority of patients were discharged to home. A similar proportion of patients in the intervention (20%) and control groups (22%) who had lived at home immediately prior to admission were discharged from the hospital to skilled care facilities (P = 0.87). The number of readmissions/ED visits (taken as a composite measure of unplanned healthcare utilization) within 30 days of discharge was lower in the intervention group; by 60 days, there was no longer a statistically significant difference in readmission/ED visit rates between groups. For those patients who had a readmission or ED visit following hospital discharge, the intervention group had a longer time interval to first event compared to controls (36.2 versus 15.7 days, P = 0.05). Of the patients discharged to skilled care, 1 in the intervention group (at 53 days) and 1 in the control group (at 16 days) had a readmission/ED visit event. Figure 2 shows time‐to‐first readmission or ED visit event curves at 30 and 60 days for both intervention and control groups. For patients who had a readmission/ED visit event, LOS for this episode was 2.2 2.1 days in controls and 3.7 2.1 days in the intervention group (insufficient power for statistical comparison). The study's small sample size prevented development of a meaningful regression model.

Figure 2

Resource utilization and the specifics of patient‐study personnel interaction associated with the intervention were tracked. Research assistants spent an average of 50 minutes daily screening charts for potential candidates. For the 20 patients who received the supplemental elderly care bundle, study CCs averaged 20 to 25 minutes per patient daily of additional time counseling patients and families, identifying and attending to discharge barriers, filling out documentation, and faxing the supplemental study discharge form to the patient's primary care physician. Any residual home care needs or issues unresolved at discharge were addressed with the patient in the 5 to 7 day follow‐up phone call. Similarly, study CPs expended approximately 20 minutes daily per patient providing medication education, reconciliation, and optimization of drug therapy. Study pharmacists recommended a change to the medication regimens of 10 patients in the intervention group; physicians acted upon these recommendations for 7 of the patients. The changes included dosage adjustment, discontinuation of medications due to possible drug interaction or duplication of drugs with the same pharmacologic effect, and addition of medications as indicated by patient condition or to reconcile with patients' at‐home medication regimens. Patients contacted via phone by the study pharmacist within 1 week after discharge were able to describe proper use of new medications started in the hospital and confirm that they obtained or had the means to obtain the prescribed drugs.

Discussion

This pilot study examined the effects of a supplemental care bundle involving patient education and discharge planning delivered by hospital‐based CCs and CPs on the rate of readmission/ED visitation in 41 elderly (70 years of age) patients. The study was not adequately powered to detect an impact of the intervention on index LOS. The care bundle did lead to significantly fewer readmissions or ED visits 30 days postdischarge and appeared to increase the time interval to first unplanned readmission or ED visit compared to usual care. This effect was no longer present at 60 days postdischarge. Resource allocations and scope of duties for CCs and CPs (an average of 20 minutes per patient per day) related to delivering the intervention were realistic for broader implementation in the hospitalized elderly population at high risk for readmission or ED visitation following discharge.

Length of stay for the initial hospitalization associated with the care bundle was an original outcome of interest to the study team. However, with the final enrollment of 41 patients and a power of 0.8, the between group difference would have needed to be 2.6 days to be statistically significant. It is likely that any change in LOS related to the care bundle would be much smaller, particularly since 2 key determinants of LOS, severity of illness and physician behavior, were beyond this patient education‐oriented intervention's scope of influence.3437 Furthermore, the diverse range of eligible diagnoses limited the study CCs' ability to reduce variability through use of clinical care pathways. One approach in leveraging an elderly care bundle to reduce LOS may be to focus on a specific disease that has well‐established inpatient benchmarks and treatment algorithms. For example, in patients with community‐acquired pneumonia, the use of care coordination in combination with standardized order sets decreased LOS without compromising safety, mainly by shortening the time from clinical stability to discharge.38

On separation of the readmission/ED visit outcome into 30 and 60 day postdischarge time frames, the intervention group had a lower rate of unplanned acute health care use within 30 days postdischarge; the difference between groups had dissipated by 60 days postdischarge. This convergence suggests that a hospital‐based intervention's influence is strongest closer to the time of the initial hospital stay, and wanes as more time has elapsed. Indeed, interventions that have successfully maintained lower readmission rates beyond 60 and 90 days postdischarge in a high‐risk elderly population (such as the program advocated by Coleman et al.16) have included a transitional care provider engaging patients during the hospitalization and performing subsequent visits to the home or nursing facility.33 An optimal intervention would capitalize on the hospital‐based staff's ability to improve short‐term readmission/ED visit rates while linking patients to longer‐term transitional care to extend these outcomes. Electronic health records could potentially facilitate these care transitions, beginning with an automated screening process for identification of high‐risk inpatients and continuing through postdischarge follow‐up. How to develop these resources in settings where outpatient practices are independent or only loosely affiliated with hospitals is an area for continued investigation.

In a group of elderly patients with multiple comorbidities and complex pharmacotherapy regimens, the study bundle component targeting medication management appears to be a high‐yield intervention to reduce unplanned health care utilization following hospital discharge. These patients are more susceptible to nonadherence and drug‐related adverse events, which may contribute to hospital readmission or ED visitation.7, 9, 39 Consistent with findings at other sites,28, 40 a heightened level of CP involvement in the care of high‐risk elderly patients may have helped reduce these undesirable outcomes. Of the 9 readmission/ED visit events in the control group, 3 were attributable to medication related complications (2 from sedatives, 1 from a diuretic). None of the readmission/ED visit events in intervention group patients stemmed from medication effects.

Correspondingly, the research CCs' provision of daily condition‐specific education, additional time to more thoroughly investigate discharge needs, engagement of patients' families as active partners in self‐care, and the use of a structured discharge form along with follow‐up phone calls may have better prepared patients to manage their health problems once released from the hospital.26, 28, 29 For example, 1 patient in the control group was readmitted less than 24 hours after initial discharge due to inability to perform self‐care at home. Given the study power issues described previously, data on LOS for the second hospitalization for patients who had a readmission event are difficult to interpret, but could suggest the occurrence of some shorter, preventable readmissions in the control group. Conversely, the readmission/ED visit events in intervention patients appeared to be associated with a specific medical condition (eg, failure of diabetic cellulitis to respond to appropriate outpatient treatment) rather than problems that would have been corrected with an educational/self‐management program such as this targeted care bundle.

This pilot study had several limitations. The main issue was a small patient sample size that was primarily due to an inability to obtain informed consent. Design of the study as a randomized controlled trial and plans to disseminate study findings beyond BHCS necessitated IRB approval rather than delivery of the supplemental care bundle as a quality improvement (QI) project. Placing QI initiatives under research regulations can lead to project delays, higher costs, and patient frustrations with the process.41, 42 This tension was evident during study screening and enrollment, as many patients who otherwise met criteria and would potentially benefit from the intervention were hesitant to participate in a research study or refused to sign a multipage consent document. The difficulties of enrolling elderly patients in clinical trials have been well‐described.43, 44 Further research involving a minimal‐risk, educational intervention such as this elderly care‐bundle would likely better fit under the category of expedited IRB review with waiver or modification of the informed consent process.45

Incomplete blinding could have potentially affected our results. At the study site, the team members delivering the care bundle were a regular part of the hospital staff (as opposed to external researchers), and it is not unusual for a CC or a pharmacist to enter a patient's room (eg, to confirm a drug allergy history). In view of this, the impact of imperfect blinding on 30‐day outcomes would likely be minimal. Furthermore, a floor staff perception that a specific patient was being taken care of by the study team resulting in a lower than usual level of care, would tend to bias the result of the intervention toward the null effect.

vThe study cohort did not have enough subjects to perform analyses (ie, modeling or examination of subgroups) beyond basic comparative findings. Issues such as preadmission living situation and the presence of depression or cognitive impairment (Mini‐Mental Status Exams were not performed on these patients) may potentially influence postdischarge recidivism; their effects can not be reliably ascertained from these data. Additionally, to prevent study personnel from engaging patients who would soon be going home, it was felt that the benefits from the care bundle would be recognized only if the intervention could be initiated within 72 hours of admission and delivered in full, a requirement that further reduced the enrollment pool. The intent of this pilot work was to guide future investigations surrounding hospital‐to‐home transitional care. The next phase of research in this area will need an enhanced sample size with more extensive baseline data collection so that potential confounding factors or outcomes in specific populations can be explored.

Another problem restricting applicability of study findings was the use of only 3 different CCs and 3 pharmacists on the research team to deliver the components of patient education, discharge planning, and medication counseling in the elderly care bundle. Personnel for the trial were chosen for their experience and interest in the area of care transitions. To distinguish the benefit of the elderly care bundle in general versus the expertise of these particular CCs and study pharmacists, a larger‐scale, multisite trial would be necessary. Lastly, due to resource constraints, patients who resided in long‐term care (either LTAC, SNFs, or nursing homes) prior to admission with anticipated return to those sites were not eligible for the study. Similar to the patients whose comorbidities or acute severity of illness prevented informed consent, this segment of the elderly population may have derived even more benefit from receipt of the elderly care bundle.10, 15, 46 Despite exclusion of this group (which would be expected to lessen the impact of the intervention), a difference in readmission/ED visits rates at 30 days following discharge was observed.

Conclusions

This pilot randomized clinical trial (RCT) evaluated the effects of a supplemental, aggregate care bundle centered on patient education, discharge planning, and medication counseling and reconciliation compared to usual care in a group of elderly patients at high risk of readmission or ED visitation following an index hospitalization. The intervention was designed to be reproducible and make use of existing hospital resources. Probably through facilitation of patient self‐care and home management, the elderly care bundle reduced the composite outcome of readmission/ED visits at 30 days postdischarge. By 60 days, this effect had waned, demonstrating the short‐term benefit of a hospital‐based educational intervention and stressing the need to incorporate additional outpatient transitional care support to sustain favorable outcomes. The study was not powered to detect small differences (which would be more likely than a change of multiple days) in length of index hospital stay related to the care bundle. There were important study limitations (primarily associated with small sample size), and this work should be viewed as hypothesis‐generating. Future trials should assess the impact of a standardized targeted care bundle delivered across multiple healthcare systems on a larger cohort of high‐risk elderly patients, including analysis of financial and personnel allocations relative to the benefits of the intervention.

In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.

How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.

Patients and Methods

Results

Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 _AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.

The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 _PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.

Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.

The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).

For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).

Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).

Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.

Figure 1

Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.

Figure 2

The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.

We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.

Conclusions

We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.

In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.

The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.

Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538

Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.

We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.

Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.

In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.

How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.

Patients and Methods

Results

Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 _AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.

The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 _PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.

Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.

The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).

For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).

Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).

Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.

Figure 1

Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.

Figure 2

The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.

We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.

Conclusions

We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.

In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.

The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.

Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538

Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.

We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.

Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.

In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.

How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.

Patients and Methods

Results

Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 _AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.

The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 _PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.

Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.

The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).

For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).

Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).

Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.

Figure 1

Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.

Figure 2

The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.

We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.

Conclusions

We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.

In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.

The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.

Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538

Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.

We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.

Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.

A 52‐year‐old woman presented with a 3‐month history of progressive bilateral leg edema and dyspnea while climbing a flight of stairs or while walking up a steep slope. She also complained of a tingling sensation in both hands and fingers, which started about 2 months prior to the onset of edema. She did not describe sensory problems in the lower extremities and did not have any other neurological complaints. She denied fever, cough, chest pain, palpitations, orthopnea, paroxysmal nocturnal dyspnea, and dark stools. She had no history of hypertension, diabetes, dyslipidemia, or asthma and had never been hospitalized. She did not smoke or consume alcohol and used no medications, including over‐the‐counter drugs or dietary supplements. The patient was born in Japan and had not traveled outside the country since her birth. She was a homemaker and had worked occasionally as a manual laborer in sugar cane agriculture. A review of systems revealed no history of polydipsia, polyuria, or cold or heat intolerance but did identify new hair growth, especially on the extremities.

This middle‐aged woman shows progressive changes in her general health status that are characterized by edema and dyspnea on effort. The differential diagnosis of edema includes a broad spectrum of illnesses, such as cardiac, lung, renal, endocrine, and hepatic diseases. Because of the life‐threatening potential, my first concern is cardiac disease, although the patient is not experiencing typical symptoms of ischemic heart disease or congestive failure. Bilateral and distal distribution of neuropathic symptoms is likely due to diseases of peripheral nerves rather than those of the central nervous system. Her complaint of a bilateral tingling sensation in the hands may suggest carpal tunnel syndrome as a result of her long‐term agricultural work. Other possible causes include radiculopathy of the cervical spine or polyneuropathy. Clues in the physical examination may help narrow the differential diagnosis to a cardiac, hepatic, or endocrine disorder.

The patient appeared ill. Her weight had increased from 48 to 61 kg since she was last weighed 6 months previously. Her blood pressure was 140/78 mm Hg, her heart rate was 72 beats/minute with a regular rhythm, her respiratory rate was 18/minute, and her temperature was 37.5C. The jugular venous pressure was elevated at 10 cm above the sternal angle. A grade III/VI systolic ejection murmur was evident at the second interspace along the left sternal border. The second heart sound was fixed and split. There were decreased breath sounds and complete dullness to percussion over both lower lung fields. Shifting dullness was noted on abdominal examination. There was pitting edema from the feet to the thighs, with slow pit‐recovery time in both legs, and she exhibited generalized hirsutism on the face, body, and extremities. There was no lymphadenopathy. On neurological examination, her mental status was normal. The cranial nerves were normal, as was coordination. There was mild generalized distal‐dominant motor weakness with generalized hyporeflexia. Sensory testing demonstrated glove‐and‐stocking type loss of sensation to pinpricks as well as dysesthesia in all extremities. Phalen and Tinel tests were negative.

The elevated venous pressure and pitting edema with slow recovery suggest high venous pressure edema rather than hypoproteinemic edema. Complete bilateral dullness of the chest and shifting dullness of the abdomen indicate the presence of bilateral pleural effusion and ascites. Edema from high venous pressure is usually caused by right, left, or biventricular cardiac failure. A fixed splitting of the second heart sound suggests an atrial septal defect, which is a rare cause of progressive right heart failure in adults. I recommend checking the patient's thyroid function to investigate the possibility of hypothyroidism, which is a common illness among middle‐aged women and could contribute to her edema as well as hirsutism. The neurological findings suggest a generalized polyneuropathy. The unusual combination of high venous pressure edema and polyneuropathy may indicate a rare multisystem disorder such as amyloidosis. Alternatively, the patient might have developed multiple diseases during the same time period. For instance, diabetic polyneuropathy is the most common cause of polyneuropathy among the middle‐aged. Finally, the differential diagnosis of hirsutism includes ovarian, adrenal, or pituitary sources of hyperandrogenism in addition to hypothyroidism. I would first evaluate for diabetes, thyroid disease, and cardiac disease and would like to see the results of laboratory tests for thyrotropin and plasma glucose as well as chest radiography and electrocardiography.

The white‐cell count was 5400/mm³ with a normal differential. Hemoglobin was 10.7 g/dL with normal red‐cell indices, and the platelet count was 276,000/mm³. The erythrocyte sedimentation rate was 29 mm/hour. Other laboratory tests revealed the following values: total protein, 6.2 g/dL; albumin, 3.3 g/dL; blood urea nitrogen, 12 mg/dL; creatinine, 0.7 mg/dL; aspartate aminotransferase, 6 U/L; alanine aminotransferase, 2 U/L; lactate dehydrogenase, 96 U/L; alkaline phosphatase, 115 U/L; creatine phosphokinase, 60 U/L; total bilirubin, 0.9 mg/dL; glucose, 96 mg/dL; hemoglobin A1c, 4.6%; total cholesterol, 111 mg/dL; and thyrotropin, 6.32 mIU/mL (normal range, 0.50‐5.00 mIU/mL). Serum free thyroxine, triiodothyronine, and urine testosterone were normal. Serum dehydroepiandrosterone sulfate was mildly elevated for her age (864 ng/mL: normal range, 180‐750 ng/mL). Serological studies for human immunodeficiency virus, human T‐lymphotrophic virus type 1, and syphilis were negative. Urinalysis was weakly positive for protein but negative for casts and occult blood. The stool was negative for occult blood.

A chest radiograph showed bilateral pleural effusions. Computed tomography demonstrated bilateral pleural effusions, ascites, mild hepatomegaly, and small, multiple, mediastinal lymph nodes. Her electrocardiogram was normal. A transesophageal echocardiogram with agitated saline contrast demonstrated normal ventricular systolic and diastolic function and no atrial septal defect. The inferior vena cava did not collapse with inspiration, and there was no evidence of infiltrative cardiomyopathy.

These laboratory results rule out diabetes as the cause of the polyneuropathy. The subclinical hypothyroidism would not explain profound edema and hirsutism. A serum albumin level of 3.3 g/dL confirms high venous pressure edema rather than hypoproteinemic edema. Normochromic, normocytic anemia and a mildly elevated sedimentation rate point to a chronic illness or inflammatory state. The mediastinal lymphadenopathy may reflect congestion as a result of the high venous pressure or reflect a systemic disease involving lymph nodes. Normal ventricular function with high venous pressure is suggestive of heart failure from diastolic dysfunction, although the patient does not have risk factors for diastolic dysfunction, such as hypertension, and has no other echocardiographic features of diastolic impairment. The combination of hyperandrogenism and neuropathy points to a systemic process, such as a paraneoplastic syndrome. I would next investigate the source of the excess androgens.

Because serum dehydroepiandrosterone sulfate was mildly elevated, I‐131 aldosterol scintigraphy was performed, and it was negative. Electromyography showed a pattern of generalized sensorimotor polyneuropathy.

At this point, it appears that cardiac, endocrine, hepatic, and renal diseases have been largely ruled out as a cause of her symptoms. Reframing and unifying the important clinical problems for this patient may be useful in resolving this diagnostic puzzle. They include (1) systemic high venous pressure edema; (2) generalized sensorimotor polyneuropathy; (3) hirsutism; (4) normocytic, normochromic anemia; (5) an elevated erythrocyte sedimentation rate; (6) mediastinal lymphadenopathy; and (7) subclinical hypothyroidism. At this point, I cannot unify these pieces of information into a single diagnosis. I would search the medical literature, focusing on these terms.

A general internist consultant performed MEDLINE and Google Scholar searches using the key words edema, polyneuropathy, and hirsutism. This search suggested the diagnosis of Crow‐Fukase syndrome, also known as POEMS (polyneuropathy, organomegaly, endocrinopathy, M protein, and skin changes) syndrome. Subsequent evaluations were performed. First, serum protein electrophoresis revealed the presence of monoclonal proteins, although hypergammaglobulinemia was not present. Second, a bone marrow examination demonstrated increased abnormal plasma cell proliferation (7%), although a radiographic skeletal survey found no lesions suggestive of plasmacytoma. Third, cerebrospinal fluid analysis showed normal cell counts but increased protein concentration (202 mg/dL). Fourth, a blood sample referred to an outside laboratory demonstrated elevated levels of vascular endothelial growth factor (3902 pg/mL: normal range, 150‐500 pg/mL). On the basis of these findings, the diagnosis of POEMS syndrome was made. After oral prednisolone (40 mg/day) was initiated, the systemic edema improved gradually, and she did well during the 2‐year follow‐up period.

Commentary

POEMS syndrome, also known as Crow‐Fukase syndrome, is a rare multisystem disorder first described by Crow in 1956.1, 2 It is characterized by polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes, as indicated by the acronym. The diagnosis of POEMS syndrome is difficult as this syndrome is rare and requires high clinical suspicion. According to a nationwide cross‐sectional survey in Japan, the prevalence of POEMS syndrome is very low (about 3 patients per 1,000,000 persons),3 and its prevalence in Western countries is considered even lower than that in Japan. The average age at onset is around 45 to 50 years old, and men are twice as likely to have this syndrome as women.46 Table 1 shows the diagnostic criteria of POEMS syndrome, based on research by Dispenzieri and others at the Mayo Clinic, and Table 2 presents the relative frequency of these clinical features.6, 7 The initial symptomatology generally includes polyneuropathy, skin changes, and generalized edema, which are nonspecific symptoms, as are other well‐recognized associated conditions such as clubbing, weight loss, thrombocytosis, polycythemia, and hyperhidrosis. Thus, it is important to consider this syndrome when one is facing an undiagnosed illness involving multiple organ systems and to distinguish it from other conditions such as multiple myeloma, amyloidosis, and monoclonal gammopathy of undetermined significance. Vascular endothelial growth factor is thought to be involved in the edema of POEMS syndrome, as massive release from aggregated platelets increases vascular permeability and venous pressure.710

Data regarding treatment and survival are largely observational. Overall mean survival from diagnosis in the 2003 Dispenzieri cohort was 13.7 years, with death often due to infection or cardiorespiratory failure.6 When a solitary plasmacytoma or osteosclerotic myeloma is present, radiation to the lesion can often lead to clinical remission. Other treatment options include alkylating agents and/or high‐dose chemotherapy with peripheral stem‐cell transplantation, corticosteroids, and supportive care.7

Clinicians frequently use the internet to aid in the clinical decision process. In a survey of the Royal New Zealand College of General Practitioners,11 half reported that they used the Internet to search for clinical information. Two well‐known resources are MEDLINE, which contains over 11 million references dating back to the 1960s, and internet search engines such as Google (and a more recent product, Google Scholar, which attempts to sort search results by including factors such as the author, the publication in which the article appears, and how often the article has been cited).

MEDLINE searches a well‐defined set of journals and uses the Medical Subject Headings (MeSH) vocabulary, which consists of sets of descriptive terms organized in a hierarchical structure to allow searching with various levels of specificity. For instance, entering the term heart attack will map to the MeSH term myocardial infarction and will also include more specific terms such as myocardial stunning and cardiogenic shock.

Google, in comparison, explores resources beyond journals without any clear boundary to its scope, and its advanced search functions can be occasionally unreliable. For instance, search results are occasionally marred by outdated citation information and may include materials that are not truly scholarly. However, search engines can search through the actual text of manuscripts and access the gray literature, which includes open‐source material that is usually original but not widely distributed or often easily available, such as technical reports and dissertations. A direct study comparing the results of searches in PubMed (one of the MEDLINE search engines) and Google Scholar is difficult, but the critical characteristics of each can be compared and contrasted (Table 3).

Internet searches may also suggest diagnoses from a compilation of clinical features, such as in this case. To be successful, such a search must complement the cognitive process; a search engine cannot completely replace clinical judgment. Clinicians must be able to identify salient clinical features and generate high‐yield search terms and then exercise skill in sifting through the citations to arrive at the appropriate diagnosis. A recent study found that Google searches revealed the correct diagnosis in 58% of the case records of the New England Journal of Medicine,12 although each search query resulted in many results, which then had to be manually reviewed for appropriateness within the case's context.

Like a traditional diagnostic test, a search can be described by sensitivity, specificity, and the number of articles needed to read.13 For example, in a study comparing the performance of search strategies to identify clinical practice guidelines in Google Scholar and SUMSearch (another freely accessible search engine), using the term guideline yielded the highest sensitivity, and using the term practice guideline generated the highest specificity and the lowest number of articles needed to read (Table 4).14

Although there are several other popular hosts of web‐based search engines, a more robust decision‐support program may help physicians more efficiently consider relevant diagnoses. One program, named Isabel, has been developed through the indexing of a database of more than 11,000 diseases according to word patterns in journal articles associated with each disease, and it is updated as new and relevant articles emerge. One recent study demonstrated that the correct diagnosis was made in 48 of 50 cases (96%) with specific, key findings as search terms but in only 37 of the same 50 cases (74%) if the entire case history was simply pasted in, again emphasizing the importance of specific search terms.15

POEMS syndrome is a rare entity occasionally seen in middle‐aged individuals and marked by a multitude of nonspecific findings, particularly polyneuropathy and plasma cell dyscrasia. In this case, the diagnostic test was an internet search based on the most prominent clinical symptoms. Such a strategy can provide a powerful addition to traditional literature and MEDLINE resources. However, the efficiency of this process is heavily dependent on the quality of the search strategy and, therefore, the cognitive faculties of the treating physician to avoid the predictable shortcoming of low specificity. Garbage in, garbage out still applies whether the computer in question is the human mind or the desktop PC.