Annually, approximately 8 million people are discharged from an acute care hospital to a post-acute care facility, accounting for >20% of all hospital discharges and >40% of all Medicare discharges. Post-acute care facilities frequently provide rehabilitation services for patients experiencing functional limitations after acute illness who cannot return home safely. Clinicians in acute care hospitals often fail to recognize hospital- acquired functional limitations until after resolution of acute medical/surgical issues. This failure delays hospital discharge and the start of rehabilitation in a post-acute care facility, which can exacerbate hospital-associated functional limitations. Patients' mobility status, one component of physical function, is an important factor in determining the requirement for a post-acute care facility. Simple, validated tools for routinely evaluating patient mobility are increasingly common in acute hospitals but are not routinely used to predict the need for discharge to a post-acute care facility. One such tool, the Activity Measure for Post-Acute Care Inpatient Mobility Short Form (AM-PAC IMSF), is a validated and reliable mobility measure for patients in acute care hospitals. The AM-PAC IMSF is used, as part of routine clinical care throughout hospitalization, for all patients in our acute care hospital. In a pilot study, we demonstrated that lower AM-PAC IMSF scores at hospital admission were strongly associated with post-acute care facility placement. Our goal is to expand upon our preliminary work to develop a formal model to predict which patients are likely to require post- acute care facility placement. Such prediction would be invaluable for improving the discharge planning process and expediting receipt of rehabilitation services at a post-acute care facility. Our overall objective is to demonstrate that prediction models, leveraging `big data' from electronic medical records, can help optimize the hospital discharge process. Thus, we propose the following Aims: 1) To determine if baseline patient mobility status, measured by the AM-PAC IMSF within 48 hours of hospital admission, is predictive of hospital discharge to specific levels of post-acute care; and 2) To develop a dynamic prediction model, using both the hospital admission AM-PAC IMSF score and the subsequent trajectory of daily scores after hospital admission, to predict hospital discharge to specific levels of post-acute care. This proposed research addresses the AHRQ priority of improved efficiency and quality of healthcare delivery via improving the hospital discharge process, with associated improvement in patient outcomes.

Public Health Relevance

We propose to use a validated and reliable mobility score, the Activity Measure for Post-Acute Care Inpatient Mobility Short Form (AM-PAC IMSF), collected as part of routine clinical care, to develop static and dynamic prediction models, using classification trees (a machine learning method), that will help with early identification of patients needing discharge to a post-acute care facility. Earlier identification of patients likely needing post- acute care facility services during acute care hospitalization can reduce costly delays in discharge which, in turn, exacerbate patients' hospital-associated functional losses and disability. Leveraging `big data' from electronic medical records and the data-driven prediction models via this proposal can fundamentally improve current acute care hospital discharge coordination and planning, along with improving patients' functional outcomes.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Small Research Grants (R03)
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Healthcare Systems and Values Research (HSVR)
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Sandmeyer, Brent
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Johns Hopkins University
Physical Medicine & Rehab
Schools of Medicine
United States
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