# Improving Hospital Efficiency: Predicting Post-Acute Care Facility Placement Using Machine Learning and Patient Mobility Scores from the Electronic Medical Record > **NIH AHRQ R03** · JOHNS HOPKINS UNIVERSITY · 2020 · $99,860 ## Abstract Project Summary / Abstract 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. ## Key facts - **NIH application ID:** 10056338 - **Project number:** 1R03HS027011-01A1 - **Recipient organization:** JOHNS HOPKINS UNIVERSITY - **Principal Investigator:** Elizabeth Colantuoni - **Activity code:** R03 (R01, R21, SBIR, etc.) - **Funding institute:** AHRQ - **Fiscal year:** 2020 - **Award amount:** $99,860 - **Award type:** 1 - **Project period:** 2020-09-30 → 2022-09-29 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10056338 ## Citation > US National Institutes of Health, RePORTER application 10056338, Improving Hospital Efficiency: Predicting Post-Acute Care Facility Placement Using Machine Learning and Patient Mobility Scores from the Electronic Medical Record (1R03HS027011-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10056338. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*