Improving Prediction of Long-Term Care Utilization Through Patient-Reported Measures: Cross-Sectional Analysis of High-Need U.S. Veterans Affairs Patients. (October 2022)

• Record Type: Journal Article
• Authors: Jacobs, Josephine C.; Maciejewski, Matthew L.; Wagner, Todd H.; Van Houtven, Courtney H.; Lo, Jeanie; Greene, Liberty; Zulman, Donna M.
• Is Part Of: Medical care research and review. Volume 79:Number 5(2022)
• Page Start: 676
• Page End: 686
• Publication Date: 2022-10
• Subjects: long-term care -- utilization -- administrative data -- survey research -- aging; Medical care -- United States -- Periodicals; Medical care -- Periodicals; Medical care -- Research -- United States -- Periodicals; Medical care -- Research -- Periodicals; Medical economics -- United States -- Periodicals; Medical economics -- Periodicals; 362.10973
• DOI: 10.1177/10775587211062403
• Languages: English
• ISSNs: 1077-5587

Abstract

This article examines the relative merit of augmenting an electronic health record (EHR)-derived predictive model of institutional long-term care (LTC) use with patient-reported measures not commonly found in EHRs. We used survey and administrative data from 3, 478 high-risk Veterans aged ≥65 in the U.S. Department of Veterans Affairs, comparing a model based on a Veterans Health Administration (VA) geriatrics dashboard, a model with additional EHR-derived variables, and a model that added survey-based measures (i.e., activities of daily living [ADL] limitations, social support, and finances). Model performance was assessed via Akaike information criteria, C-statistics, sensitivity, and specificity. Age, a dementia diagnosis, Nosos risk score, social support, and ADL limitations were consistent predictors of institutional LTC use. Survey-based variables significantly improved model performance. Although demographic and clinical characteristics found in many EHRs are predictive of institutional LTC, patient-reported function and partnership status improve identification of patients who may benefit from home- and community-based services.