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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Jacobs, JC; Maciejewski, ML; Wagner, TH; Van Houtven, CH; Lo, J; Greene, L; Zulman, DM

Published Date

  • October 2022

Published In

Volume / Issue

  • 79 / 5

Start / End Page

  • 676 - 686

PubMed ID

  • 34906010

Electronic International Standard Serial Number (EISSN)

  • 1552-6801

Digital Object Identifier (DOI)

  • 10.1177/10775587211062403


  • eng

Conference Location

  • United States