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

Med Care Res Rev. 2022 Oct;79(5):676-686. doi: 10.1177/10775587211062403. Epub 2021 Dec 14.


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.

Keywords: administrative data; aging; long-term care; survey research; utilization.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Activities of Daily Living
  • Cross-Sectional Studies
  • Humans
  • Long-Term Care*
  • Patient Reported Outcome Measures
  • United States
  • United States Department of Veterans Affairs
  • Veterans*