Improving Prediction of Long-Term Care Utilization Through Patient-Reported Measures: Cross-Sectional Analysis of High-Need U.S. Veterans Affairs Patients.
Josephine C JacobsMatthew L MaciejewskiTodd H WagnerCourtney Harold Van HoutvenJeanie LoLiberty GreeneDonna M ZulmanPublished in: Medical care research and review : MCRR (2021)
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.
Keyphrases
- patient reported
- social support
- electronic health record
- cross sectional
- long term care
- depressive symptoms
- healthcare
- mental health
- newly diagnosed
- end stage renal disease
- health information
- risk assessment
- machine learning
- clinical decision support
- peritoneal dialysis
- climate change
- health insurance
- social media
- human health
- data analysis