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

Jacobs JC, Maciejewski ML, Wagner TH, Van Houtven CH, Lo J, Greene L, Zulman DM. Improving Prediction of Long-Term Care Utilization Through Patient-Reported Measures: Cross-Sectional Analysis of High-Need U.S. Veterans Affairs Patients. Medical care research and review : MCRR. 2022 Oct 1; 79(5):676-686.

Related HSR&D Project(s)

• IIR 15-379 – Quality of Care and Patient Experience in GeriPACT: A Comparative Effectiveness Study
• IIR 19-106 – Outpatient Palliative Care and Prescribing Safety and Quality at End-of-Life
• PPO 20-342 – The Impact of the COVID-19 Pandemic on Veteran Caregivers: A Mixed Methods Study to inform the VA Caregiver Support System
• SDR 16-194 – Evaluation of Veteran Directed Home and Community Based Services

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information vaww.hsrd.research.va.gov/dimensions/

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.    Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions

Search for Abstract from PubMed

---

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.