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

Publication ,  Journal Article
Jacobs, JC; Maciejewski, ML; Wagner, TH; Van Houtven, CH; Lo, J; Greene, L; Zulman, DM
Published in: Med Care Res Rev
October 2022

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.

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Published In

Med Care Res Rev

DOI

EISSN

1552-6801

Publication Date

October 2022

Volume

79

Issue

5

Start / End Page

676 / 686

Location

United States

Related Subject Headings

  • Veterans
  • United States Department of Veterans Affairs
  • United States
  • Patient Reported Outcome Measures
  • Long-Term Care
  • Humans
  • Health Policy & Services
  • Cross-Sectional Studies
  • Activities of Daily Living
  • 4203 Health services and systems
 

Citation

APA
Chicago
ICMJE
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Jacobs, J. C., Maciejewski, M. L., Wagner, T. H., Van Houtven, C. H., Lo, J., Greene, L., & Zulman, D. M. (2022). 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, 79(5), 676–686. https://doi.org/10.1177/10775587211062403
Jacobs, Josephine C., Matthew L. Maciejewski, Todd H. Wagner, Courtney H. Van Houtven, Jeanie Lo, Liberty Greene, and Donna M. Zulman. “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 79, no. 5 (October 2022): 676–86. https://doi.org/10.1177/10775587211062403.
Jacobs JC, Maciejewski ML, Wagner TH, Van Houtven CH, Lo J, Greene L, et al. 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):676–86.
Jacobs, Josephine C., et al. “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, vol. 79, no. 5, Oct. 2022, pp. 676–86. Pubmed, doi:10.1177/10775587211062403.
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. Med Care Res Rev. 2022 Oct;79(5):676–686.
Journal cover image

Published In

Med Care Res Rev

DOI

EISSN

1552-6801

Publication Date

October 2022

Volume

79

Issue

5

Start / End Page

676 / 686

Location

United States

Related Subject Headings

  • Veterans
  • United States Department of Veterans Affairs
  • United States
  • Patient Reported Outcome Measures
  • Long-Term Care
  • Humans
  • Health Policy & Services
  • Cross-Sectional Studies
  • Activities of Daily Living
  • 4203 Health services and systems