Reducing Health Care Costs Through Patient Targeting: Risk Adjustment Modeling to Predict Patients Remaining High Cost. Issue 2 (20th April 2017)

• Record Type: Journal Article
• Authors: Wrathall, Jonathan; Belnap, Tom
• Is Part Of: EGEMS. Volume 5:Issue 2(2017)
• Publication Date: 2017-04-20
• Subjects: Value/Cost -- Care Coordination -- Population Health; Medical records -- Data processing -- Periodicals; Medical care -- Data processing -- Periodicals; Medical Records; Automatic Data Processing; Medical care -- Data processing; Medical records -- Data processing; Periodicals; Periodicals; 651.504261
• DOI: 10.13063/2327-9214.1279
• Languages: English
• ISSNs: 2327-9214

Abstract

Context: The transition to population health management has changed the healthcare landscape to identify high risk, high cost patients. Various measures of patient risk have attempted to identify likely candidates for care management programs. Pre-screening patients for outreach has often required several years of data. Intermountain Healthcare relied on cost-ranking algorithms which had limited predictive ability. A new risk-adjusted algorithm shows improvements in predicting patients' future cost status to facilitate identifying patient eligibility for care management. Case Description: A retrospective cohort study design was used to evaluate high-cost patient status for two of the next three years. Modeling was developed using logistic regression and tested against other decision tree methods. Key variables included those readily available in electronic health records supplemented by additional clinical data and estimates of socio-economic status. Findings: The risk-adjusted modeling correctly identified 79.0% of patients ranking among the top 15% of costs in one of the next three years. In addition, it correctly estimated 48.1% of the patients in the top 15% cost group in two of the next three years. This method identified patients with higher medical costs and more comorbid conditions than previous cost-ranking methods. Major Themes: This approach improves the predictive accuracy of identifying high cost patients in the future and increases the sensitivity of identifying at-risk patients. It also shortened data requirements to identify eligibility criteria for case management interventions. Conclusion: Risk-adjustment modeling may improve management programs' interface with patients thus decreasing costs. This method may be generalized to other healthcare settings.