Predicting High-risk and High-cost Patients for Proactive Intervention. Issue 8 (30th August 2022)

• Record Type: Journal Article
• Authors: Gao, Jian; Moran, Eileen; Higgins, Donald S.; Mecher, Carter
• Is Part Of: Medical care. Volume 60:Issue 8(2022)
• Page Start: 610
• Page End: 615
• Publication Date: 2022-08-30
• Subjects: predictive modeling -- patient risk -- timely intervention -- cost -- outcomes; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000001737
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Background: It is well known that 20% of the patients incur 80% of health care costs and many diseases and complications can be prevented or ameliorated with prompt intervention. One of the well-recognized strategies for cost reduction and better outcomes is to predict or identify high-risk and high-cost (HRHC) patients for proactive intervention. Objective: The objective of this study was to develop a predictive model that can be used to identify HRHC patients more accurately for proactive intervention. Methods: This is an observational study using fiscal year (FY) 2018 administrative data to predict FY 2019 total cost at the patient level. All 5, 676, 248 patients who received care in both FYs 2018 and 2019 from the Veterans Health Administration were included in the analyses. The Veterans Health Administration Corporate Data Warehouse was our main data source. With split-sample analyses, 3 sets of patient comorbidities and 5 statistical models were assessed for the highest predictive power. Results: The Box-Cox regression using comorbidities designated by the expanded CCSR (Clinical Classifications Software Refined) groups as predictors yielded the highest predictive power. The R 2 reached 0.51 and 0.37 for the transformed and raw scale cost, respectively. Conclusions: The predictive model developed in this study exhibits substantially higher predictive power than what has been reported in the literature. The algorithm based on administrative data and a publicly available patient classification system can be readily implemented by other value-based health systems to identify HRHC patients for proactive intervention.