Assessing the Impact of Body Mass Index Information on the Performance of Risk Adjustment Models in Predicting Health Care Costs and Utilization. Issue 12 (December 2018)

• Record Type: Journal Article
• Authors: Kharrazi, Hadi; Chang, Hsien-Yen; Heins, Sara E.; Weiner, Jonathan P.; Gudzune, Kimberly A.
• Is Part Of: Medical care. Volume 56:Issue 12(2018)
• Publication Date: 2018-12
• Subjects: body mass index (BMI) -- risk stratification -- predictive modeling -- electronic health records -- administrative claims -- the Johns Hopkins ACG system; 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.0000000000001001
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Background: Using electronic health records (EHRs) for population risk stratification has gained attention in recent years. Compared with insurance claims, EHRs offer novel data types (eg, vital signs) that can potentially improve population-based predictive models of cost and utilization. Objective: To evaluate whether EHR-extracted body mass index (BMI) improves the performance of diagnosis-based models to predict concurrent and prospective health care costs and utilization. Methods: We used claims and EHR data over a 2-year period from a cohort of continuously insured patients (aged 20–64 y) within an integrated health system. We examined the addition of BMI to 3 diagnosis-based models of increasing comprehensiveness (ie, demographics, Charlson, and Dx-PM model of the Adjusted Clinical Group system) to predict concurrent and prospective costs and utilization, and compared the performance of models with and without BMI. Results: The study population included 59, 849 patients, 57% female, with BMI class I, II, and III comprising 19%, 9%, and 6% of the population. Among demographic models, R 2 improvement from adding BMI ranged from 61% (ie, R 2 increased from 0.56 to 0.90) for prospective pharmacy cost to 29% (1.24–1.60) for concurrent medical cost. Adding BMI to demographic models improved the prediction of all binary service-linked outcomes (ie, hospitalization, emergency department admission, and being in top 5% total costs) with area under the curve increasing from 2% (0.602–0.617) to 7% (0.516–0.554). Adding BMI to Charlson models only improved total and medical cost predictions prospectively (13% and 15%; 4.23–4.79 and 3.30–3.79), and also improved predicting all prospective outcomes with area under the curve increasing from 3% (0.649–0.668) to 4% (0.639–0.665; and, 0.556–0.576). No improvements in prediction were seen in the most comprehensive model (ie, Dx-PM). Discussion: EHR-extracted BMI levels can be used to enhance predictive models of utilization especially if comprehensive diagnostic data are missing. Abstract : Supplemental Digital Content is available in the text.