Complex Patients Have More Emergency Visits: Don't Punish the Systems That Serve Them. Issue 4 (April 2021)

• Record Type: Journal Article
• Authors: Mick, Eric O.; Alcusky, Matthew J.; Li, Nien-Chen; Eanet, Frances E.; Allison, Jeroan J.; Kiefe, Catarina I.; Ash, Arlene S.
• Is Part Of: Medical care. Volume 59:Issue 4(2021)
• Publication Date: 2021-04
• Subjects: emergency department utilization -- behavioral health -- risk adjustment -- social determinants of health; 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.0000000000001515
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Importance: Better patient management can reduce emergency department (ED) use. Performance measures should reward plans for reducing utilization by predictably high-use patients, rather than rewarding plans that shun them. Objective: The objective of this study was to develop a quality measure for ED use for people diagnosed with serious mental illness or substance use disorder, accounting for both medical and social determinants of health (SDH) risks. Design: Regression modeling to predict ED use rates using diagnosis-based and SDH-augmented models, to compare accuracy overall and for vulnerable populations. Setting: MassHealth, Massachusetts' Medicaid and Children's Health Insurance Program. Participants: MassHealth members ages 18–64, continuously enrolled for the calendar year 2016, with a diagnosis of serious mental illness or substance use disorder. Exposures: Diagnosis-based model predictors are diagnoses from medical encounters, age, and sex. Additional SDH predictors describe housing problems, behavioral health issues, disability, and neighborhood-level stress. Main Outcome and Measures: We predicted ED use rates: (1) using age/sex and distinguishing between single or dual diagnoses; (2) adding summarized medical risk (DxCG); and (3) further adding social risk (SDH). Results: Among 144, 981 study subjects, 57% were women, 25% dually diagnosed, 67% White/non-Hispanic, 18% unstably housed, and 37% disabled. Utilization was higher by 77% for those dually diagnosed, 50% for members with housing problems, and 18% for members living in the highest-stress neighborhoods. SDH modeling predicted best for these high-use populations and was most accurate for plans with complex patients. Conclusion: To set appropriate benchmarks for comparing health plans, quality measures for ED visits should be adjusted for both medical and social risks. Abstract : Supplemental Digital Content is available in the text.