Measuring Inconsistency in Quality Across Patient Groups to Target Quality Improvement. Issue 6 (22nd June 2022)

• Record Type: Journal Article
• Authors: Agniel, Denis; Martino, Steven C.; Burkhart, Q.; Hudson Scholle, Sarah; Quigley, Denise D.; Hambarsoomian, Katrin; Orr, Nate; Maksut, Jessica L.; Darabidian, Biayna; Elliott, Marc N.
• Is Part Of: Medical care. Volume 60:Issue 6(2022)
• Page Start: 453
• Page End: 461
• Publication Date: 2022-06-22
• Subjects: quality improvement -- performance inconsistency -- methodology -- social risk; 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.0000000000001712
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Background: Quality improvement (QI) may be aimed at improving care for all patients, or it may be targeted at only certain patient groups. Health care providers have little guidance when determining when targeted QI may be preferred. Objectives: The aim was to develop a method for quantifying performance inconsistency and guidelines for when inconsistency indicates targeted QI, which we apply to the performance of health plans for different patient groups. Research Design and Measures: Retrospective analysis of 7 Health Care Effectiveness Data and Information Set (HEDIS) measures of clinical care quality. Subjects: All Medicare Advantage (MA) beneficiaries eligible for any of 7 HEDIS measures 2015–2018. Results: MA plans with higher overall performance tended to be less inconsistent in their performance ( r =−0.2) across groups defined by race-and-ethnicity and low-income status (ie, dual eligibility for Medicaid or receipt of Low-Income Subsidy). Plan characteristics were usually associated with only small differences in inconsistency. The characteristics associated with differences in consistency [eg, size, Health Maintenance Organization (HMO) status] were also associated with differences in overall performance. We identified 9 (of 363) plans that had large inconsistency in performance across groups (>0.8 SD) and investigated the reasons for inconsistency for 2 example plans. Conclusions: This newly developed inconsistency metric may help those designing and evaluating QI efforts to appropriately determine when targeted QI is preferred. It can be used in settings where performance varies across groups, which can be defined by patient characteristics, geographic areas, hospital wards, etc. Effectively targeting QI efforts is essential in today's resource-constrained health care environment.