Using Electronic Health Record Data to Measure Care Quality for Individuals with Multiple Chronic Medical Conditions. Issue 9 (7th July 2016)

• Record Type: Journal Article
• Authors: Bayliss, Elizabeth A.; McQuillan, Deanna B.; Ellis, Jennifer L.; Maciejewski, Matthew L.; Zeng, Chan; Barton, Mary B.; Boyd, Cynthia M.; Fortin, Martin; Ling, Shari M.; Tai‐Seale, Ming; Ralston, James D.; Ritchie, Christine S.; Zulman, Donna M.
• Is Part Of: Journal of the American Geriatrics Society. Volume 64:Issue 9(2016:Sep.)
• Page Start: 1839
• Page End: 1844
• Publication Date: 2016-07-07
• Subjects: quality -- multimorbidity -- electronic health record; Geriatrics -- Periodicals; 618.97
• DOI: 10.1111/jgs.14248
• Languages: English
• ISSNs: 0002-8614

Abstract

Abstract : Objectives: To inform the development of a data‐driven measure of quality care for individuals with multiple chronic conditions (MCCs) derived from an electronic health record (EHR). Design: Qualitative study using focus groups, interactive webinars, and a modified Delphi process. Setting: Research department within an integrated delivery system. Participants: The webinars and Delphi process included 17 experts in clinical geriatrics and primary care, health policy, quality assessment, health technology, and health system operations. The focus group included 10 individuals aged 70–87 with three to six chronic conditions selected from a random sample of individuals aged 65 and older with three or more chronic medical conditions. Measurements: Through webinars and the focus group, input was solicited on constructs representing high‐quality care for individuals with MCCs. A working list was created of potential measures representing these constructs. Using a modified Delphi process, experts rated the importance of each possible measure and the feasibility of implementing each measure using EHR data. Results: High‐priority constructs reflected processes rather than outcomes of care. High‐priority constructs that were potentially feasible to measure included assessing physical function, depression screening, medication reconciliation, annual influenza vaccination, outreach after hospital admission, and documented advance directives. High‐priority constructs that were less feasible to measure included goal setting and shared decision‐making, identifying drug–drug interactions, assessing social support, timely communication with patients, and other aspects of good customer service. Lower‐priority domains included pain assessment, continuity of care, and overuse of screening or laboratory testing. Conclusion: High‐quality MCC care should be measured using meaningful process measures rather than outcomes. Although some care processes are currently extractable from electronic data, capturing others will require adapting and applying technology to encourage holistic, person‐centered care.