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Implementing Health Care Quality Measures in Electronic Health Records: A Conceptual Model
Author(s) -
Campbell Claire M.,
Murphy Daniel R.,
Taffet George E.,
Major Anita B.,
Ritchie Christine S.,
Leff Bruce,
Naik Aanand D.
Publication year - 2021
Publication title -
journal of the american geriatrics society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.992
H-Index - 232
eISSN - 1532-5415
pISSN - 0002-8614
DOI - 10.1111/jgs.17033
Subject(s) - medicine , conceptual model , quality (philosophy) , measure (data warehouse) , quality management , health care quality , health care , medical record , process (computing) , nursing , process management , operations management , computer science , data mining , database , philosophy , business , epistemology , radiology , economics , economic growth , operating system , management system
Background/Objectives There is significant literature on the development and validation of quality measures, but comparably less on their implementation into learning health systems. Electronic Health Records (EHRs) have made vast amounts of data available for quality improvement purposes. In this paper we describe a conceptual model for EHR implementation of quality measures. Design The model involves five steps: (1) select a measure; (2) define measure criteria; (3) validate criteria and measurement process; (4) improve recording of measure‐related activity; and (5) engage quality improvement processes. The model was used to develop and implement a quality measure in the Home‐Based Medical Care (HBMC) setting. Setting Harris Health House Call Program (HHHC) provides primary medical and palliative care for homebound patients in Houston. Participants Four‐hundred twenty‐four primary care patients followed in the HHHC. Measurement Completion rate of the 9‐item Patient Health Questionnaire (PHQ‐9) within the Electronic Health Record of newly enrolled HHHC patients. Results Use of the conceptual model to guide implementation of a quality measure of depression screening in a HMBC practice was successful. Additional components of early leadership and clinician buy‐in were required, as well as strong relationships with IT to ease implementation and limit disruptions in clinicians' work‐flow. Conclusion This conceptual model was feasible for guiding implementation of a quality measure for depression care of HBMC patients, and it can guide broader implementation of EHR‐based quality measures in the future.