@article{95ac9c3199af47d491ac3a0a70abe9ef,
title = "Implementing Health Care Quality Measures in Electronic Health Records: A Conceptual Model",
abstract = "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.",
keywords = "Electronic Health Records, health information technology, medical informatics",
author = "Campbell, {Claire M.} and Murphy, {Daniel R.} and Taffet, {George E.} and Major, {Anita B.} and Ritchie, {Christine S.} and Bruce Leff and Naik, {Aanand D.}",
note = "Funding Information: Dr. Campbell was supported by advanced geriatrics fellowship training from the Huffington Center on Aging. Additional funding and resources are provided by the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413). These funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Funding Information: Dr. Campbell was supported by advanced geriatrics fellowship training from the Huffington Center on Aging. Additional funding and resources are provided by the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413). These funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. There are no conflicts of interest for any authors. All authors contributed to conception and writing and revisions of the manuscript. Dr. Campbell was supported by advanced geriatrics fellowship training from the Huffington Center on Aging. Additional funding and resources are provided by the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413). These funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Key Points We propose a conceptual model for guiding Electronic Health Record–based quality measure selection and implementation. Healthcare organizations can use the model as a step-by-step guide for embedding a quality measure within Electronic Health Record workflows. We provide a practical example of the model using the case of a depression screening measure in a Home-Based Medical Care setting. Why Does this Paper Matter? Quality improvement measures are increasingly embedded within Electronic Health Records but few models exist to guide the implementation and validation of these measures. We developed a structured model and describe a test case of its use to fill this key gap. We propose a conceptual model for guiding Electronic Health Record–based quality measure selection and implementation. Healthcare organizations can use the model as a step-by-step guide for embedding a quality measure within Electronic Health Record workflows. We provide a practical example of the model using the case of a depression screening measure in a Home-Based Medical Care setting. Quality improvement measures are increasingly embedded within Electronic Health Records but few models exist to guide the implementation and validation of these measures. We developed a structured model and describe a test case of its use to fill this key gap. Publisher Copyright: {\textcopyright} 2021 The American Geriatrics Society",
year = "2021",
month = apr,
doi = "10.1111/jgs.17033",
language = "English (US)",
volume = "69",
pages = "1079--1085",
journal = "Journal of the American Geriatrics Society",
issn = "0002-8614",
publisher = "Wiley-Blackwell",
number = "4",
}