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“EMERGing” Electronic Health Record Data Metrics: Insights and Implications for Assessing Residents’ Clinical Performance in Emergency Medicine
Author(s) -
SebokSyer Stefanie S.,
Shepherd Lisa,
McConnell Allison,
Dukelow Adam M.,
Sedran Robert,
Lingard Lorelei
Publication year - 2021
Publication title -
aem education and training
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.49
H-Index - 9
ISSN - 2472-5390
DOI - 10.1002/aet2.10501
Subject(s) - electronic health record , competence (human resources) , interdependence , metric (unit) , grounded theory , data collection , medical education , medicine , data science , psychology , computer science , qualitative research , health care , social psychology , social science , operations management , statistics , mathematics , sociology , political science , law , economics , economic growth
Objectives Competency‐based medical education requires that residents are provided with frequent opportunities to demonstrate competence as well as receive effective feedback about their clinical performance. To meet this goal, we investigated how data collected by the electronic health record (EHR) might be used to assess emergency medicine (EM) residents’ independent and interdependent clinical performance and how such information could be represented in an EM resident report card. Methods Following constructivist grounded theory methodology, individual semistructured interviews were conducted in 2017 with 10 EM faculty and 11 EM residents across all 5 postgraduate years. In addition to open‐ended questions, participants were presented with an emerging list of EM practice metrics and asked to comment on how valuable each would be in assessing resident performance. Additionally, we asked participants the extent to which each metric captured independent or interdependent performance. Data collection and analysis were iterative; analysis employed constant comparative inductive methods. Results Participants refined and eliminated metrics as well as added new metrics specific to the assessment of EM residents (e.g., time between signup and first orders). These clinical practice metrics based on data from our EHR database were organized along a spectrum of independent/interdependent performance. We conclude with discussions about the relationship among these metrics, issues in interpretation, and implications of using EHR for assessment purposes. Conclusions Our findings document a systematic approach for developing EM resident assessments, based on EHR data, which incorporate the perspectives of both clinical faculty and residents. Our work has important implications for capturing residents’ contributions to clinical performances and distinguishing between independent and interdependent metrics in collaborative workplace‐based settings.