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Appl Clin Inform 2020; 11(03): 497-514
DOI: 10.1055/s-0040-1714692
Research Article
Georg Thieme Verlag KG Stuttgart · New York

An Electronic Medical Record-Derived Individualized Performance Metric to Measure Risk-Adjusted Adherence with Perioperative Prophylactic Bundles for Health Care Disparity Research and Implementation Science

Michael H. Andreae 
1   Department of Anesthesiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States
,
Stephan R. Maman 
1   Department of Anesthesiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States
2   Penn State College of Medicine, Hershey, Pennsylvania, United States
,
Abrahm J. Behnam
1   Department of Anesthesiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States
› Institutsangaben Funding Research reported in this publication was supported by the National Center for Advancing Translational Sciences (NIH grants TL1 TR002016 and UL1 TR002014). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The research reported was furthermore supported through the Donald E. Martin Professorship for Anesthesia and Pain Medicine, a career development endowment of the Department of Anesthesiology at Penn State Milton S. Hershey Medical Center.
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Publikationsverlauf

24. März 2020

01. Juni 2020

Publikationsdatum:
29. Juli 2020 (online)

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Abstract

Background Health care disparity persists despite vigorous countermeasures. Clinician performance is paramount for equitable care processes and outcomes. However, precise and valid individual performance measures remain elusive.

Objectives We sought to develop a generalizable, rigorous, risk-adjusted metric for individual clinician performance (MIP) derived directly from the electronic medical record (EMR) to provide visual, personalized feedback.

Methods We conceptualized MIP as risk responsiveness, i.e., administering an increasing number of interventions contingent on patient risk. We embedded MIP in a hierarchical statistical model, reflecting contemporary nested health care delivery. We tested MIP by investigating the adherence with prophylactic bundles to reduce the risk of postoperative nausea and vomiting (PONV), retrieving PONV risk factors and prophylactic antiemetic interventions from the EMR. We explored the impact of social determinants of health on MIP.

Results We extracted data from the EMR on 25,980 elective anesthesia cases performed at Penn State Milton S. Hershey Medical Center between June 3, 2018 and March 31, 2019. Limiting the data by anesthesia Current Procedural Terminology code and to complete cases with PONV risk and antiemetic interventions, we evaluated the performance of 83 anesthesia clinicians on 2,211 anesthesia cases. Our metric demonstrated considerable variance between clinicians in the adherence to risk-adjusted utilization of antiemetic interventions. Risk seemed to drive utilization only in few clinicians. We demonstrated the impact of social determinants of health on MIP, illustrating its utility for health science and disparity research.

Conclusion The strength of our novel measure of individual clinician performance is its generalizability, as well as its intuitive graphical representation of risk-adjusted individual performance. However, accuracy, precision and validity, stability over time, sensitivity to system perturbations, and acceptance among clinicians remain to be evaluated.

Keywords

health care disparity - perioperative care - postoperative nausea and vomiting - implementation science - electronic medical record

Protection of Human and Animal Subjects

The Penn State Health Institutional Review Board determined that the described work met the criteria for exempt research according to the policies of this institution and the provisions of applicable federal regulations (STUDY00007035).


Supplementary Material

 

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