ArticleApplied clinical informatics2020
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.
Article in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Post-Thyroidectomy Nausea and Vomiting Using Continuous Remifentanil Infusion During Emergence Depending on the Inhaled Anesthetics: A Retrospective Cohort Study.Medicina (Kaunas, Lithuania) · 2026Article
- Disparities in Antiemetic Prophylaxis Care Processes Predicted by Patient Neighborhood: Retrospective Cohort and Geospatial Analysis.JMIR public health and surveillance · 2026Article
- Using nominal group technique to identify barriers and facilitators to preventing HIV using combination same-day pre-exposure prophylaxis and medications for opioid use disorder.Harm reduction journal · 2022Article
- Diversity, equity, and inclusion in anesthesiology.International anesthesiology clinics · 2021Article
- Ascertaining Design Requirements for Postoperative Care Transition Interventions.Applied clinical informatics · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundHealth 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.
objectivesWe 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.
methodsWe 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.
resultsWe 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.
conclusionThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.