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
