Mayo Clinic Proceedings: Innovations, Quality & Outcomes (Sep 2017)

A Multifaceted Organizational Physician Assessment Program

  • Andrea N. Leep Hunderfund, MD, MHPE,
  • Yoon Soo Park, PhD,
  • Frederic W. Hafferty, PhD,
  • Kelly M. Nowicki, MA,
  • Steven I. Altchuler, PhD, MD,
  • Darcy A. Reed, MD, MPH

Journal volume & issue
Vol. 1, no. 2
pp. 130 – 140

Abstract

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Objective: To provide validity evidence for a multifaceted organizational program for assessing physician performance and evaluate the practical and psychometric consequences of 2 approaches to scoring (mean vs top box scores). Participants and Methods: Participants included physicians with a predominantly outpatient practice in general internal medicine (n=95), neurology (n=99), and psychiatry (n=39) at Mayo Clinic from January 1, 2013, through December 31, 2014. Study measures included hire year, patient complaint and compliment rates, note-signing timeliness, cost per episode of care, and Likert-scaled surveys from patients, learners, and colleagues (scored using mean ratings and top box percentages). Results: Physicians had a mean ± SD of 0.32±1.78 complaints and 0.12±0.76 compliments per 100 outpatient visits. Most notes were signed on time (mean ± SD, 96%±6.6%). Mean ± SD cost was 0.56±0.59 SDs above the institutional average. Mean ± SD scores were 3.77±0.25 on 4-point and 4.06±0.31 to 4.94±0.08 on 5-point Likert-scaled surveys. Mean ± SD top box scores ranged from 18.6%±16.8% to 90.7%±10.5%. Learner survey scores were positively associated with patient survey scores (r=0.26; P=.003) and negatively associated with years in practice (r=−0.20; P=.02). Conclusion: This study provides validity evidence for 7 assessments commonly used by medical centers to measure physician performance and reports that top box scores amplify differences among high-performing physicians. These findings inform the most appropriate uses of physician performance data and provide practical guidance to organizations seeking to implement similar assessment programs or use existing performance data in more meaningful ways.