Abstract
OBJECTIVE: Determine whether patient-level or provider-level factors have greater influence on patient satisfaction scores in an academic general internal medicine clinic. METHODS: Two years of data (2017-2019) from the Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CGCAHPS) surveys from ambulatory internal medicine clinic visits in an academic health center located in the Midwest United States were used. Patient satisfaction was measured using the overall provider satisfaction score (0-10), dichotomized with 9-10 defined as satisfactory and 0-8 as unsatisfactory. Provider-level independent variables included age, sex, race/ethnicity, provider type, service type, clinical effort, academic rank, and years since graduation. Patient-level factors included age, sex, race/ethnicity, education, and Epic Risk Score. Generalized mixed-effects logistic regression models were used to investigate associations between top-box satisfaction score and patient- and provider-level factors, accounting for the nesting of patients within providers. RESULTS: Thirty-three providers and 4597 patients were included in the analysis. Male providers (OR, 1.57; 95% CI, 1.00, 2.47), minority group 2 (OR, 3.54; 95% CI, 1.24, 10.07) and minority group 3 (OR, 6.04; 95% CI, 1.45, 25.12), faculty (OR, 3.83; 95% CI, 1.56, 9.36), and primary care providers (OR, 5.60; 95% CI, 1.62, 19.34) had increased odds of having a top-box rating compared with females, minority group 1, advanced practice providers, and perioperative providers respectively. Age was the only patient independent correlate of top-box rating with a 3% increased odds of top-box rating for every year increase in age (OR, 1.03; 95% CI 1.02, 1.03). CONCLUSIONS: In this academic general internal medicine clinic, top-box satisfaction scores were more strongly associated with provider-level factors, including provider race/ethnicity, provider type, and service type, as opposed to patient-level factors. Further research is needed to confirm these findings and identify potential system-level interventions.