Objective: To develop a model for identifying clinic performance at fulfilling next-day and walk-in requests after adjusting for patient demographics and risk. Data Source: Using Department of Veterans Affairs (VA) administrative data from 160 VA primary care clinics from 2014 to 2017. Study Design: Using a retrospective cohort design, we applied Bayesian hierarchical regression models to predict provision of timely care, with clinic-level random intercept and slope while adjusting for patient demographics and risk status. Timely care was defined as the provision of an appointment within 48 hours of any patient requesting the clinicʼs next available appointment or walking in to receive care. Data Collection/Extraction Methods: We extracted 1 841 210 timely care requests from 613 263 patients. Principal Findings: Across 160 primary care clinics, requests for timely care were fulfilled 86 percent of the time (range 83 percent-88 percent). Our model of timely care fit the data well, with a Bayesian R2 of.8. Over the four years of observation, we identified 25 clinics (16 percent) that were either struggling or excelling at providing timely care. Conclusion: Statistical models of timely care allow for identification of clinics in need of improvement after adjusting for patient demographics and risk status. VA primary care clinics fulfilled 86 percent of timely care requests.
- access/demand/utilization of services
- administrative data uses
- primary care