Optimization of Patient Flow and Process for a Primary Care Clinic during the COVID-19 Pandemic

ABSTRACT

Many patient throughput inefficiencies result from poor communication practices, inadequate understanding of optimizing healthcare systems to maximize efficiency, and longterm complications caused by the COVID-19 pandemic. The challenges precipitated by the pandemic, combined with the need to provide safe, high-quality care to patients, have further exacerbated existing patient flow and throughput issues. The overarching goal of this project is to improve the patient experience in primary care clinics and reduce the stress placed on providers, nurses, and staff. The authors implemented a two-phased approach that combined qualitative observations with quantitative data analysis, developed a robust methodology for understanding the University Physicians of Charlottesville (UPC) Clinic's processes, and produced structured insights for stakeholders. We established what components comprised a typical patient's journey through system intake through qualitative clinic observations pre-registration, check-in, and rooming. In contrast to the qualitative observations, the quantitative analysis encompassed the complete patient experience, outs coping to include appointment durations and check-out. All quantitative analyses relied on data from the University of Virginia (UVA) Health's electronic medical record (EMR) system, Epic. In addition to the qualitative analyses, the authors utilized Cadence reports and appointment scheduling data to understand patient flow through the UPC Clinic. Primarily, the data are utilized to understand the distributions between the different patient flow milestones of registration, clinic check-in, rooming, and check-out and what factors, if any, were statistically significant. This approach enabled us to model the distribution of patient arrival times, wait times between arrival and rooming, and other relevant bottlenecks in the flow process. © 2022 IEEE.