Article ID: | iaor20162353 |
Volume: | 19 |
Issue: | 2 |
Start Page Number: | 170 |
End Page Number: | 191 |
Publication Date: | Jun 2016 |
Journal: | Health Care Management Science |
Authors: | Morrice Douglas, Bard Jonathan, Shu Zhichao, Wang Dongyang, Poursani Ramin, Leykum Luci |
Keywords: | queues: applications, markov processes, simulation, scheduling |
This paper presents an analysis of a residency primary care clinic whose majority of patients are underserved. The clinic is operated by the health system for Bexar County and staffed primarily with physicians in a three‐year Family Medicine residency program at The University of Texas School of Medicine in San Antonio. The objective of the study was to obtain a better understanding of patient flow through the clinic and to investigate changes to current scheduling rules and operating procedures. Discrete event simulation was used to establish a baseline and to evaluate a variety of scenarios associated with appointment scheduling and managing early and late arrivals. The first steps in developing the model were to map the administrative and diagnostic processes and to collect time‐stamped data and fit probability distributions to each. In conjunction with the initialization and validation steps, various regressions were performed to determine if any relationships existed between individual providers and patient types, length of stay, and the difference between discharge time and appointment time. The latter two statistics along with resource utilization and closing time were the primary metrics used to evaluate system performance. The results showed that up to an 8.5 % reduction in patient length of stay is achievable without noticeably affecting the other metrics by carefully adjusting appointment times. Reducing the no‐show rate from its current value of 21.8 % or overbooking, however, is likely to overwhelm the system’s resources and lead to excessive congestion and overtime. Another major finding was that the providers are the limiting factor in improving patient flow. With an average utilization rate above 90 % there is little prospect in shortening the total patient time in the clinic without reducing the providers’ average assessment time. Finally, several suggestions are offered to ensure fairness when dealing with out‐of‐order arrivals.