A Data-Driven Approach for Reducing Patient Waiting Times in Walk-In Clinics
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Walk-in clinics have grown in popularity in the United States as a substitute for traditional medical care delivered in primary care clinics and emergency rooms. Walk-in clinics offer an affordable option for basic medical services when compared to a hospital emergency room or an urgent care clinic. This type of medical facility simplifies the health care process for many patients with non-life threatening conditions since no previous appointments are required to see a provider. However, the open access nature and lack of patient scheduling can lead to long wait times for patients or long periods of idle time for providers. In this thesis, we derive a discrete-event simulation model to study pure walk-in clinics where patients are served without appointments. In addition, a non-linear programming model is developed to capture the trade-offs between the clinic and patients benefits and costs. A case study is discussed that consider a walk-in clinic located in central Texas. The computational study provides useful insights that are applicable to any walk-in health care facility. For instance, a trade-off between management cost and patient satisfaction can be achieved by proper allocation of resources at each station of the walk-in clinic. Even with various levels of demand (low, normal, and high), it is possible for the clinic to achieve positive results. The analysis provides valuable guidance to clinic administration about allocation of resources to improve patient satisfaction and the overall clinic performance.