Modeling open access and fixed appointment scheduling in outpatient clinics

This research addresses an optimal open access scheduling (OAS) and fixed appointment scheduling (FAS) model to offset low show rates and increase clinic profit, using analytical modeling, Monte Carlo simulation, and decision support system (DSS) model. Due to the increasing demand and capacity of healthcare services, a need for an improved scheduling system is at the forefront. In improving the scheduling system, outpatient clinics are transforming from traditional sequential scheduling to OAS. The traditional system schedules patients several weeks to months in advance resulting in high cancellation and no-show rates. The impact on patients includes high patient dissatisfaction levels and low quality of care. The OAS schedules patients within one to two days of the appointment, resulting in a decrease of patient no-show rates and increase in continuity of care. This research compares both the OAS and FAS to determine the number of patients that should be allocated in order to maximize the expected profit. The purpose of this research is to construct an analytical, simulation, and DSS model that outputs the optimal scheduling sequence given various parameters. The expected results will indicate in clinic environments where the revenue is high and cost of underscheduling and overscheduling is low will ultimately maximize the expected profit. In addition, it is concluded that underscheduling, or allocating more patients than the demand will maximize revenue. Future works of this study will include combining the FA and OA model to allow OA to be dependent on FA.