Optimization of Telemedicine Appointments in Rural Areas

What is the optimal number of patients to schedule for a telemedicine clinic, and when should they be scheduled to arrive? Telemedicine services are increasingly being used to provide medical care to patients in rural areas; thus, it is important to determine the best way to schedule patients given factors such as length of the clinic day, time needed to sanitize procedure equipment, and patients missing their appointments. The authors provide scheduling insights for a telemedicine clinic that provides procedures for bladder cancer surveillance in rural Virginia. They find that between five and seven patients should be scheduled for this clinic depending on whether the clinic would rather minimize provider overtime (five patients) or see as many patients as possible without excessive overtime (seven patients). When minimizing provider overtime, it is recommended to schedule patients close together at the beginning of the day and after lunch, because some patients may not come to their appointments. The findings from this work have informed scheduling practices at the telemedicine clinic in rural Virginia. The insight for management is that optimal appointment scheduling has the potential to reduce provider downtime between patients and efficiently allow more patients to benefit from specialty care via telemedicine. Telemedicine services are increasingly being used to provide medical care to patients in rural areas. We present a two-stage stochastic linear program to inform optimal scheduling of telemedicine patients considering cleaning of procedure devices and patient no-show behavior. We consider the weighted average of patient waiting time and provider idle time and overtime as the performance metric for the schedule. We present a case study for scheduling rural patients to a telemedicine clinic to receive a cystoscopy for bladder cancer surveillance. Through this case study, we provide scheduling insights, including the optimal number of patients to schedule, the appropriate mix of local and distant patients, and the effect of procedure length on the number of patients who can be scheduled without exceeding overtime constraints. We also present experiments to determine how sensitive the results are to changes in the no-show rate and cost coefficients for patient waiting time, provider idle time, and overtime of the clinic. The online appendix is available at https://doi.org/10.1287/serv.2018.0222 .