Current trends suggest telemedicine will continue to play a key role in post-pandemic care delivery. Some empirical studies, however, observed that adopting telemedicine can trigger more demand for in-person visits and overcrowd the clinic. We develop a queueing game model to assess the impact of telemedicine in equilibrium. Analyzing this model allows us to characterize the optimal resource allocation for outpatient clinics and the conditions under which introducing telemedicine is beneficial.

We describe a novel approach to automatically generating data-driven simulation models from event log data by combining process mining, queue mining, and machine learning techniques. The resulting model can be used for mapping and improving the process. We describe a healthcare application of this technique where the focus is on estimating direct and indirect effects of congestion. We also discuss how to overcome a challenge posed by very scarce event log.

Problem: Impact of telehealth on patient behaviors (no-shows, unpunctuality) unclear. Analysis of 280,067 appointments shows telehealth reduces no-shows by 4.0% and late arrivals by 10.2%. Adherence improves for follow-up patients (convenience) and new patients (timely access). Improved adherence enhances throughput, efficiency, and access to care. Telehealth implementation justified for increased revenue.