Optimization of teleconsultation appointment scheduling in National Telemedicine Center of China

Abstract

Teleconsultation service plays an important role in the Chinese healthcare service provision especially during the period of the COVID-19 pandemic. We propose a two-stage distributionally robust optimization model in teleconsultation appointment scheduling to minimize the total cost considering doctors’ and an inpatient’s waiting for appointments in a neurology department. This approach suits well with unique type of patients, expert doctor’s mobility and primary doctors’ time-anxiety of teleconsultation in the National Telemedicine Center of China. Given the ambiguity set based on the estimated mean and covariance matrix of the distribution, an approximate semidefinite programming model is reformulated, which can be solved by a standard solver due to special structures of constraints. In this paper, we have compared the performance of the strategy with and without considering primary doctors’ time-anxiety. We have used real-world data to conduct numerical experiments to determine the sensitivity of the results with respect to the changes in the parameters such as ambiguity set parameters, the risk level of overtime and waiting cost. Finally, managerial recommendations are provided for decision-makers to achieve better performance in the case of teleconsultation systems.