The Impact of Information-Granularity and Prioritization on Patients’ Care Modality Choice

Abstract

The past few years have witnessed a significant expansion in telemedicine adoption by healthcare providers. On one hand, telemedicine has the potential to increase patients' access to medical appointments. On the other hand, due to the limitations of remote diagnostic and treatment methods, telemedicine may be insufficient for patients' treatment needs and may necessitate subsequent in-person follow-up visits. To better understand this tradeoff, we model the healthcare system as a queueing network providing two types of service: telemedicine and in-person consultations. We assume that an in-person visit guarantees successful treatment, whereas a telemedicine visit may fail to meet the patient's treatment needs with a probability that is contingent on individual patient characteristics. We formulate patients' strategic choices between these care modalities as a queueing game, and characterize the game-theoretic equilibrium and the socially optimal patients' choices. We further examine how improving patients' understanding of their telemedicine suitability through predictive analytics at the online triage stage affects system performance. We find that increasing information granularity maximizes the stability region of the system but may not always be optimal in reducing the average waiting time. This limitation, however, can be overcome by simultaneously deploying a priority rule that induces the social optimum under specific conditions. Finally, leveraging real-world data from a large academic hospital in the United States, we perform a comprehensive case study that encompasses both the development of a prediction model for in-person follow-up needs and the implementation of effective information provision and prioritization strategies.

Keywords: Telemedicine, Online Triage, Strategic Queueing, Information Granularity, Waiting Times, Priority Rules