Operational-strategy Seminars

Over the past few years, healthcare providers have widely adopted telemedicine consultations. On one hand, telemedicine has the potential of increasing the availability of medical appointments. On the other hand, due to the limitation of diagnosis and treatment methods, telemedicine may be ineffective and lead to in-person follow-up visits. Our model is motivated by this setting. We study a healthcare system as a queuing network that provides two types of services: telemedicine and in-person. Each service station is modeled as an M/M/1 unobservable queue. We assume that the in-person visit guarantees a successful treatment; whereas a telemedicine visit successfully treats patients with a probability that decreases with the patient’s condition complexity. If the telemedicine visit fails to succeed, an in-person follow-up visit is required. We assume patients are heterogeneous with respect to their service complexity levels and model their strategic choices between the two service channels as a queueing game. We characterize the equilibrium and the socially optimal patients’ choices. We investigate the impact of information granularity and find that improving patients' perception of their condition complexity level is not always optimal. This limitation can be overcome by simultaneously deploying a priority rule that can under certain conditions induce the social optimum.