Hospital dual-channel adoption decisions with telemedicine referral and misdiagnosis

With the rapid development of telemedicine and the impact of the COVID-19 pandemic, more and more patients are now resorting to using telemedicine channels for healthcare services. However, for hospitals, there exists a lack of managerial guidance in place to help them adopt telemedicine in a practical and standardized way. This study considers a hospital operating with both telemedicine (virtual) and face-to-face (physical) medical channels, and which allocates its capacity by also taking into account the possibility of both referrals and misdiagnosis. Methodologically, we construct a game model based on a queuing framework. We first analyze equilibrium strategies for patient arrivals. Then we propose the necessary conditions for a hospital to develop a telemedicine channel and to operate both channels simultaneously. Finally, we find the optimal decisions for the service level of telemedicine, which can also be regarded as the optimal proportion of diseases treated by telemedicine, and the optimal hospital capacity allocation ratio between the two channels. We also find that hospitals in a full coverage market (e.g., for certain small-scale hospitals and community hospitals or cancer hospitals) are more difficult to adopt telemedicine than hospitals in a partial coverage market (e.g., for comprehensive large-scale hospitals with many potential patients). Small-scale hospitals are more suited to operating telemedicine as a gatekeeper to help triage patients, while large hospitals are more prone to regard telemedicine as a medical channel for providing professional medical services to patients. We also analyze the effects of the telemedicine cure rate and the cost ratio of telemedicine to the physical hospital on the overall healthcare system performance, including the physical hospital arrival rate, patients’ waiting time, total profit, and social welfare. Then we compare the performance, ex ante versus ex post, the implementation of telemedicine. It is shown that when the market is partially covered, the total social welfare is always higher than it was before the implementation. However, as far as the profit goes, if the telemedicine cure rate is low and the cost ratio is high, the total hospital profit may be lower than it was prior to using telemedicine. However, the profit and social welfare of hospitals in the full coverage market are always lower than it was before the implementation. In addition, the waiting time in the hospital is always higher than that before the implementation, which means that the implementation of telemedicine will make patients who must receive treatment in the physical hospital face even worse congestion than before. More insights and results are gleaned from a series of numerical studies.