Consultation Dispatching in Telemedicine Considering Patient Satisfaction

In telemedicine, a critical focus of online medical platforms is to improve patient satisfaction by providing doctor-patient consultation dispatching services based on patient demand and doctor capacity. Existing literatures shedding lights on the satisfactory consultation dispatching mainly focused on the approach of characterizing patient satisfaction. However, the uncertainty of patient demand as well as the uncertainty of online consultation processing time, which significantly influences patient satisfaction, is often overlooked. To improve the after-consultation satisfaction of patients, we propose a consultation dispatching optimization model based on prospect theory and uncertainty theory. Firstly, we investigate the reference-point effect and time effect that sculptures patient satisfaction. With the aid of prospect theory, we then construct a deterministic optimization model that maximizes patient satisfaction under doctor capacity constraints while minimizing the consultation makespan. In practice, both patient's reference cognition to the matched doctor and doctor's processing time of consultation involve uncertainty that could be hard to be estimated precisely by the platform. Therefore, we utilize the uncertainty theory to transform the deterministic model into an uncertain programming model. Solving this model can produce the consultation dispatching results for the online medical platform. Finally, a numerical experiment is discussed to verify the effectiveness of the proposed method.