Physician ranking optimization based on patients' browse behaviors and resource capacities

Digital healthcare has become one of the most important Internet applications in the recent years, and digital platforms have been acting as interfaces between the patients and physicians. Although these technologies enhance patient convenience, they create new challenges in platform management. For instance, on physician rating websites, information overload negatively influences patients' decision-making in relation to selecting a physician. This scenario calls for an automated mechanism to provide real-time rankings of physicians. Motivated by an online healthcare platform, this study develops a method to deliver physician ranking on platforms by considering patients' browse behaviors and the capacities of service resources.

The authors use a probabilistic model for explicitly capturing the browse behaviors of patients. Since the large volume of information in digital systems makes it intractable to solve the dynamic ranking problem, we design a ranking with value approximation algorithm that combines a greedy ranking policy and the value function approximation methods.

The authors found that the approximation methods are quite effective in dealing with the ranking optimization on the digital healthcare system, and it is mainly because the authors incorporate the patient behaviors and patient availability in the model.

To the best of the authors’ knowledge, this is one of the first studies to present solutions to the dynamic physician ranking problem. The ranking algorithms can also help platforms improve system and operational performance.