Physician Recommendation on Healthcare Appointment Platforms Considering Patient Choice

In recent years, web-based appointment platforms develop rapidly, and many of them provide physician recommendation services to patients. Considering patients’ heterogeneity in both illness and behavior, it is a great challenge to deliver personalized recommendations. Motivated by this healthcare application, this study incorporates patient choices and focuses on optimizing real-time personalized recommendation of physician assortment with limited resources, in order to satisfy patients’ varying demands and improve the resource allocation efficiency. This work considers not only the influence of physician assortment to patient choice but also how physicians in the assortment are displayed on the webpage, i.e., the order of physicians. We adopt the location-based two-stage choice model to capture the patient behavior, in which patients randomly view the top physicians and then choose one among them. The recommendation problem is studied in both static and dynamic environments. In the static environment, we ignore resource capacities and optimize the recommendation of physician assortment as well as the displayed ranking. We propose a heuristic algorithm SORT for this static version of the problem and prove the lower bound of algorithm performance, along with the numerical performance validation. In the dynamic environment, we optimize the physician recommendations for a sequence of randomly arriving patients considering patients’ heterogeneity in both matching degrees and choice probabilities. We propose a dynamic algorithm Adjust-exponential inventory balancing (Adjust-EIB) by incorporating our static algorithm SORT in the improved existing algorithm, which makes recommendation decisions based on the real-time remaining resources. We conduct a series of numerical experiments to compare our algorithm with several benchmarks. The numerical results show that Adjust-EIB outperforms the benchmark algorithms, especially in congested systems. We also conduct a case study with real-world data and verify the capability of our algorithm in improving real-world system efficiency. Note to Practitioners —This work is motivated by the increasing popularity of web-based appointment platforms. Considering the fact of limited physician resources, we address the issue of how to recommend physicians for patients in a personalized and real-time way on web-based appointment platform, in order to improve the matching degree between physicians and patients as well as the resource allocation efficiency. To the best of our knowledge, we propose one of the initial methods to recommend personalized physician rankings in dynamic environments with limited resources. We construct a specific model and propose algorithms to make physician recommendation decision, which performs better than other benchmark approaches based on the numerical analysis, and the case study with real-world system data indicates our method’s capability of solving the practical problem. Our method is robust enough for the reason that the model allows arbitrary arrival pattern. The model and methods are specifically designed for the web-based appointment platforms, but it can also be easily applied in other application scenarios based on visual web pages, such as e-commerce.