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.

中文翻译：

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考虑患者选择的医疗保健预约平台的医师建议

近年来，基于Web的约会平台发展迅速，其中许多为患者提供医生推荐服务。考虑到患者在疾病和行为上的异质性，提供个性化建议是一个巨大的挑战。受此医疗保健应用程序的激励，本研究结合了患者的选择，并着重于优化资源有限的医师分类的实时个性化推荐，从而满足患者的不同需求并提高资源分配效率。这项工作不仅考虑医生分类对患者选择的影响，而且考虑分类中的医生如何在网页上显示，即医生的顺序。我们采用基于位置的两阶段选择模型来捕获患者行为，在这些患者中，随机查看顶级医生，然后从中选择一名。在静态和动态环境中都研究推荐问题。在静态环境中，我们忽略资源容量，并优化医师分类的建议以及显示的排名。我们针对该问题的静态版本提出了一种启发式算法SORT，并证明了算法性能的下限以及数值性能验证。在动态环境中，考虑到患者在匹配程度和选择概率上的异质性，我们针对一系列随机到达的患者优化了医生的建议。通过将静态算法SORT合并到改进的现有算法中，我们提出了一种动态算法调整指数库存平衡（Adjust-EIB），根据实时剩余资源做出推荐决策。我们进行了一系列数值实验，以将我们的算法与多个基准进行比较。数值结果表明，Adjust-EIB优于基准算法，尤其是在拥挤的系统中。我们还对真实数据进行了案例研究，并验证了我们算法在提高真实系统效率方面的能力。执业者注意 -这项工作是受到基于Web的约会平台日益普及的推动。考虑到医师资源有限的事实，我们解决了如何在基于Web的约会平台上以个性化和实时的方式为患者推荐医师的问题，以提高医师与患者之间的匹配度以及资源分配效率。据我们所知，我们提出了一种初始方法中的一种，可以在资源有限的动态环境中推荐个性化医师排名。我们建立了一个特定的模型，并提出了做出医师推荐决策的算法，该算法的执行效果优于基于数值分析的其他基准测试方法，并结合实际系统数据进行的案例研究表明，该方法具有解决实际问题的能力。我们的方法足够健壮，原因是模型允许任意到达模式。该模型和方法是专门为基于Web的约会平台设计的，但是它也可以轻松地应用于基于可视网页的其他应用场景中，例如电子商务。