Physician Recommendation on Healthcare Appointment Platforms Considering Patient Choice
Physician Recommendation on Healthcare Appointment Platforms Considering Patient Choice
复制标题
DOI：
10.1109/tase.2019.2950724
复制
发表时间：
2020-04
影响因子：
5.6
通讯作者：
Hanqi Wen;Jie Song;Xin Pan
中科院分区：
文献类型：
--
作者：
Hanqi Wen;Jie Song;Xin Pan
近年来，基于网络的预约平台发展迅速，其中许多平台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，提供个性化的建议是一个巨大的挑战。受这一医疗应用的启发，本研究结合患者的选择，致力于在有限的资源下优化医生分类的实时个性化推荐，以满足患者的不同需求，提高资源配置效率。这项工作不仅考虑了医生类别对患者选择的影响，还考虑了类别中的医生在网页上的显示方式，即医生的顺序。我们采用基于位置的两阶段选择模型来捕捉患者的行为，在该模型中，患者随机查看顶级医生，然后从其中选择一位。研究了静态和动态环境下的推荐问题。在静态环境下，忽略资源容量，对医生分类推荐和显示的排名进行优化。针对这一静态问题，我们提出了一种启发式算法排序，证明了算法性能的下界，并进行了数值性能验证。在动态环境中，我们考虑了患者在匹配度和选择概率上的异质性，对随机到达的患者序列的医生推荐进行了优化。将静态排序算法融入到改进的现有算法中，提出了一种动态调整-指数库存平衡算法(ADJUST-EIB)，该算法根据实时剩余资源做出推荐决策。我们进行了一系列的数值实验，将我们的算法与几个基准测试进行了比较。数值结果表明，ADJUST-EIB算法的性能优于Benchmark算法，特别是在拥塞系统中。我们还利用真实世界的数据进行了案例研究，验证了我们的算法在提高真实系统效率方面的能力。从业者请注意-这项工作的动机是基于网络的预约平台越来越受欢迎。考虑到医生资源有限的现实，如何在基于网络的预约平台上以个性化、实时的方式为患者推荐医生，以提高医患匹配度和资源配置效率。就我们所知，我们提出了在资源有限的动态环境中推荐个性化医生排名的初步方法之一。构建了一个具体的医生推荐决策模型并提出了相应的算法，在数值分析的基础上取得了比其他基准方法更好的效果，并通过对实际系统数据的案例分析表明了该方法解决实际问题的能力。我们的方法是足够稳健的，因为该模型允许任意的到达模式。该模型和方法是专门为基于网络的预约平台设计的，但也可以很容易地应用于其他基于可视化网页的应用场景，如电子商务。
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.