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Physician ranking optimization based on patients' browse behaviors and resource capacities
Internet Research ( IF 5.9 ) Pub Date : 2021-06-29 , DOI: 10.1108/intr-10-2020-0609
Xin Pan , Hanqi Wen , Ziwei Wang , Jie Song , Xing Lin Feng

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.



中文翻译:

基于患者浏览行为和资源能力的医师排名优化

目的

近年来,数字医疗已成为最重要的互联网应用之一,数字平台已成为患者和医生之间的接口。尽管这些技术提高了患者的便利性,但它们也给平台管理带来了新的挑战。例如,在医生评级网站上,信息过载会对患者选择医生的决策产生负面影响。这种情况需要一种自动机制来提供医生的实时排名。受在线医疗平台的启发,本研究开发了一种方法,通过考虑患者的浏览行为和服务资源的能力,在平台上提供医生排名。

设计/方法/方法

作者使用概率模型来明确捕获患者的浏览行为。由于数字系统中的大量信息使得动态排序问题难以解决,我们设计了一种结合贪婪排序策略和值函数逼近方法的值逼近算法排序。

发现

作者发现近似方法在处理数字医疗系统的排名优化方面非常有效,这主要是因为作者在模型中加入了患者行为和患者可用性。

原创性/价值

据作者所知,这是最早提出动态医师排名问题解决方案的研究之一。排名算法还可以帮助平台提高系统和运营性能。

更新日期:2021-06-29
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