Background

There is an increasing trend in using network science methods and algorithms, including community detection methods, in different areas of healthcare. These areas include protein networks, drug prescriptions, healthcare fraud detection, and drug abuse. Counterfeit drugs, off-label marketing issues, and finding the healthcare community structures in a network of hospitals, are examples of using community detection in healthcare.

Objective

This paper attempts to find physicians’ real medical specialties based on their prescription history. As a novel application of community detection in the healthcare field, this knowledge can be used as an alternative for missing values of the healthcare databases. Therefore, it could help scientists and researchers to obtain more accurate and more reliable results.

Methods

This research is done through the community detection method and applying big data tools as well as interviews with the field experts. The big data, which is used in this paper, includes 32 million written medical prescriptions in the year 2014, provided by the Health Insurance Organization. The results are validated both qualitatively and quantitatively.

Results

The findings reveal nine major communities of physicians, and labeling these communities by experts presents almost every specialty in the drug prescriptions field. Some of these communities are labeled as a single well-known specialty, and some others are consist of two or more specialties that have overlap with each other.

Conclusion

After receiving the prescription data and getting the experts’ opinions, it was revealed that some physicians might persistently prescribe drugs that are not in their fields of expertise. Regarding the accuracy of community detection and the use of existing data values, we proved this hypothesis.

中文翻译：

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社区检测在识别医师真正专长方面的新应用。

背景

在医疗保健的不同领域中，使用网络科学方法和算法（包括社区检测方法）的趋势正在增长。这些领域包括蛋白质网络，药物处方，医疗保健欺诈检测和药物滥用。假冒药品，标签外行销问题以及在医院网络中查找医疗保健社区结构都是在医疗保健中使用社区检测的示例。

目的

本文试图根据医生的处方历史找到他们真正的医学专业。作为社区检测在医疗保健领域的一种新颖应用，该知识可以用作医疗保健数据库缺失值的替代方法。因此，它可以帮助科学家和研究人员获得更准确，更可靠的结果。

方法

这项研究是通过社区检测方法和应用大数据工具以及对现场专家的采访来完成的。本文使用的大数据包括由健康保险组织提供的2014年3200万份书面医疗处方。结果得到定性和定量验证。

结果

调查结果揭示了九个主要的医师社区，并由专家对这些社区进行标记，介绍了药物处方领域中几乎每个专业。这些社区中有一些被标记为单个著名的专业，而另一些则由两个或多个彼此重叠的专业组成。

结论

在收到处方数据并征询专家意见后，发现有些医生可能会坚持开出自己专业领域以外的药物。关于社区检测的准确性和现有数据值的使用，我们证明了这一假设。