A new application of community detection for identifying the real specialty of physicians. (August 2020)

• Record Type: Journal Article
• Authors: Shirazi, Saeed; Albadvi, Amir; Akhondzadeh, Elham; Farzadfar, Farshad; Teimourpour, Babak
• Is Part Of: International journal of medical informatics. Volume 140(2020)
• Publication Date: 2020-08
• Subjects: Graph mining -- Community detection -- Big data -- Drug prescription; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104161
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: We used the community detection method in an innovative way of identifying the real specialty of physicians in the Spark framework as a big data analysis tool. Besides, Scala and python programming languages were used for implementations. This knowledge can help scientists accurately determine the real medical fields that the physicians prescribe in. Moreover, it could increase the accuracy of the prospective investigations on the prescription data. As a part of pre-processing, this can be a solution for missing values of the physicians' specialty field in prescription data. Abstract: 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.