Open Access Article on Advancements in Blockchain for Health Care Insurance Fraud Detection Solutions: Taxonomy and Performance Evaluation

[Leila Ismail]

Dear Colleagues,

I am pleased to share our latest research that has been published Journal of Medical Internet Research (Top 5% on Scopus, CiteScore 14.4).

I look forward to hearing your feedback.

Title: "Automatic Recommender System of Development Platforms for Smart Contract-Based Health Care Insurance Fraud Detection Solutions: Taxonomy and Performance Evaluation":
https://www.jmir.org/2024/1/e50730


This study developed smart contracts to efficiently detect healthcare insurance fraud. We provided a taxonomy of fraud scenarios and implemented their detection using a suitable blockchain platform. To automatically and efficiently select the best platform, we designed and implemented a decision map-based recommender system, supported by a taxonomy of 102 blockchain platforms.

Abstract

Background:Health care insurance fraud is on the rise in many ways, such as falsifying information and hiding third-party liability. This can result in significant losses for the medical health insurance industry. Consequently, fraud detection is crucial. Currently, companies employ auditors who manually evaluate records and pinpoint fraud. However, an automated and effective method is needed to detect fraud with the continually increasing number of patients seeking health insurance. Blockchain is an emerging technology and is constantly evolving to meet business needs. With its characteristics of immutability, transparency, traceability, and smart contracts, it demonstrates its potential in the health care domain. In particular, self-executable smart contracts are essential to reduce the costs associated with traditional paradigms, which are mostly manual, while preserving privacy and building trust among health care stakeholders, including the patient and the health insurance networks. However, with the proliferation of blockchain development platform options, selecting the right one for health care insurance can be difficult. This study addressed this void and developed an automated decision map recommender system to select the most effective blockchain platform for insurance fraud detection.

Objective:This study aims to develop smart contracts for detecting health care insurance fraud efficiently. Therefore, we provided a taxonomy of fraud scenarios and implemented their detection using a blockchain platform that was suitable for health care insurance fraud detection. To automatically and efficiently select the best platform, we proposed and implemented a decision map-based recommender system. For developing the decision-map, we proposed a taxonomy of 102 blockchain platforms.

Methods:We developed smart contracts for 12 fraud scenarios that we identified in the literature. We used the top 2 blockchain platforms selected by our proposed decision-making map-based recommender system, which is tailored for health care insurance fraud. The map used our taxonomy of 102 blockchain platforms classified according to their application domains.

Results:The recommender system demonstrated that Hyperledger Fabric was the best blockchain platform for identifying health care insurance fraud. We validated our recommender system by comparing the performance of the top 2 platforms selected by our system. The blockchain platform taxonomy that we created revealed that 59 blockchain platforms are suitable for all application domains, 25 are suitable for financial services, and 18 are suitable for various application domains. We implemented fraud detection based on smart contracts.

Conclusions:Our decision map recommender system, which was based on our proposed taxonomy of 102 platforms, automatically selected the top 2 platforms, which were Hyperledger Fabric and Neo, for the implementation of health care insurance fraud detection. Our performance evaluation of the 2 platforms indicated that Fabric surpassed Neo in all performance metrics, as depicted by our recommender system. We provided an implementation of fraud detection based on smart contracts.
J Med Internet Res 2024;26:e50730

doi:10.2196/50730<https://doi.org/10.2196/50730>



Kind regards,
Leila

---
Leila Ismail, Ph.D.
Associate Professor, Dept. of Computer Science & Software Engineering
Founding Director of Intelligent Distributed Computing & Systems (INDUCE) Laboratory -
Themed Intelligent Clouds and Smart City
(Industry 5.0 AI projects and beyond)

[The sky is not the limit.  Our possibilities are limitless]

College of IT, UAE University
P.O.Box 15551, Al-Ain, UAE
Telephone: +971-3-7135530
Email: leila at uaeu.ac.ae<mailto:leila at uaeu.ac.ae>

International Liaison Chair, IEEE COINS 2024
https://coinsconf.com/<https://ems-urlprotect.trendmicro.com/wis/clicktime/v1/query?url=https%3a%2f%2fcoinsconf.com&umid=5d4b4933-35fc-4918-b664-ab70a1b45d52&auth=e76fb8029304125d326fbeca0cc52ed489c7dcd9-093ea5481f987614e341e8468520199ecb1b46b2>
IEEECloudCom 2024 Track Chair, Organizer and Chair of a Sepcial Session on  Special Session on Next-Generation Technologies in Smart Digital Healthcare: AI, Edge, Cloud, and Blockchain
https://www.cloudcom2024.org/<https://ems-urlprotect.trendmicro.com/wis/clicktime/v1/query?url=https%3a%2f%2fwww.cloudcom2024.org&umid=5d4b4933-35fc-4918-b664-ab70a1b45d52&auth=e76fb8029304125d326fbeca0cc52ed489c7dcd9-799ca6c012106348cf49771257845c34fae5aaca>

ORCID: https://orcid.org/0000-0003-0946-1818
Google Scholar: https://scholar.google.ae/citations?hl=en&user=TY-2fcUAAAAJ&view_op=list_works&sortby=pubdate







Disclaimer:"The content of this email together with any attachments, statements and opinions expressed herein contains information that is confidential in nature and intended for the named addressee(s) only. If you are not the addressee of this email or you have received this message in error please notify the sender and delete the message and any associated files from your system, you have no right to copy, print, distribute or use this email or any of its attachments, or permit or disclose its contents to any other party in any way, except with the prior approval of the sender. In case of breach of what has been explained above, you will be held legally accountable."

?????: "?????: ?? ????? ??? ?????? ?????????? ???????? ???????? ?????? ??????? ?? ??? ??????? ????? ??? ??????? ????? ??? ????? ????? ??????? ?????? ???? ???? ???? ?? ??? ?????? ???? ?? ??? ??????? ?? ??? ?? ????? ??????? ??????? ????? ????? ??????? ???? ??????? ???? ????? ?????? ?? ?????? ????? ??? ?? ??? ???? ???? ?? ??? ?? ????? ?? ????? ?? ??????? ??????? ??? ?????? ??????????? ?? ?????? ?? ????? ?? ??? ??? ??? ??? ??? ?? ??? ??? ??????? ????? ?? ?????? ????? ??? ?????? ??? ??? ?????? ???????? ?????????" .

www.uaeu.ac.ae<http://www.uaeu.ac.ae/>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://isworld.org/pipermail/aisworld_isworld.org/attachments/20241222/2f857d41/attachment.htm>