Architecture for Detecting Fraudulent Transactions

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Table of Contents

Introduction
Research Gap and Motivation
Design and Architecture
Case Study and Results
Limitations and Future Work
Conclusion
Reference

Introduction

All Americans are potentially impacted by healthcare fraud, waste, and abuse. That is why it is crucial to have a firm grasp of the issue, its symptoms, and preventative measures. Well, healthcare fraud occurs when a person uses deception to obtain financial gain from a healthcare program like Medicaid or Medicare. Overuse of the healthcare system is an example of waste. Additionally, abuse occurs when standard medical procedures are not followed, which results in unnecessary costs and interventions. In the article called A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems, researchers introduce a unique process-based approach to health coverage fraud prevention and detection by applying sequence mining ideas (Matloob et al., 2022). The purpose of this paper is to bring attention to the problem of healthcare fraud by reviewing a recent article that proposes an approach to address the issue.

Research Gap and Motivation

The authors start out by providing an overview of their contributions, which are as follows: they mined time series traces to figure out how physicians and hospitals managed each individual patient throughout 60 different specializations. Thanks to this study, operators of network systems were able to implement preemptive actions in the event of anomalous activity. This work addresses a research gap that was previously recognized and filled.

The majority of the previous research has put an emphasis on utilizing domain experts to build their knowledge bases. Nevertheless, the authors of this study believe there is a demand for a system that makes use of machine learning methods (Matloob et al., 2022). According to estimates provided by the National Healthcare Anti-Fraud Association, health insurance fraud costs the economy of the United States approximately $80 billion annually (Matloob et al., 2022). The accumulation of such facts served as the fundamental motivation for carrying out this investigation.

Design and Architecture

The authors’ proposed healthcare fraud detection methodology’s design concept and architecture were provided in the system architecture design part. The designs provide an idea to which the architecture gives physical form. It also displays the architecture from two different perspectives: the use case perspective and the logical perspective. The use cases consist of the following: login, patient transaction, maintaining patient information, maintaining doctor information, selecting actors for analysis, monitoring ratings of actors, viewing performance reports, rule editor, and close monitoring (Matloob et al., 2022). An application layer, a business services layer, and a middleware layer make up the logical perspective design (Matloob et al., 2022). The use of layers allowed for a more systematic organization of the components necessary to develop software solutions to the given issue.

Case Study and Results

A real-world case study was required to put the framework to the test. In a case study of the Ophthalmologist field, the authors calculated a measure of confidence for every service offered by the field’s practitioners. In this field, the clients received a Scan Biometry for Cataract Surgery, Galilei Scan, and an OCT scan (Matloob et al., 2022). On the basis of a sequence database of treatment and patient data, they have employed a prefix span sequence mining approach and Bayes rule to populate the engine with both common and uncommon sequences (Matloob et al., 2022).

Patient deviations from predefined sequences in a sequence rule engine have been identified through analyses of medical behavior in patient care. Hence, they were able to identify patterns of conduct that deviated from the norm in healthcare. As soon as out-of-the-ordinary patterns were uncovered, an additional investigation was conducted to find instances of fraud. The case study in ophthalmology was proven to be successful and has the potential to be applied to other areas of medicine.

Limitations and Future Work

Given that the dataset contained sensitive patient information, it took much work to come by. Although a five-year transactional dataset was employed, authors note that larger datasets would be more beneficial for testing the architecture and methodology’s efficacy (Matloob et al., 2022). Larger datasets would provide greater visualization and highlight the advantages of the proposed work from a more holistic view. Additional elements or areas of expertise that aid in the evaluation of fraud can be added to the suggested technique in further research. For each ailment, a digital physician order entry system can be designed utilizing machine learning techniques, which would further enhance the suggested fraud detection procedure (Matloob et al., 2022). All of the future work will be more reflective and more likely to provide positive results if adopted a more comprehensive vision.

Conclusion

The costs of fraud in the healthcare industry are considerable, both monetarily and in terms of public trust and confidence in the reliability of the American healthcare system as a whole. No one should believe that healthcare fraud never harms anyone. It is undeniable that the consequences of healthcare fraud can be severe. People are frequently used for profit and harmed by undergoing medical operations that are unnecessary or hazardous. Personal insurance data or medical records are stolen and used to file fraudulent claims. This article has provided not only a thorough study of the topic at hand but also a tangible plan to enhance the quality and safety of patient outcomes and care delivery.

Reference

Matloob, I., Khan, S. A., Rukaiya, R., Khattak, M. A. K., & Munir, A. (2022). A sequence mining-based novel architecture for detecting fraudulent transactions in healthcare systems. IEEE Access, 10, 48447–48463. Web.

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