Healthcare Fraud Detection Based on Trustworthiness of Doctors

Cui, Haoyi; Li, Hui; Zhou, Yan

doi:10.1109/trustcom.2016.0048

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…The main issue is that types of frauds are already defined using storyboards in this research. In [24], the rational treatment model for some diseases using graph mining and frequent pattern mining techniques is proposed. The copying prescription problem is also dealt for assessing the doctors' trustworthiness which is one of the critical metrics for detecting fraud at the provider level.…”

Section: Background and Related Workmentioning

confidence: 99%

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

et al. 2022

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With the exponential rise in government and private health-supported schemes, the number of fraudulent billing cases is also increasing. Detection of fraudulent transactions in healthcare systems is an exigent task due to intricate relationships among dynamic elements, including doctors, patients, and services. Hence, to introduce transparency in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in existing procedures, so that the fraudulent medical billing cases can be accurately identified. Moreover, there is also a need to optimize both the cost burden for the service provider and medical benefits for the client. This paper presents a novel process-based fraud detection methodology to detect insurance claim-related frauds in the healthcare system using sequence mining concepts. Recent literature focuses on the amount-based analysis or medication versus disease sequential analysis rather than detecting frauds using sequence generation of services within each specialty. The proposed methodology generates frequent sequences with different pattern lengths. The confidence values and confidence level are computed for each sequence. The sequence rule engine generates frequent sequences along with confidence values for each hospital's specialty and compares them with the actual patient values. This identifies anomalies as both sequences would not be compliant with the rule engine's sequences. The process-based fraud detection methodology is validated using last five years of a local hospital's transactional data that includes many reported cases of fraudulent activities.

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Section: Background and Related Workmentioning

confidence: 99%

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

et al. 2022

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“…The similarity adjacency graph is used along with group mining for distinguishing the normal behaviour from abnormal behaviours [29]. The treatment model for different diseases, that of, assessing the doctors' trustworthiness, which is one of the critical metrics for detecting fraud at the provider level, is introduced [30]. The association rule mining is a very important technique which generates rules for the frequently occurring items.…”

mentioning

confidence: 99%

Medical Health Benefit Management System for Real-Time Notification of Fraud Using Historical Medical Records

et al. 2020

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This paper presents a novel framework for fraud detection in healthcare systems which self-learns from the historical medical data. Historical medical records are required for training and testing of machine learning models. The main problem being faced by both private and government health supported schemes is a rapid rise in the amount of claims by beneficiaries mostly based on fraudulent billing. Detection of fraudulent transactions in healthcare systems is a strenuous task due to intricate relationships among dynamic elements including doctors, patients, service. In light of aforementioned challenges in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in procedures which may lead to successful reimbursement of fraudulent medical bills. In order to address the issue of fraud in healthcare programs our solution proposes a framework based on three entities (patient, doctor, service). Firstly, the framework computes association scores for three elements of the healthcare ecosystem namely patients, doctors or services. The framework filters out identified cases using association scores. The Confidence values, after G-means clustering of transactional data, are computed for each service in each specialty. Rules are generated based on the confidence values of services for each specialty. Then, an evaluation of identified cases is done using rule engine. The framework classifies cases into fraudulent activities based on the similarity bit’s value. The validation of framework is performed on local hospital employees transactional data which includes many reported cases of fraudulent activities in addition to some introduced anomalies.

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Fraudulent traffic detection in online advertising with bipartite graph propagation algorithm

2021

Expert Systems with Applications

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Healthcare Fraud Detection Based on Trustworthiness of Doctors

Cited by 8 publications

References 21 publications

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

Medical Health Benefit Management System for Real-Time Notification of Fraud Using Historical Medical Records

Fraudulent traffic detection in online advertising with bipartite graph propagation algorithm

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