Abstract
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.
Highlights
This paper presents a novel framework for fraud detection in healthcare systems which self-learns from the historical medical data
This paper presents a novel framework for the fraud detection in healthcare; which considers all three main elements, namely, Patient, Doctors and Services
The addressed problem is the constant increase in employees insurance coverage expenditures in each year as depicted in Figure 7 and it can be predicted as exponential increment in coming years due to increase in healthcare frauds
Summary
‘Fraud’ and ‘abuse’, these two phrases are generally used to identify the major medical reimbursement issues that defeat the ultimate objective of a valid claim. The major concern is to reduce/prevent the chance of fraudulent activities in such programs This can only be achieved by implementing different adaptive-modelling techniques for detecting fraud through the healthcare data. For this we have utilized last five years insurance claim data of employees of one of the largest and well-equipped hospital of Pakistan. This paper presents a novel framework for the fraud detection in healthcare; which considers all three main elements, namely, Patient (service-consumer), Doctors (service_providers) and Services (lab tests and treatments). The association scores are computed based on frequency of visits between the above mentioned elements and used these association scores to detect anomalies Another novel idea of generating confidence values of all services in each specialty of a local hospital is introduced.
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