Table 4.

Articles related to methods for detecting and identifying medical fraud and abuse

Method of Detection	Study Type	Study Population	Article Title	Country	Year	Author
Neural networks, decision trees, Bayesian networks, data mining	Bayesian hierarchical methods	U.S. Medicare Part B	An Unsupervised Bayesian Hierarchical Method for Medical Fraud Assessment [20]	USA	2019	Ekin, Tahir and et al.
Statistical methods	Statistical methods divided into two classes, supervised and unsupervised methods	Comprehensive survey of the statistical methods applied to health care fraud detection	A survey on statistical methods for health care fraud detection [13]	USA	2008	Jing Li and et al.
Behavioral model for identifying prescription anomalies	Statistical methods for detection of fraud and abuse	Prescribers, patients and pharmacies	Computer-aided auditing of prescription drug claims [21]	USA	2014	Vijay S Iyengar and et al.
Statistical modeling	Sparrow's fraud type classifications	Relevant and important problem in Medicaid healthcare fraud detection	Predicting Healthcare Fraud in Medicaid: A Multidimensional Data Model and Analysis Techniques for Fraud Detection [14]	USA	2013	Dallas Thornton and et al.
Random sampling of prescriptions, estimation of overpayment, data mining	Statistical methods in medical fraud assessment	An overview of fraud types and detection is followed	Statistical Medical Fraud Assessment: Exposition to an Emerging Field [22]	USA	2018	Tahir Ekin and et al.
Information technology and data sharing Integrated data warehouse for fraud detection and abuse Data mining tools Audit systems for detecting drug deviations	Systematic review	Types of Medicare/Medicaid fraud	Recommendations to protect patients and health care practices from Medicare and Medicaid fraud [23]	USA	2003	Zhen Xing Chen and et al.
Auditing and inspection	Comparative research, fraud cases and literature study has been proposed	From 17 top suspicions analyzed, we reported eventually 12 of those to offcials,	Outlier based Predictors for Health Insurance Fraud Detection within U.S. Medicaid [16]	USA	2013	Guido van Capelleveen and et al.
Bayesian methods	Application of Bayesian ideas in healthcare fraud detection	Application of Bayesian Methods	Application of Bayesian Methods in Detection of Healthcare Fraud [24]	USA	2013	Tahir Ekin and et al.
Data mining, decision trees, machine learning	Machine Learning	7.37 million encrypted treatment records beginning from 2014 on 300,000 people sampled from Hangzhou, Zhejiang, China.	Medical Fraud and Abuse Detection System Based on Machine Learning [25]	Chjna	2020	Conghai Zhang et al.
Random review of physicians' financial billing	Pilot study	Pilot study (n=188) conducted in 2015–2016 in Madrid and León	Financial fraud and health:the caseof Spain [26]	Spain	2017	Maria Victoria Zunzunegui and et al.
Diagnosis of abuse A percentage of patients who have visited more than once a month. Average number of medications prescribed in a single prescription The average cost of a prescription. The ratio of 5 expensive antibiotic prescriptions to all doctors' prescriptions The ratio of total injections prescribed to all doctors' prescriptions The total ratio of antibiotics prescribed to all doctors' prescriptions The ratio of injectable antibiotics to the doctor's prescription The ratio of prescribed injectable corticosteroids to all prescriptions by doctors Diagnosis of fluctuation Percentage of repeat patients The average cost of a prescription The ratio of prescriptions referred to the expensive pharmacy Paraphrase text	Data mining approach	Major health insurance organization dataset of private sector general physicians’ prescription claims	Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study [27]	Iran	2016	Hossein Joudaki and et al.
Manual review of medical records, artificial intelligence, data mining	Systematic review	‘prevention’ and ‘detection’ of fraud from 1975 to 2008	No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse: A Systematic Review of Literature [28]	Iran	2012	Arash Rashidian and et al.
Data mining framework	Data-mining framework	The proposed approaches have been evaluated objectively by a real-world data set gathered from the National Health Insurance (NHI) program in Taiwan	A process-mining framework for the detection of healthcare fraud and abuse [29]	Twivan	2006	Wan-Shiou Yang and San-Yih Hwang
Logistic regression, neural networks, decision trees	Data-mining framework	Taiwan's National Health Insurance system	Detecting hospital fraud and claim abuse through diabetic outpatient services. [30]	Twivan	2008	Fen-May Liou and et al.
Early detection, risk management, sub-groups, data deliberations	Sub-groups efforts and deliberations over a short period of 12 weeks.	Health insurance industry	FICCI Working Paper on Health Insurance Fraud [17]	India	2014	Hsrii
Data mining framework	Health care insurance fraud and abuse detection system	Health care insurance fraud and abuse detection system	An Effective Health Care Insurance Fraud And Abuse Detection System [18]	Nigeria	2020	Aderonke Ikuomola & Oluwafolake Esther Ojo
Auditing methods and data mining	Meta-analysis	Eighty eight literatures obtained from journal articles, conference proceedings and books based on their relevance to the research problem were reviewed	Meta-analysis of fraud, waste and abuse detection methods in healthcare [19]	Nigeria	2019	Rhoda Ikono and et al.