Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review

Villegas-Ortega, José; Bellido-Boza, Luciana; Mauricio, David

doi:10.1186/s40352-021-00149-3

Cited by 15 publications

(6 citation statements)

References 97 publications

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“…Among the key findings of our study, the most important contributing factors to health insurance fraud are policy type, education, and claimant age. Like our findings, Villegas-Ortega et al identified macroenvironmental factors (culture, regulations); mesoenvironmental factors (provider characteristics, management policy, reputation); microenvironmental factors (sex, race, insurance condition, language, treatments, future risk of disease); and other factors that influence health insurance fraud (Villegas-Ortega et al 2021). Regarding policy type, claimants with VIP (comprehensive-coverage policy) were associated with more fraudulent acts.…”

Section: Discussionsupporting

confidence: 80%

Fraud Detection in Healthcare Insurance Claims Using Machine Learning

Nabrawi,

Alanazi

2023

Risks

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Healthcare fraud is intentionally submitting false claims or producing misinterpretation of facts to obtain entitlement payments. Thus, it wastes healthcare financial resources and increases healthcare costs. Subsequently, fraud poses a substantial financial challenge. Therefore, supervised machine and deep learning analytics such as random forest, logistic regression, and artificial neural networks are successfully used to detect healthcare insurance fraud. This study aims to develop a health model that automatically detects fraud from health insurance claims in Saudi Arabia. The model indicates the greatest contributing factor to fraud with optimal accuracy. The labeled imbalanced dataset used three supervised deep and machine learning methods. The dataset was obtained from three healthcare providers in Saudi Arabia. The applied models were random forest, logistic regression, and artificial neural networks. The SMOT technique was used to balance the dataset. Boruta object feature selection was applied to exclude insignificant features. Validation metrics were accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC). Random forest classifiers indicated policy type, education, and age as the most significant features with an accuracy of 98.21%, 98.08% precision, 100% recall, an F1 score of 99.03%, specificity of 80%, and an AUC of 90.00%. Logistic regression resulted in an accuracy of 80.36%, 97.62% precision, 80.39% recall, an F1 score of 88.17%, specificity of 80%, and an AUC of 80.20%. ANN revealed an accuracy of 94.64%, 98.00% precision, 96.08% recall, an F1 score of 97.03%, a specificity of 80%, and an AUC of 88.04%. This predictive analytics study applied three successful models, each of which yielded acceptable accuracy and validation metrics; however, further research on a larger dataset is advised.

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Section: Discussionsupporting

confidence: 80%

Fraud Detection in Healthcare Insurance Claims Using Machine Learning

Nabrawi,

Alanazi

2023

Risks

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“…More details about the characteristics of the included studies are shown in Appendix 2. The included studies utilized AI for fraud detection (n=17) [1,2,6,8,14,17,18,19,21,22,23,24,26,27,28,30,31], identifying and classifying detected fraud (n=8) [3,4,11,12,13,15,20,25], and investigating and analyzing fraudulent data (n=6) [5,7,9,10,16,29]. The most common algorithm used in the included studies was Convolutional Neural Network (CNN) (n=13), followed by Artificial Neural Network (ANN) (n=10).…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review

Iqbal

Abd‐Alrazaq

Househ

2022

Studies in Health Technology and Informatics

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Over the past decade, Artificial Intelligence (AI) technologies have quickly become implemented in protecting data, including detecting fraud in healthcare organizations. This scoping review aims to explore AI solutions utilized in fraud detection occurring in treatment settings. To find relevant literature, PubMed and Google Scholar were searched. Out of 183 retrieved studies, 31 met all inclusion criteria. This review found that AI has been used to detect different types of fraud such as identify theft and kickbacks in healthcare. Additionally, this review discusses how AI techniques used in network mapping fraud can detect and visualize the hacker’s network. A proper system must be implemented in healthcare settings for successful fraud detection, which may overall improve the healthcare system.

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“…Several studies have shown that health insurance fraud leads to a significant increase in the cost of national health insurance programs and is one of the main causes of inefficiencies in the operation of health insurance funds [4,5]. In particular, in some high-income countries, 3-10% of annual healthcare expenditures are lost as a result of health insurance fraud, amounting to billions of dollars [6][7][8][9]. However, due to the unique characteristics of the health insurance industry and the complex and insidious of health insurance fraud, the supervision of health insurance funds is very difficult [10,11].…”

Section: Introductionmentioning

confidence: 99%

What influences the public’s willingness to report health insurance fraud in familiar or unfamiliar healthcare settings? a cross-sectional study of the young and middle-aged people in China

Xu,

Zhang,

Zhang

et al. 2024

BMC Public Health

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Introduction Young and middle-aged people are important participants in the fight against health insurance fraud. The study aims to investigate the differences in their willingness to report health insurance fraud and the factors influencing it when it occurs in familiar or unfamiliar healthcare settings. Methods Data were obtained from a validated questionnaire from 828 young and middle-aged people. McNemar’s test was used to compare the public’s willingness to report under the two scenarios. Chi-square tests and multiple logistic regression analysis were used to analyze the determinants of individuals’ willingness to report health insurance fraud in different scenarios. Results Young and middle-aged people were more likely to report health insurance fraud in a familiar healthcare setting than in an unfamiliar one (McNemar’s χ²=26.51, P < 0.05). Their sense of responsibility for maintaining the security of the health insurance fund, the government’s openness about fraud cases, and the perception of their ability to report had significant positive effects on the public’s willingness to report in both settings (P < 0.05). In a familiar healthcare setting, the more satisfied the public is with government measures to protect whistleblowers, the more likely they are to report (OR = 1.44, P = 0.025). Those who perceive the consequences of health insurance fraud to be serious are more likely to report than those who perceive the consequences to be less serious (OR = 1.61, P = 0.042). Conclusion Individuals are more likely to report health insurance fraud in familiar healthcare settings than in unfamiliar ones, in which their awareness of the severity of the consequences of health insurance fraud and their perceived risk after reporting it play an important role. The government’s publicizing of fraud cases and enhancing the public’s sense of responsibility and ability to maintain the safety of the health insurance fund may be a way to increase their willingness to report, regardless of whether they are familiar with the healthcare setting or not.

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Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review

Cited by 15 publications

References 97 publications

Fraud Detection in Healthcare Insurance Claims Using Machine Learning

Fraud Detection in Healthcare Insurance Claims Using Machine Learning

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review

What influences the public’s willingness to report health insurance fraud in familiar or unfamiliar healthcare settings? a cross-sectional study of the young and middle-aged people in China

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