No evidence of the effect of the interventions to combat health care fraud and abuse: a systematic review of literature

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

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.

The Public's Self-Avoidance and Other-Reliance in the Reporting of Medical Insurance Fraud: A Cross-Sectional Survey in China

Purpose

To understand the public's self-willingness to report medical insurance fraud and their expectations on others, to provide a reference for the government to do a good job in medical insurance anti-fraud.

Methods

Data were obtained from a questionnaire survey of 846 respondents in China. Descriptive statistical analyses and multinomial logistic regression were used to analyze the different subjective attitudes of the public toward different subjects when faced with medical insurance fraud and the influencing factors.

Results

511 (60.40%) respondents were willing to report medical insurance fraud, while 739 (87.35%) respondents expected others to report it. 485 (57.33%) respondents were willing and expected others to report medical insurance fraud, followed by those who were not willing but expected others to report it (254, 30.02%). Compared to those who were unwilling to report themselves and did not want others to report, those who believe their reporting is useless (OR=3.13, 95% CI=1.15-8.33) and those who fear for their safety after reporting (OR=2.96, 95% CI=1.66-5.26) were more likely to expect others to report. Self-reporting willingness was stronger among the public who were satisfied with the government's protective measures for the safety of whistleblowers (OR=4.43, 95% CI=1.38-14.17). The public who believe that both themselves and others have responsibilities to report medical insurance fraud were willing to report and expect others to do the same.

Conclusion

The public had a "self-avoidance" and "other-reliance" mentality in medical insurance anti-fraud. The free-rider mentality, lack of empathy, concerns about own risk after reporting, and the interference of decentralized responsibility were important factors contributing to this public mentality. At this stage, the government should prevent the public's "collective indifference" in medical insurance anti-fraud efforts. Improving the safety and protection of whistleblowers and making everyone feel more responsible and valued may be effective incentives to enhance the public's willingness to report.

Tripartite Evolutionary Game and Simulation Analysis of Healthcare Fraud Supervision under the Government Reward and Punishment Mechanism

This study aims to provide useful insights for the Chinese government in dealing with healthcare fraud by creating an evolutionary game model that involves hospitals, third-party entities, and the government based on the government reward and punishment mechanism. This paper analyzes the evolutionary stability of each participant's strategy choice, discusses the influence of each element on the tripartite strategy choice, and further analyzes the stability of the equilibrium point in the tripartite game system. The results show that (1) the government increasing fines on hospitals is conducive to compliant hospital operations, and the incentive mechanism has little effect on such operations; (2) the lack of an incentive mechanism for third parties results in false investigations by third parties; and (3) rewards from higher levels of government promote strict supervision by local governments, but that the high cost of supervision and rewards for hospitals inhibits the probability of strict supervision. Finally, Matlab 2020a is used for simulation analysis to provide a reference for the government to improve the supervision of healthcare fraud.

Why Not Blow the Whistle on Health Care Insurance Fraud? Evidence from Jiangsu Province, China

Purpose

To identify the factors that influence whistleblowing behavior as it relates to health care insurance fraud in Jiangsu Province, China.

Methods

To construct a factor model and formulate research hypotheses using the Motivation–Opportunity–Ability framework. We designed a questionnaire containing 24 items and distributed it on-site to 2081 respondents in Jiangsu Province, China. Afterward, we applied structural equation modeling to validate the research hypotheses.

Results

Policy awareness negatively contributes to whistleblowing behavior, risk perception does not reduce the incentive to blow the whistle, and an inability to recognize fraud is another critical barrier to converting whistleblowing intentions into behavior.

Conclusion

Practices that are likely to promote citizen whistleblowing on insurance fraud may focus on the constraints identified by the comprehensive Motivation–Opportunity–Ability framework.

A Study of Health Insurance Fraud in China and Recommendations for Fraud Detection and Prevention

Healthcare insurance fraud influences not only organizations by overburdening the already fragile healthcare systems, but also individuals in terms of increasing premiums in health insurance and even fatalities. Identifying the behavioral characteristics of fraudulent claims can help shed light on the development of artificial intelligence and machine learning technologies to detect fraud in health information system research. In this paper, a theoretical model of medical insurance fraud identification is proposed, which characterizes the judgment variables of fraud from the three dimensions of time, quantity, and expenses. The model is verified with large-scale, real-world medical records. Our study shows that, in comparison with claims made by normal people, fraudulent claims usually have a greater frequency of hospital visits, and more medical bills, accompanied by higher amounts of medical expenses. An interesting discovery is that the price per bill for fraudulent cases is not statistically different from the normal cases.

Predictors of loss due to pharmaceutical fraud: evidence from the U.S

BACKGROUND

Globally and in the U.S. in particular, pharmaceutical fraud account for a large number out of all crimes in health care, which result into severe costs to the society. The Academy of Managed Care Pharmacists (Fraud, waste, and abuse in prescription drug benefits. 2019. Posted May 20. https://www.amcp.org/policy-advocacy/policy-advocacy-focus-areas/where-we-stand-position-statements/fraud-waste-and-abuse-prescription-drug-benefits .) estimate that pharmacy fraud is 1% of costs, therefore estimating that pharmacy fraud costs at $3.5 billion, given that pharmacy costs are $358 billion (Statista. Prescription drug expenditure in the United States from 1960 to 2020. 2021. https://www.statista.com/statistics/184914/prescription-drug-expenditures-in-the-us-since-1960/ ).

AIM

This exploratory study aims to demonstrate a fraudster's profile as well as to estimate average consequences in terms of costs and identify the loss predictors' hierarchy in the pharmaceutical industry in the U.S.

MATERIALS AND METHODS

Data from the Corporate Prosecution Registry and mixed-effects models are utilized for this purpose. The dataset covers years 2001-2020 and 75 cases, falling into one of the following broad sub-categories: misbranding, counterfeit, off-label use of drugs/deceptive marketing; violation of the Food, Drug and Cosmetic Act.

RESULTS

The main factors positively associated with loss due to pharmaceutical fraud are: (i) duration of , and (ii) the scheme and scheme being executed at a U.S. public company. Surprisingly, presence of collusion negatively and significantly effects the cost. Potential factors include: (a) principal perpetrator being a white American and/or male, and (b) number of employees at individual and organizational level respectively.

CONCLUSION

This study empirically justifies considering loss, due to pharmaceutical fraud, from a multi-level perspective. Identified profiles of a typical fraudster helped to elaborate on specific practical recommendations aimed at pharmaceutical fraud prevention in the U.S.

DepTSol: An Improved Deep-Learning- and Time-of-Flight-Based Real-Time Social Distance Monitoring Approach under Various Low-Light Conditions

Social distancing is an utmost reliable practice to minimise the spread of coronavirus disease (COVID-19). As the new variant of COVID-19 is emerging, healthcare organisations are concerned with controlling the death and infection rates. Different COVID-19 vaccines have been developed and administered worldwide. However, presently developed vaccine quantity is not sufficient to fulfil the needs of the world’s population. The precautionary measures still rely on personal preventive strategies. The sharp rise in infections has forced governments to reimpose restrictions. Governments are forcing people to maintain at least 6 feet (ft) of safe physical distance to stay safe. With summers, low-light conditions can become challenging. Especially in the cities of underdeveloped countries, where poor ventilated and congested homes cause people to gather in open spaces such as parks, streets, and markets. Besides this, in summer, large friends and family gatherings mostly take place at night. It is necessary to take precautionary measures to avoid more drastic results in such situations. To support the law and order bodies in maintaining social distancing using Social Internet of Things (SIoT), the world is considering automated systems. To address the identification of violations of a social distancing Standard Operating procedure (SOP) in low-light environments via smart, automated cyber-physical solutions, we propose an effective social distance monitoring approach named DepTSol. We propose a low-cost and easy-to-maintain motionless monocular time-of-flight (ToF) camera and deep-learning-based object detection algorithms for real-time social distance monitoring. The proposed approach detects people in low-light environments and calculates their distance in terms of pixels. We convert the predicted pixel distance into real-world units and compare it with the specified safety threshold value. The system highlights people violating the safe distance. The proposed technique is evaluated by COCO evaluation metrics and has achieved a good speed–accuracy trade-off with 51.2 frames per second (fps) and a 99.7% mean average precision (mAP) score. Besides the provision of an effective social distance monitoring approach, we perform a comparative analysis between one-stage object detectors and evaluate their performance in low-light environments. This evaluation will pave the way for researchers to study the field further and will enlighten the efficiency of deep-learning algorithms in timely responsive real-world applications.

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

BACKGROUND

Healthcare fraud entails great financial and human losses; however, there is no consensus regarding its definition, nor is there an inventory of its manifestations and factors. The objective is to identify the definition, manifestations and factors that influence health insurance fraud (HIF).

METHODS

A scoping review on health insurance fraud published between 2006 and 2020 was conducted in ACM, EconPapers, PubMed, ScienceDirect, Scopus, Springer and WoS.

RESULTS

Sixty-seven studies were included, from which we identified 6 definitions, 22 manifestations (13 by the medical provider, 7 by the beneficiary and, 2 by the insurance company) and 47 factors (6 macroenvironmental, 15 mesoenvironmental, 20 microenvironmental, and 6 combined) associated with health insurance fraud. We recognized the elements of fraud and its dependence on the legal framework and health coverage. From this analysis, we propose the following definition: "Health insurance fraud is an act of deception or intentional misrepresentation to obtain illegal benefits concerning the coverage provided by a health insurance company". Among the most relevant manifestations perpetuated by the provider are phantom billing, falsification of documents, and overutilization of services; the subscribers are identity fraud, misrepresentation of coverage and alteration of documents; and those perpetrated by the insurance company are false declarations of benefits and falsification of reimbursements. Of the 47 factors, 25 showed an experimental influence, including three in the macroenvironment: culture, regulations, and geography; five in the mesoenvironment: characteristics of provider, management policy, reputation, professional role and auditing; 12 in the microenvironment: sex, race, condition of insurance, language, treatments, chronic disease, future risk of disease, medications, morale, inequity, coinsurance, and the decisions of the claims-adjusters; and five combined factors: the relationships between beneficiary-provider, provider-insurance company, beneficiary-insurance company, managers and guānxi.

CONCLUSIONS

The multifactorial nature of HIF and the characteristics of its manifestations depend on its definition; Identifying the influence of the factors will support subsequent attempts to combat HIF.

Anti-corruption, transparency and accountability in health: concepts, frameworks, and approaches

Background: As called for by the Sustainable Development Goals, governments, development partners and civil society are working on anti-corruption, transparency and accountability approaches to control corruption and advance Universal Health Coverage.

Objectives: The objective of this review is to summarize concepts, frameworks, and approaches used to identify corruption risks and consequences of corruption on health systems and outcomes. We also inventory interventions to fight corruption and increase transparency and accountability.

Methods: We performed a critical review based on a systematic search of literature in PubMed and Web of Science and reviewed background papers and presentations from two international technical meetings on the topic of anti-corruption and health. We identified concepts, frameworks and approaches and summarized updated evidence of types and causes corruption in the health sector.

Results: Corruption, or the abuse of power for private gain, in health systems includes bribes and kickbacks, embezzlement, fraud, political influence/nepotism and informal payments, among other behaviors. Drivers of corruption include individual and systems level factors such as financial pressures, poorly managed conflicts of interest, and weak regulatory and enforcement systems. We identify six typologies and frameworks that model relationships influencing the scope and seriousness of corruption, and show how anti-corruption strategies such as transparency, accountability, and civic participation can affect corruption risk. Little research exists on the effectiveness of anti-corruption measures; however, interventions such as community monitoring and insurance fraud control programs show promise.

Conclusions: Corruption undermines the capacity of health systems to contribute to better health, economic growth and development. Interventions and resources on prevention and control of corruption are essential components of health system strengthening for Universal Health Coverage.

Medical Fraud and Abuse Detection System Based on Machine Learning

It is estimated that approximately 10% of healthcare system expenditures are wasted due to medical fraud and abuse. In the medical area, the combination of thousands of drugs and diseases make the supervision of health care more difficult. To quantify the disease–drug relationship into relationship score and do anomaly detection based on this relationship score and other features, we proposed a neural network with fully connected layers and sparse convolution. We introduced a focal-loss function to adapt to the data imbalance and a relative probability score to measure the model’s performance. As our model performs much better than previous ones, it can well alleviate analysts’ work.

Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

Background

Surgical resection of pheochromocytoma may lead to high risk factors for intraoperative hemodynamic instability (IHD), which can be life-threatening. This study aimed to investigate the risk factors that could predict IHD during pheochromocytoma surgery by data mining.

Method

Relief-F was used to select the most important features. The accuracies of seven data mining models (CART, C4.5, C5.0, and C5.0 boosted), random forest algorithm, Naive Bayes and logistic regression were compared, the cross-validation, hold-out, and bootstrap methods were used in the validation phase. The accuracy of these models was calculated independently by dividing the training and the test sets. Receiver-Operating Characteristic curves were used to obtain the area under curve (AUC).

Result

Random forest had the highest AUC and accuracy values of 0.8636 and 0.8509, respectively. Then, we improved the random forest algorithm according to the classification of imbalanced data. Improved random forest model had the highest specificity and precision among all algorithms, including relatively higher sensitivity (recall) and the highest f1-score integrating recall and precision. The important attributes were body mass index, mean age, 24 h urine vanillylmandelic acid/upper normal limit value, tumor size and enhanced computed tomography difference.

Conclusions

The improved random forest algorithm may be useful in predicting IHD risk factors in pheochromocytoma surgery. Data mining technologies are being increasingly applied in clinical and medical decision-making, and provide continually expanding support for the diagnosis, treatment, and prevention of various diseases.

Corruption in global health: the open secret

Corruption is embedded in health systems. Throughout my life-as a researcher, public health worker, and a Minister of Health-I have been able to see entrenched dishonesty and fraud. But despite being one of the most important barriers to implementing universal health coverage around the world, corruption is rarely openly discussed. In this Lecture, I outline the magnitude of the problem of corruption, how it started, and what is happening now. I also outline people's fears around the topic, what is needed to address corruption, and the responsibilities of the academic and research communities in all countries, irrespective of their level of economic development. Policy makers, researchers, and funders need to think about corruption as an important area of research in the same way we think about diseases. If we are really aiming to achieve the Sustainable Development Goals and ensure healthy lives for all, corruption in global health must no longer be an open secret.

Detecting medical prescriptions suspected of fraud using an unsupervised data mining algorithm

Nowadays, health insurance companies face various types of fraud, like phantom billing, up-coding, and identity theft. Detecting such frauds is thus of vital importance to reduce and eliminate corresponding financial losses. We used an unsupervised data mining algorithm and implemented an outlier detection model to assist the experts in detecting medical prescriptions suspected of fraud. The implementation ran medicine code, patients' sex, and patients' age variables through three successive screening steps. The proposed model is capable of detecting 25% to 100% of cases violating the standards for some medicines that are not supposed to be prescribed at the same time in one single prescription. This model can also detect medical prescriptions suspected of fraud with a sensitivity of 62.16%, specificity of 55.11%, and accuracy of 57.2%. This paper shows that data mining can help detecting potential fraud cases in medical prescriptions more quickly and accurately than by the manual inspection as well as reducing the number of medical prescriptions to be checked which will result in reducing investigators heavy workload. The results of the proposed model can also help policymakers to plan for fighting against fraudulent activities. Graphical Abstract Detecting Medical Prescriptions Suspected of Fraud Using an Unsupervised Data Mining Algorithm.

The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data

Healthcare in the United States is a critical aspect of most people's lives, particularly for the aging demographic. This rising elderly population continues to demand more cost-effective healthcare programs. Medicare is a vital program serving the needs of the elderly in the United States. The growing number of Medicare beneficiaries, along with the enormous volume of money in the healthcare industry, increases the appeal for, and risk of, fraud. In this paper, we focus on the detection of Medicare Part B provider fraud which involves fraudulent activities, such as patient abuse or neglect and billing for services not rendered, perpetrated by providers and other entities who have been excluded from participating in Federal healthcare programs. We discuss Part B data processing and describe a unique process for mapping fraud labels with known fraudulent providers. The labeled big dataset is highly imbalanced with a very limited number of fraud instances. In order to combat this class imbalance, we generate seven class distributions and assess the behavior and fraud detection performance of six different machine learning methods. Our results show that RF100 using a 90:10 class distribution is the best learner with a 0.87302 AUC. Moreover, learner behavior with the 50:50 balanced class distribution is similar to more imbalanced distributions which keep more of the original data. Based on the performance and significance testing results, we posit that retaining more of the majority class information leads to better Medicare Part B fraud detection performance over the balanced datasets across the majority of learners.

The sustainable development goals as a framework to combat health-sector corruption

Corruption is diverse in its forms and embedded in health systems worldwide. Health-sector corruption directly impedes progress towards universal health coverage by inhibiting people’s access to quality health services and to safe and effective medicines, and undermining systems for financial risk protection. Corruption is also a cross-cutting theme in the United Nations’ sustainable development goals (SDGs) which aim to improve population health, promote justice and strong institutions and advance sustainable human development. To address health-sector corruption, we need to identify how it happens, collect evidence on its impact and develop frameworks to assess the potential risks and put in place protective measures. We propose that the SDGs can be leveraged to develop a new approach to anti-corruption governance in the health sector. The aim will be to address coordination across the jurisdictions of different countries and foster partnerships among stakeholders to adopt coherent policies and anti-corruption best practices at all levels. Combating corruption requires a focused and invigorated political will, better advocacy and stronger institutions. There is no single solution to the problem. Nevertheless, a commitment to controlling corruption via the SDGs will better ensure the integrity of global health and human development now and beyond 2030.

Governance arrangements for health systems in low-income countries: an overview of systematic reviews

BACKGROUND

Governance arrangements include changes in rules or processes that determine authority and accountability for health policies, organisations, commercial products and health professionals, as well as the involvement of stakeholders in decision-making. Changes in governance arrangements can affect health and related goals in numerous ways, generally through changes in authority, accountability, openness, participation and coherence. A broad overview of the findings of systematic reviews can help policymakers, their technical support staff and other stakeholders to identify strategies for addressing problems and improving the governance of their health systems.

OBJECTIVES

To provide an overview of the available evidence from up-to-date systematic reviews about the effects of governance arrangements for health systems in low-income countries. Secondary objectives include identifying needs and priorities for future evaluations and systematic reviews on governance arrangements and informing refinements of the framework for governance arrangements outlined in the overview.

METHODS

We searched Health Systems Evidence in November 2010 and PDQ Evidence up to 17 December 2016 for systematic reviews. We did not apply any date, language or publication status limitations in the searches. We included well-conducted systematic reviews of studies that assessed the effects of governance arrangements on patient outcomes (health and health behaviours), the quality or utilisation of healthcare services, resource use (health expenditures, healthcare provider costs, out-of-pocket payments, cost-effectiveness), healthcare provider outcomes (such as sick leave), or social outcomes (such as poverty, employment) and that were published after April 2005. We excluded reviews with limitations that were important enough to compromise the reliability of the findings of the review. Two overview authors independently screened reviews, extracted data and assessed the certainty of evidence using GRADE. We prepared SUPPORT Summaries for eligible reviews, including key messages, 'Summary of findings' tables (using GRADE to assess the certainty of the evidence) and assessments of the relevance of findings to low-income countries.

MAIN RESULTS

We identified 7272 systematic reviews and included 21 of them in this overview (19 primary reviews and 2 supplementary reviews). We focus here on the results of the 19 primary reviews, one of which had important methodological limitations. The other 18 were reliable (with only minor limitations).We grouped the governance arrangements addressed in the reviews into five categories: authority and accountability for health policies (three reviews); authority and accountability for organisations (two reviews); authority and accountability for commercial products (three reviews); authority and accountability for health professionals (seven reviews); and stakeholder involvement (four reviews).Overall, we found desirable effects for the following interventions on at least one outcome, with moderate- or high-certainty evidence and no moderate- or high-certainty evidence of undesirable effects. Decision-making about what is covered by health insurance- Placing restrictions on the medicines reimbursed by health insurance systems probably decreases the use of and spending on these medicines (moderate-certainty evidence). Stakeholder participation in policy and organisational decisions- Participatory learning and action groups for women probably improve newborn survival (moderate-certainty evidence).- Consumer involvement in preparing patient information probably improves the quality of the information and patient knowledge (moderate-certainty evidence). Disclosing performance information to patients and the public- Disclosing performance data on hospital quality to the public probably encourages hospitals to implement quality improvement activities (moderate-certainty evidence).- Disclosing performance data on individual healthcare providers to the public probably leads people to select providers that have better quality ratings (moderate-certainty evidence).

AUTHORS' CONCLUSIONS

Investigators have evaluated a wide range of governance arrangements that are relevant for low-income countries using sound systematic review methods. These strategies have been targeted at different levels in health systems, and studies have assessed a range of outcomes. Moderate-certainty evidence shows desirable effects (with no undesirable effects) for some interventions. However, there are important gaps in the availability of systematic reviews and primary studies for the all of the main categories of governance arrangements.

Interventions to reduce corruption in the health sector

BACKGROUND

Corruption is the abuse or complicity in abuse, of public or private position, power or authority to benefit oneself, a group, an organisation or others close to oneself; where the benefits may be financial, material or non-material. It is wide-spread in the health sector and represents a major problem.

OBJECTIVES

Our primary objective was to systematically summarise empirical evidence of the effects of strategies to reduce corruption in the health sector. Our secondary objective was to describe the range of strategies that have been tried and to guide future evaluations of promising strategies for which there is insufficient evidence.

SEARCH METHODS

We searched 14 electronic databases up to January 2014, including: CENTRAL; MEDLINE; EMBASE; sociological, economic, political and other health databases; Human Resources Abstracts up to November 2010; Euroethics up to August 2015; and PubMed alerts from January 2014 to June 2016. We searched another 23 websites and online databases for grey literature up to August 2015, including the World Bank, the International Monetary Fund, the U4 Anti-Corruption Resource Centre, Transparency International, healthcare anti-fraud association websites and trial registries. We conducted citation searches in Science Citation Index and Google Scholar, and searched PubMed for related articles up to August 2015. We contacted corruption researchers in December 2015, and screened reference lists of articles up to May 2016.

SELECTION CRITERIA

For the primary analysis, we included randomised trials, non-randomised trials, interrupted time series studies and controlled before-after studies that evaluated the effects of an intervention to reduce corruption in the health sector. For the secondary analysis, we included case studies that clearly described an intervention to reduce corruption in the health sector, addressed either our primary or secondary objective, and stated the methods that the study authors used to collect and analyse data.

DATA COLLECTION AND ANALYSIS

One review author extracted data from the included studies and a second review author checked the extracted data against the reports of the included studies. We undertook a structured synthesis of the findings. We constructed a results table and 'Summaries of findings' tables. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess the certainty of the evidence.

MAIN RESULTS

No studies met the inclusion criteria of the primary analysis. We included nine studies that met the inclusion criteria for the secondary analysis.One study found that a package of interventions coordinated by the US Department of Health and Human Services and Department of Justice recovered a large amount of money and resulted in hundreds of new cases and convictions each year (high certainty of the evidence). Another study from the USA found that establishment of an independent agency to investigate and enforce efforts against overbilling might lead to a small reduction in overbilling, but the certainty of this evidence was very low. A third study from India suggested that the impacts of coordinated efforts to reduce corruption through increased detection and enforcement are dependent on continued political support and that they can be limited by a dysfunctional judicial system (very low certainty of the evidence).One study in South Korea and two in the USA evaluated increased efforts to investigate and punish corruption in clinics and hospitals without establishing an independent agency to coordinate these efforts. It is unclear whether these were effective because the evidence is of very low certainty.One study from Kyrgyzstan suggested that increased transparency and accountability for co-payments together with reduction of incentives for demanding informal payments may reduce informal payments (low certainty of the evidence).One study from Germany suggested that guidelines that prohibit hospital doctors from accepting any form of benefits from the pharmaceutical industry may improve doctors' attitudes about the influence of pharmaceutical companies on their choice of medicines (low certainty of the evidence).A study in the USA, evaluated the effects of introducing a law that required pharmaceutical companies to report the gifts they gave to healthcare workers. Another study in the USA evaluated the effects of a variety of internal control mechanisms used by community health centres to stop corruption. The effects of these strategies is unclear because the evidence was of very low certainty.

AUTHORS' CONCLUSIONS

There is a paucity of evidence regarding how best to reduce corruption. Promising interventions include improvements in the detection and punishment of corruption, especially efforts that are coordinated by an independent agency. Other promising interventions include guidelines that prohibit doctors from accepting benefits from the pharmaceutical industry, internal control practices in community health centres, and increased transparency and accountability for co-payments combined with reduced incentives for informal payments. The extent to which increased transparency alone reduces corruption is uncertain. There is a need to monitor and evaluate the impacts of all interventions to reduce corruption, including their potential adverse effects.

Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study

BACKGROUND

We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse.

METHODS

We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach.

RESULTS

Thirteen indicators were developed in total. Over half of the general physicians (54%) were 'suspects' of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data.

CONCLUSION

Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians.

Using data mining to detect health care fraud and abuse: a review of literature

Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to data mining of health care claims.