SUPPORT Summaries
Evidence of the effects of health system interventions
for low- and middle-income countries
Evidence of the effects of health system interventions
for low- and middle-income countries
December, 2013 - SUPPORT Summary of a systematic review | print this article | download PDF
Health care fraud is a serious problem for health systems considering the enormous volume of money spent on health care. There are increasing national, regional and international fraud control initiatives. Political, legislative, and administrative interventions are being intendend to combat it.
Key messages
People deciding to combat health care fraud and abuse.
Rashidian A, Joudaki H, Vian T. No evidence of the effect of the interventions to combat health care fraud and abuse: a systematic review of literature. PloS one. 2012;7(8):e41988.
A summary of studies addressing a clearly formulated question that uses systematic and explicit methods to identify, select, and critically appraise the relevant research, and to collect and analyse data from the included studies.
SUPPORT – was an international project to support the use of policy relevant reviews and trials to inform decisions about maternal and child health in low- and middle-income countries, funded by the European Commission (FP6) and the Canadian Institutes of Health Research.
Fraud has been defined as an ‘‘intentional deception or misrepresentation made by a person or an entity, with the knowledge that the deception could result in some kinds of unauthorized benefits to that person or entity’’. It has been estimated that 3-10% of health care spending is lost to health care fraud and abuse.The degree of intent by the individual or entity is often the determining factor in distinguishing between fraud and abuse. The term ‘‘abuse’’ may be used to describe problematic behavior which is not clearly against the law or where certain elements of the fraud definition (such as knowing deception) are missing (e.g. providing unnecessary care).Based on who conducts the fraud, it can be classifed as provider fraud (e.g. by physicians, dentists, hospitals, diagnostic services, pharmaceutical or medical device manufacturers), consumer fraud (by patient or insured), and insurer or payer fraud. Interventions to combat fraud can be categorised into three categories: prevention(e.g creating an antifraud culture and improving controls), detection (e.g. by medical claims’ reviews), and response (e.g. administrative and legal actions).
| Review objectives: To assess the effectiveness of the interventions to combat health care fraud and abuse. | ||
| Type of | What the review authors searched for | What the review authors found |
|---|---|---|
| Study designs & interventions | Interventional studies with or without a concurrent control group assessing any intervention to combat health care fraud (including prevention, detection, and response interventions) |
Four studies were included, 3 assessing detection actions and 2 response actions. The study designs were: longitudinal with concurrent control group (1), da-ta mining (2) and before after study (1). |
| Participants | Providers, patients or insured people fraud, insurer (third party payer). |
Taiwan’s National Health Insurance, Medicare and Medicaid (in USA). |
| Settings | Public and non public health sector. |
Taiwan (2) and USA (2). |
| Outcomes | Prevention, detection, and response related outcomes. |
Detection of fraudulent claims, amount of antifraud expenditure, occurrence of health care fraud and abuse, fraudulent activities in diagnostic laboratories. |
| Date of most recent search: 2010 | ||
Rashidian A, Joudaki H, Vian T. No evidence of the effect of the interventions to combat health care fraud and abuse: a systematic review of literature. PloS one. 2012;7(8):e41988.
High: This research provides a very good indication of the likely effect. The likelihood that the effect will be substantially different† is low.
Moderate: This research provides a good indication of the likely effect. The likelihood that effect will be substantially different† is moderate.
Low: This research provides some indication of the likely effect. Howevver, the likelihood that it will be substantially different† is high.
Very low: This research does not provide a reliable indication of the likely effect. The likelihood that the effect will be substantially different† is very high.
* This is sometimes referred to as 'quality of evidence' or 'confidence in the estimate'.
† Substantially different = a large enough difference that it might affect a decision.
See last page for more information.
Four studies were included: none about prevention, three about detection and two about response interventions (one study assessed both detection and response interventions).
1) Detection interventions
One study found that the three data mining algorithms (logistic regression, neural networks, and classification trees) were accurate in detecting previously known fraudulent claims. Another data mining study, that used deviations from clinical pathways as an indication of potential fraud or abuse, showed sensitivity and specificity rates of detection of 64 and 67%, respectively. A third study estimated antifraud activities in different states, and then linked that with fraudulent activities in the states. This study estimated the amount of antifraud expenditure at the state level per hospital and per patient as a measure of intensity of antifraud interventions in the state. The study covered issues relevant to the ‘detection’, and ‘response’ categories of interventions to combat health care fraud.
2) Response interventions
The main conclusion of the study mentioned above was that more intense antifraud activity results in generally less occurrence of health care fraud and abuse. Increased antifraud expenditure was linked to decline in billing by certain types of patients and hospitals without adverse consequences for patients’ health outcomes.
Another study (before after design) assessed the effects of a legal intervention on fraud in diagnostic laboratories. It found that more lenient sanctions, during a four years period, correlated with a gradual increase in the percentage of fraudulent activities in the same period.
Interventions to combat health care fraud and abuse |
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| People: Medicare beneficiaries. Settings: Medicare. Intervention: Antifraud activity. Comparison: No intervention. |
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| Outcomes | Impact | Certainty of the evidence (GRADE) |
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| Inpatient expenditures |
In models that measure enforcement on a per hospital basis, a 1% increase in enforcement leads to a 0.92% decreasein the acute inpatient expenditures of a young male patient without a prior year’s hospital admission, relative to the expenditures of an older, more infirm female patient. A 1% increase in enforcement leadsto 5.55% relative decline in nonacute inpatient expenditures for the same population. |
Very low |
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| GRADE: GRADE Working Group grades of evidence (see above and last page) |
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| Findings | Interpretation* |
|---|---|
| APPLICABILITY | |
| There is no study from low income countries and only two from middle income countries. |
Low income countries might be more prone and vulnerable to health care fraud and its consequences.
-The availability of human and technical resources to combat fraud -The acceptability and costs of the interventions |
| EQUITY | |
| There was no information in the included studies regarding the differential effects of the interventions on resource disadvantaged populations. |
Resources needed to combat health care fraud are less available in disadvantaged settings.
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| ECONOMIC CONSIDERATIONS | |
| One study estimated the amount of antifraud expenditure, but the systematic review did not address any other economic considerations. |
Scaling up interventions to combat health care fraud and abuse will require important resources, that should to be considered.
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| MONITORING & EVALUATION | |
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There is a lack of research assessing the effectiveness of intervention to combating health care fraud, and the limited studies available did not use robust research methods.
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Understanding the nature and the scale of fraud is a prerequisite in any attempts to combat health care fraud and to prioritize efforts.
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*Judgements made by the authors of this summary, not necessarily those of the review authors, based on the findings of the review and consultation with researchers and policymakers in low income countries. For additional details about how these judgements were made see:www.supportsummaries.org/methods |
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Li J, Huang KY, Jin J, Shi J (2008) A survey on statistical methods for health care fraud detection. Healthc Manag Sci 11: 275–87.
Gee J, Button M, Brooks G, Vincke P. The financial cost of healthcare fraud [Internet]. Portsmouth: University of Portsmouth, MacIntyre Hudson, MiltonKeynes. Available: http://eprints.port.ac.uk/3987/1/The-Financial-Cost-of-Healthcare-Fraud-Final-(2).pdf. Accessed 2013 December 28.
Rashidian A, Joudaki H (2010) Assessing medical misconduct and complaints in Iran health system: a systematic review of literature. Sci J Forensic Med 15: 234–43. [In Persian]
Sokol L, Garcia B, Rodriguez J, West M, Johnson K. Using data mining to find fraud in HCFA health care claims. Topics in health information management. 2001;22(1):1-13.
Williams B. The ethical constraints of fraud & abuse regulations. Tennessee medicine: journal of the Tennessee Medical Association. 2002;95(2):53-56.
Agustín Ciapponi, Instituto de Efectividad Clínica y Sanitaria, Buenos Aires, Argentina.
None declared. For details, see: www.supportsummaries.org/coi
This summary has been peer reviewed by: [Name, Country;].
Rashidian A, Joudaki H, Vian T. No evidence of the effect of the interventions to combat health care fraud and abuse: a systematic review of literature. PloS one. 2012;7(8):e41988.
Ciapponi A, Do interventions reduce health care fraud and abuse? A SUPPORT Summary of a systematic review. December 2013. www.supportsummaries.org
All Summaries:
evidence informed health policy, evidence based, systematic review, health systems research, health care, low and middle income countries, developing countries, primary health care.
health care fraud, health care abuse, prevention, detection, response
