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Healthcare Fraud DetectionFraudulent healthcare claims increase the burden to society. Therefore healthcare fraud detection is now becoming more and more important. Generally, healthcare frauds are not obvious and thus difficult to detect. The followings are typical examples of healthcare fraud techniques used by health care providers and patients;
Statistical healthcare fraud detection techniquesThe net effect of excessive fraudulent claims is excessive billing amounts, higher per-patient costs, excessive per-doctor patients, higher per-patient tests, and so on. This excess can be identified using special analytical tools. Provider statistics include;
Analytic Healthcare Fraud Detection MethodsHealthcare fraud detection involves account auditing and detective investigation. Careful account auditing can reveal suspicious providers and policy holders. Ideally, it is best to audit all claims one-by-one carefully. However, auditing all claims is not feasible by any practical means. Furthermore, it's very difficult to audit providers without concrete smoking clues. A practical approach is to develop short lists for scrutiny and perform auditing on providers and patients in the short lists. Various analytic techniques can be employed in developing audit short lists. Keep in mind that excessive fraudulent claims lead deviations in aggregate claims statistics. In addition, fraudulent claims often develop into patterns that can be detected using predictive models! Statistical listings of risky providersWhen abusive claims are repeated frequently, the consequent is higher provider statistics. Various provider statistics can be used to identify fraudulent claims. For instance, audit short-lists may include the followings;
It is noted that statistical analytic techniques can reveal excessive providers who might be outright stupid! But it will be difficult to identify modest level fraud activities. The subsequent section describes how sophisticated techniques can be applied. Data Mart for Healthcare Claims AuditIncorporating the techniques described in previous sections leads to intelligent audit and fraud detection environment. It is noted that healthcare fraud detection requires compilation of potentially huge data, involving complex computation and sorting operations. Our data mart platform for healthcare fraud detection is based on the following architecture. First, claim payment records are transformed and loaded into healthcare fraud data mart. Data is added into data mart, normally monthly or quarterly basis. Summary information is created for providers, doctors and policy holders. Expert systems engines are used to analyze, score and detect potentially risky providers and claims, Finally, auditors (and investigators) analyze data.
Rosella BI Platform for Healthcare Data MartHealthcare claims data marts can contain potentially huge amount of information. In addition, the complexity in detecting fraudulent claims makes fraud detection extremely challenging. Rosella BI Platform can help you with the following features;
1. Fast Rosella DBMS
2. Predictive modeling
3. Expert systems
4. Chart and report writing Rosella BI Platform provides all-in-one end-to-end platform for healthcare data mart solutions that you can build. It is available to both value-added solution vendors and in-house developers. Note that it comes with template implementation that you can extend. If you are interested, please write to us so we can discuss more about the technology. |
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