Healthcare insurers detect fraud and abuse with data mining and analytics

Data mining and analytics in healthcare

Jan 15 2016

Healthcare

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The healthcare insurers have always struggled to protect their business interests from the greedy eyes of the fraudsters, be it health service providers or healthcare policy holders.For decades, the traditional method for screening questionable healthcare billing and service requests were manually checked through the available information system one by one. In the real healthcare industry, the volume of questionable healthcare billing and service request is so high that the entire fraud detection process per case cost the healthcare industry seventy-five Moreover, due to excessive workload and inexperienced manpower, the chances of misjudging were very high—resulting in wasted resources and improper settlement of claims.

Although healthcare frauds have increased manifold in the past decade, the misadventures in the healthcare industry are now detected by advanced technology like fraud analytics and data mining to save time and cost. The advanced fraud analytics display certain patterns in healthcare fraud, which include:

  • Billing for bogus services
  • Requesting unnecessary diagnostic tests and treatment procedures; also expensive tests and equipments
  • Multiple-billing for services
  • Separate billing for grouped laboratory tests to obtain higher reimbursements
  • Un-uniform charging by different service providers for same service
  • Conducting unrelated medical procedures and services.
  • Policy holders wasting money on medical tourism for treatment available locally
  • Policy holders allowing others to use their health cards

The above frauds frequently lead to excess billing amounts and exorbitantly high patient care costs.

Healthcare fraud detection with analytics

In the technological world, healthcare fraud detection is accomplished with automated account auditing and fraud investigation. As auditing millions of claims one by one is not feasible, automated auditing can help reveal suspicious health providers and health policy holders. Thus data mining and advanced analytics have been used to generate sample audit cases for careful scrutiny. Also, predictive models have been utilized to detect patterns in fraudulent claims. You can also review healthcare analytics to find out how data technology tackles healthcare data management issues.

 The use of data mining in health data discovery

The big advantage of data mining is that it allows health data experts to compare vast amounts of related healthcare data and discover new “facts, rules, or relations” from the data. Data mining enables the formation of data models that may be used later for further data discovery and pattern matching. This is how the healthcare industry has exploited data mining techniques to gain insights into fraudulent healthcare claims:

  • Data mining has enabled health data discovery and extraction of data models
  • Data mining has also enabled pattern matching and the use of models for presenting insights
  • Data mining supports knowledge presentation through visual techniques.

Thus health insurance providers are now equipped with powerful technology to shortlist questionable healthcare providers and policy holders, detect fraudulent service requests and fraudulent claims, and finally plug all the leaks to build a more profitable business. The service page data management solutions provides a glimpse into the world of data management for business solutions.



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