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How Can Data Analytics Save Billions of Dollars for the Healthcare Industry?

Oct 6, 2017

Healthcare insurance is a boon to a large segment of people who would otherwise not be able to afford healthcare services due to its high cost. The process of claiming insurance benefits has also been pretty straightforward to improve delivery of services. However, people find loopholes and indulge in fraudulent activities to fetch themselves some financial benefits. Fraudulent activities in the healthcare industry have cost insurance companies billions of dollars. As a result, companies are leveraging massive amounts of data that goes through healthcare systems to combat fraud.

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Here is how analytics can help the healthcare industry to detect fraud and tackle them effectively:

Identity Analytics

A majority of the fraud cases in the healthcare industry happens because of identity theft. Hackers gain unauthorized access to healthcare records and obtain healthcare services under the victim’s identity. With the help of modeling and real-time analytics, suspicious activity or inconsistent information about applicants can be detected.

Claims Analytics

Traditional fraud detection systems that identify outliers are usually deployed after payment is made. As a result, the players in the healthcare industry are leveraging the power of analytics to develop predictive models, consisting of rule-based screens and edits that identify possible cases of improper payments by a scoring method. This method is also effective enough to work the other way around in an attempt to pay legitimate claims as quickly and effectively as possible.

To know more about how your organization can leverage data analytics to detect and prevent fraudulent activities, request for more information.

Social Network Analytics

A vast majority of the fraud that happens in the healthcare industry is the result of organized, collusive, and sophisticated activities among service providers, healthcare providers, and patients. Manual detection of such activities is nearly impossible, but by analyzing data from social network analysis identifying relationships, links and hidden patterns of information sharing and interactions can help detect fraud, waste, and abuse.

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