At Infosys, our focus on Healthcare is aimed at radical progress in affordability, wellness, and patient-centricity. We believe technology is a catalyst for game-changing healthcare solutions. In this blog, we discuss challenges, ideas, innovations, and solutions for the healthcare economy.

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Applications of Big Data in Healthcare - Part 4

The healthcare fraud is one of the dominant factors behind the rising cost of healthcare globally. The majority of the frauds are usually associated with upcoding of services and items, billing for services not rendered, unbundling, duplicate claims.  The providers tend to use incorrect procedure codes in the claim form they submit to payor for which claim payment is higher.
The major challenge the payors face is how to develop the required intelligence to detect the fraudulent claims during claims processing and avoid reimbursing higher amount to the providers.

The payors can leverage the already available patient data points, structured as well as unstructured, and feed them to Big Data algorithms to identify the fraudulent claims. Some of the data points which payors need to consider for a patient include

• Patient demographic details and susceptibility to any specific disease
• Patient disease history
• Past history of the medical procedures undergone for the disease
• Preventive care records

To cite an example if the claim has been submitted for fracture in the knee, the payor needs to know the requirement of blood sugar test which is administered along with other procedures. This could be a redundant test as the patient is not diabetic. By analyzing the unstructured data available through Big Data algorithms, the payors can acquire the additional intelligence to detect the fraudulent claims.

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