Healthcare Payers in the US lose around $60 billion every year due to fraud, waste or abuse of the services they provide. Though many products and services exist to prevent these occurrences, text mining can be used effectively to overcome this billion dollar problem, HCL tells how.

1. How Preventing FWA can save Billions for Healthcare Insurance
Providers
Text Analytics Solutions for Fraud, Waste and Abuse

2. Most Common FWA
Modes in Healthcare
Industry
• Billing for medical services or equipment
that weren’t ordered or provided
• Billing for relatively costlier services or
procedures than rendered
• Patient with or without a physical ailment
obtains multiple prescriptions for
narcotics or controlled substances from
various physicians
• Overutilization of services
• Payment for services that fail to meet
professionally recognized standards of
care

3. Shortcomings of Conventional
Method of Detecting FWA
• Rudimentary and ineffective enterprise wide
applications to detect FWAs
• Manual investigation of claims
• 40-50% flags raised are false alarms
• Only 5-10% percent of FWA instances are
successfully rejected

4. Text Mining with Predictive Modelling – A
HCL Solution
CRISP-DM (Cross-Industry Standard Process of Data Mining) methodology

5. Questions
• Why stopping instances of FWAs is difficult for healthcare insurance providers?
• How does text mining can help in curbing FWA of insurance services?
• How much healthcare insurance industry loses monetarily due to FWAs?
• What is Predictive Modeling?
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