US Health Care is going through a major upheaval with the passage of Patient Protection and Affordable Care Act (PPACA), also known as Health Care reforms. PPACA includes several reforms like guaranteed health insurance, increased MLR limits, setting up of Health Insurance Exchanges, adoption of ICD-10 standards etc. These reforms will lead health plans to look for new initiatives and solutions to manage Health Care Fraud, Waste and Abuse (FWA) in post-reform scenario.

With PPACA, there is a potential of 30 million new customers entering the Health Insurance market which will result in high claim volume, creating a needle-in-a-haystack scenario for identifying fraud. Because of the sheer number of claims processed, erroneous claims can go undetected, easily bypassing rule edits in most claims adjudication systems.

Beginning in 2014, consumers including individuals and small businesses will be able purchase health insurance from the new health insurance exchanges established by the Act. This will drive up the customer acquisition cost of payers. The new Medical Loss Ratio (MLR) limits will also force payers to cut an average of 15-20% of administrative costs to maintain margins. As a result, health plans would look at reducing the amount of money lost in fraud, waste and abuse.

Health reforms also mandate replacing International Classification of Diseases-9 (ICD-9) with ICD-10 code sets. This transition from ICD-9 to ICD-10 code sets poses immediate challenges for Payer’ Special Investigation Units (SIU’s) due to confusions arising from code set mapping errors and increased risk of false positives from standard FWA analytics engines

Today, most of the health plans are focusing on deploying product solutions to manage fraud, waste and abuse cases. These analytics solutions rely only on the output of Rule Engine and/or Scoring Engine to identify suspected claims which leads to high false positive rates. Need of the hour is a shift in approach from only product centric model to a more services based model where services like claim validation, recovery, post-claim analytics, rules and model enhancement etc would complement FWA Analytic Engines to deliver more efficient outcomes.