The US spent $2.7 trillion on healthcare in 2013, more than 17 percent of GDP. According to a Commonwealth Fund study, despite the high spending on health care, Americans have poor population health compared to other high-income countries (indicated by low life expectancy at birth, chronic conditions, obesity rates, infant mortality, etc.). One of the reasons is fraud, waste and abuse, which diverts significant resources away from necessary care. The US healthcare system loses more than $200 billion every year in fraud, waste and abuse, nearly 10 percent, of annual healthcare spending.
The Government Accountability Office (GAO) has deemed Medicaid to be highly vulnerable to fraud, waste and abuse. This could be due to services or drugs not covered, not medically necessary, provided but incorrectly billed, or billed for but never provided.
Unsupervised learning methods identified a recovery opportunity of $45M from overpayments, and established cost containment processes
A leading multi-billion dollar healthcare payer, with a growing government business supporting Medicare and Medicaid, wanted to identify claims overpayments and opportunities to better contain costs.
Claims may be overpaid due to fraud, waste or abuse by providers, pharmacies, members or payers’ internal claims processing errors. With variations in member demographics, health conditions, condition severities and treatment patterns, it is critical to compare claims with other similar claims when looking for irregularities or aberrancies.
An unsupervised learning framework was developed to identify overpayments
The Payer was able to identify more than $45 million dollars in recoverable overpaid claims by:
Developing business rules leading to systemic changes to hold possible overpayments
Identifying 20 times more claims with dosage and pricing aberrancies
Optimizing the recovery process through the recommended prioritization framework
More than 75% of the identified claim overpayments were due to higher dosage or units billed than actually serviced
Nearly 25% of the identified overpaid claims were systemic inconsistencies
More than $45M of recoverable opportunity identified from overpayments
Identified 20x more claims with dosage and pricing aberrancies
Faster time to insight to adjust actions
Efficiency improvement going beyond flagging overpaid claims to also recommend reasons to foragers
Scalable approach to quickly expand lines of business and procedures