Medicaid program integrity, in simple terms, is a program that ensures that the funds allocated for Medicaid are used appropriately, that patients receive the care they deserve and that resources aren’t mishandled.

But consider this staggering fact: In 2020, the Centers for Medicare and Medicaid Services (CMS) reported that Medicaid program integrity efforts recovered over $4.1 billion in improper payments.

Yet, program integrity experts know even more goes unidentified and uncollected. Advanced analytics has the power to save billions, improve patient care and safeguard the integrity of vital programs. Therefore, states must adopt advanced analytics with artificial intelligence, machine learning, and work process automation to bolster payment integrity and better manage program risks.

Keep reading as I put this problem into perspective and explain why Medicaid agencies should use analytics to bridge the gap.

Uncovering inefficiencies in my own experiences

Read more stories about how analytics is modernizing Medicaid and Medicare

My experiences in Medicaid showed me how weak payment integrity and manual processes wasted money the program desperately needed for patient care.

For example, skilled nursing facilities were overpaid $15-20 million, with manual audits initiated five years after the overpayments occurred. This resulted in a substantial outstanding sum, akin to an interest-free eight-year loan when considering trailing administrative appeals and hearings that extended the repayment period.

The audit process for skilled nursing facilities underwent a significant transformation using advanced analytics. The five-year delay was reduced to just one year, administrative appeals nearly disappeared, outstanding overpayments dropped from $100 million to less than $10 million and recovery time for final audit dollars shrank from two years to only six months.

Furthermore, using analytics to automate a substantial portion of the audit process reduced the number of required auditors from 42 to just eight full-time positions. These auditors were then deployed for other provider-based risks and to expand audit coverage of Medicaid Managed Care, which represented over $10 billion in expenditures but had gone nearly untouched as antiquated manual processes devoured audit staff resources.

The hidden oversight gap in Medicaid

Medicaid Managed Care and value-based payments only make risk management in Medicaid more difficult. A shift to managed care doesn’t eliminate fraud, waste, or abuse. It only shifts such risk management to managed care entities (MCEs). Enormous claim volume, complex programs, and payment models can mask payment integrity issues. Likewise, states often have little insight into how the MCEs manage program integrity risks.

State Medicaid agencies often fail to account for payment integrity risks properly. States may withhold one to two percent of managed care capitated payments to attempt to account for risks related to fraud, waste, and abuse (FWA). However, CMS, the Health and Human Services Office of Inspector General (HHS-OIG), and the National Healthcare Anti-Fraud Association (NHCAA) collectively estimate that FWA conservatively costs healthcare payers a minimum of 10% of total spending. The lost difference is compounded yearly as new managed care payment rates are calculated off an ever-expanding base.

The power of a unified analytics solution for Medicaid

To bridge this oversight gap and fortify payment integrity, states need a unified analytics solution that uses AI and machine learning. Predictive analytics and AI can identify suspect providers and anomalous billing behaviors and patterns. Automating payment integrity analysis and alerting maximizes Medicaid staff capabilities by using the technology to get more done.  World-class data visualizations with easy-to-use point-and-click solutions provide no-code/low-code solutions to democratize data analysis across organizations.

In health care, AI and machine learning, health care payers are already making a profound impact. Computer vision and text analytics automate medical record reviews, prior authorizations, and program integrity functions.

Augmenting people's work with advanced analytics helps state agencies and other organizations get more done with limited staffing. This is especially important in both government and health care. People are retiring, and recruitment is challenging. Advanced analytics with artificial intelligence allow you to get more done with less.

Indeed, program integrity is not merely about combating health care fraud. It serves as a key risk management function for controlling costs in Medicaid. It’s not just a luxury; it’s a necessity for maintaining both Medicaid’s payment integrity and the overall resilience of the program.


Learn more about how SAS can help to modernize your Medicaid systems.


About Author

John Maynard

Principal Industry Consultant

John is a Principal Industry Consultant at SAS, and former State Medicaid Program Integrity Director, with nearly 25 years in state government. As part of the SAS Global Fraud practice, he supports private and federal/state public healthcare and other government social benefit programs worldwide. John has a BA in Business and holds CPA, CFE, and AHFI designations.

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