The truth is public healthcare is drowning in data but starving for insight. Agencies like CMS, the VA, and DHA are sitting on billions of claims, reports, and medical records, but that data isn’t organized, structured, or actually working for them. Meanwhile, fraud and waste bleed billions of federal healthcare programs every year.  

A tangle of outdated audits, slow-moving compliance checks, and AI models that make guesses instead of delivering certainty isn’t the answer. And neither is AI alone. Without structured, high-quality, and deeply connected data, even the most advanced AI models will reinforce existing blind spots rather than eliminating them. The challenge isn’t just detecting issues—it’s building proactive, AI-powered strategies that prevent waste before it happens. 

The $100 Billion Problem: Fraud, Waste & Improper Payments 

Improper payments aren’t an abstract issue. They’re a direct drain on taxpayer dollars, and they make it harder for agencies to focus on what matters—making sure people get the care they need, when they need it. Improper payments remain one of the biggest financial problems in federal healthcare programs. While the majority of claims are paid correctly, even a small percentage of errors leads to billions in financial loss. 

The Program Integrity Information Act (PIIA) of 2019 mandates that agencies assess, report, and reduce improper payments to stay within compliance. However, staying PIIA compliant is getting harder because –  

• Billing models are evolving faster than oversight systems can keep up. 

• Telehealth, remote care, and hospital-at-home programs have redrawn the map on payment accuracy. 

• The volume of transactions is too big for traditional audits to handle. 

Traditional methods—risk-based sampling, retrospective audits, and static fraud models—have failed to keep pace with these challenges. Agencies need an approach that integrates AI, predictive analytics, and full-spectrum data validation. Otherwise, mistakes slip through, fraud finds blind spots, and agencies are left playing defense instead of preventing waste from the start.  

AI Can Fix This—If It Has the Right Data 

AI-powered fraud detection is only transformative if trained on accurate, representative data that accounts for the full scope of improper payment risk. This is why precision-driven sampling models are redefining how agencies approach PIIA compliance and fraud prevention.  

What’s different about our approach? 

How We’re Rewriting the Playbook on Payment Integrity 

1. Precision Predictive Modeling Across Multiple Risk Factors 

Instead of relying on high-risk-only sampling, which distorts improper payment rates and leaves gaps in oversight, our model stratifies claims based on volume, cost, and clinical complexity. This ensures all risk levels are analyzed resulting in a more accurate national improper payment rate.  

1. Eliminates Loopholes by Analyzing the Full Claim Universe 

Conventional sampling models focus only on high-risk claims, which allows for the manipulation of data and detection avoidance. By contrast, our approach applies AI-driven prioritization to every claim category, ensuring fraud cannot be hidden by exploiting blind spots in the sampling process. 

1. Ensures Data Integrity with Sample Representativeness Testing 

Accurate oversight requires valid, representative data. Without it, agencies risk overstating or understating the true scope of improper payments. Our methodology guarantees that reported data accurately reflects reality, providing agencies with actionable intelligence to drive policy and process improvements.  

The results?  

• 336% ROI within three years on CMS’s national improper payment task order. 

• 30% improved compliance through proactive provider engagement and education. 

• First-in-the-industry precision in achieving OMB and Congressional mandates for improper payment rates.  

This isn’t just about catching errors, it’s about preventing them from happening and ensuring a resilient, AI-powered compliance strategy that stands up to evolving federal regulations.  

Value-Based Care Strategy 

Fraud prevention is just one piece of the puzzle. Value-based care models demand an entirely new data strategy, one that enables agencies to: 

• Track and analyze patient outcomes in real time to assess the actual impact of care.  

• Enhance provider collaboration by improving documentation quality and compliance. 

• Optimize financial resources by ensuring that payments are directly tied to quality, not volume. 

This requires data that is not just digitized, but structured, interoperable, and AI-ready. 

How We’re Turning Data into Something That Works 

Our AI-powered solutions are already empowering public sector agencies manage data, detect fraud, and drive better patient outcomes through – 

• Medical Record Retrieval That Doesn’t Take Months – 80% less manual input. Full access to over 1 million providers. 

• AI That Reads Medical Records Like a Fraud Investigator – Not just text recognition—real classification, tagging, and pattern analysis that connects the dots between billing data and clinical records. 

• A Health Data Exchange That Actually Moves Information – No more waiting for manual audits. Agencies get real-time access to structured, searchable case data. 

These are not just operational improvements. They represent a fundamental shift in how government agencies interact with and utilize healthcare data.  

What’s Next? A New Strategy 

As new policies emphasizing healthcare integrity, cost containment, and value-based reimbursement models take shape, agencies must be ready to align their AI and data strategies with government priorities including – 

• Greater financial oversight – ensuring that every public dollar is tied to measurable patient outcomes 

• Stronger fraud prevention measures – not just detecting improper payments but stopping them before they occur  

• More efficient AI-driven compliance solutions – allowing agencies to scale oversight without increasing administrative burden  

Healthcare doesn’t stand still. Neither should oversight. Agencies need tools that evolve in sync with police, reimbursement models, and care delivery trends.  

We’re Not Just a Tech Company. We’re the People Who Make Data Work. 

We’re not here to sell another AI solutions, analytics dashboard, or compliance checklist. We’re here to make public healthcare’s data work like it should. 

• We turn fragmented data into structured intelligence. 

• We build AI models that prevent financial waste before it happens. 

• We help agencies transform oversight from a reactive burden into a proactive advantage. 

Because at the end of the day, the greatest good in healthcare doesn’t come from more data, it comes from better decisions. 

Let’s make them together.