How an ACO Uncovered $4.5M in DME Fraud Using Population Health Analytics

Healthcare fraud costs the United States an estimated $100 billion annually, according to the National Health Care Anti-Fraud Association, with Medicare bearing a disproportionate burden of fraudulent claims.

For Accountable Care Organizations (ACOs) operating under value-based care contracts, fraudulent billing presents a particularly insidious threat that can undermine years of careful population health management and jeopardize participation in programs like the Medicare Shared Savings Program (MSSP).

How can ACOs use population health analytics to spot fraud before it erodes shared savings?  

In this case study, one Medicare ACO suspected something was off with its durable medical equipment (DME) spend but lacked the visibility to prove it. By using Koan Health’s population health analytics platform, the ACO rapidly identified unusual DME utilization patterns, traced them to a small group of suppliers, and uncovered suspicious billing worth approximately $4.5million. The investigation helped protect shared savings performance, support CMS enforcement efforts, and strengthen the ACO's ongoing fraud surveillance program.

Rising DME Spend With No Clear Explanation

In 2023, an ACO client noticed an alarming trend in its financial data. Durable Medical Equipment (DME) costs had inexplicably spiked. DME costs were growing faster than other categories, but traditional claims reviews were too manual and narrow to pinpoint the problem. Multiple suppliers appeared legitimate on the surface, and members were scattered across several states, making it difficult to see whether this was appropriate utilization, waste, or outright fraud. Without a scalable way to monitor utilization and spot outliers across its Medicare population, the ACO was at risk of losing millions in shared savings.

_{The numbers were staggering: DME costs increased from $130,000 to$4.5 million in a single year — a 3,360% increase. Yet utilization data showed no corresponding rise in patient need or service volume.}

Using Population Health Analytics to Detect Fraud

The ACO partnered with Koan Health to use its population health analytics platform to analyze claims data across the entire attributed Medicare population. The analytics team built targeted views by supplier, member, diagnosis, and DME type, then layered in risk scores and utilization benchmarks to look for cost anomalies. This allowed them to quickly surface DME suppliers and members whose patterns did not align with typical clinical or utilization expectations, signaling potential fraud.

Key Analytics Steps

Step 1: Trend Analysis — Initial population health analytics flagged the cost anomaly, showing the dramatic spike in DME expenditures without corresponding utilization increases.

Step 2: Claims Deep Dive — The ACO exported comprehensive claims data spanning 2022–2023, creating a complete picture of DME billing patterns across their patient population.

Step 3: Multi-Layer Analysis — Data was segmented by procedure codes, billing providers, and rendering providers, revealing suspicious patterns invisible in aggregate reporting.

Step 4: Pattern Recognition — Two specific procedure codes emerged as outliers, with costs that defied logical explanation when compared to historical norms and patient utilization.

_{The Turning Point: Billing-Provider Analysis. Adding a billing-provider view surfaced 17 suspicious billing providers responsible for the fraudulent DME claims—a clear pattern of coordinated healthcare fraud.}

What the Analytics Revealed

Within weeks, the ACO’s analytics team identified a pattern of recurring claims for expensive DME tied to a concentrated set of supplier and members. Many claims appeared inconsistent with documented diagnoses, care pathways, or expected utilization patterns. When the results were escalated and investigated further, the ACO confirmed a DME fraud scheme worth approximately$4.5 million in billed claims.

System-Wide Impact and Recovery

Armed with concrete data and irrefutable evidence, the ACO opened a formal case with the Centers for Medicare & Medicaid Services(CMS) and shared their findings with the National Association of ACOs (NAACOS)community.

The investigation’s impact extended far beyond a single organization. CMS confirmed that the identified providers were perpetrating DME fraud across multiple ACOs nationwide. The agency took swift action, removing all expenditures for the fraudulent procedure codes across all ACOs participating in the MSSP for performance year 2023.

Financial Protection and Stronger Fraud Surveillance

The ACO worked with its legal team, CMS, and other agencies to stop payments to the fraudulent suppliers and support further investigation. By catching the scheme early, the ACO protected millions in potential shared‑savings losses and reinforced its reputation as a responsible steward of Medicare funds.

The success didn’t end with fraud detection. The ACO implemented quarterly reviews using Koan Health’s analytics, establishing a proactive monitoring system that continues to protect their financial health. The organization also used insights from this case to tighten its ongoing fraud monitoring program, embedding DME anomaly detection into its regular population health analytics workflows.

What can other Medicare Shared Savings Program ACOs learn from this DME fraud case?

Unexpected cost increases are not always the result of changes in patient needs or provider behavior. In some cases, they may indicate fraud, waste, abuse, or operational issues that require immediate attention.

Organizations can improve their ability to identify and respond to anomalies by:

• Monitoring utilization and expendituresthroughout the performance year
• Investigating sudden cost increases that lack aclinical explanation
• Conducting provider-level analysis when unusual trends emerge
• Establishing recurring, analytics-driven reviews of high-cost service categories such as DME
• Using analytics to quantify financial risk and prioritize investigations

For ACOs across the United States operating undervalue-based care arrangements, early detection can help protect both financial performance and program integrity.

How Koan Health Helps ACOs Detect Fraud, Waste, and Abuse

Koan Health’s population health analytics platform gives ACOs and health systems a scalable way to monitor utilization, identify cost anomalies, and support fraud, waste, and abuse (FWA) programs across Medicare, Medicaid, and commercial populations. Interactive dashboards, flexible queries, and drill‑down capabilities help teams find suspicious supplier and member patterns far earlier than manual review alone. When combined with care management and network optimization insights, this fraud‑detection capability becomes part of a broader strategy to protect financial performance while improving patient outcomes.

Ready to see how population health analytics can help identify fraud, uncover cost anomalies, and protect shared savings performance? Contact Koan Health to schedule a demo.