AI tackles payment integrity as health plans face $935B wake-up call

As healthcare costs continue to rise, U.S. health plans must be more vigilant in ensuring the integrity of their claims payment processes and systems. Payment integrity allows health plans to reduce unnecessary medical expenses, control costs, minimize provider abrasion and comply with regulations and contractual agreements. While AI offers a promising solution, many companies have yet to embrace its potential, even as annual losses reach a staggering $935 billion.

Health plans have long been trapped in a cycle of chasing improper payments after they’ve been made. This outdated approach leaves more than 30% of errors unrecovered and an ever-widening financial gap. The answer lies in shifting to prepay prevention powered by AI.

With regulatory changes on the horizon requiring stricter auditing and compliance standards, health plans face a pivotal moment. They must either embrace AI-driven payment integrity or risk falling behind. The situation draws parallels to Kodak’s missed opportunity as the pioneer of digital photography that didn’t capitalize on it and allowed competitors to dominate. Healthcare organizations that fail to adapt—and modernize their payment integrity strategies with AI—may similarly find themselves irrelevant in a rapidly evolving marketplace. They will face escalating challenges, while plans that seize this moment can lead the industry in both compliance and efficiency.

While traditional cost management strategies in healthcare often take years to show returns, payment integrity offers a quicker path to savings. Strategies such as utilization behavior changes and unit cost management have long been the focus of healthcare plans. While effective, these strategies are complex and expensive. They require multiple years to achieve return on investment.

In contrast, payment integrity presents an immediate opportunity for cost reduction as a low-hanging fruit for rapid savings and operational efficiency. The industry is taking notice, with private equity firms making substantial investments in payment integrity companies. KKR’s recent $5.5 billion stake in Cotiviti and New Mountain Capital’s $3 billion merger of major payment integrity vendors Apixio, Rawlings Group and Varis highlight the growing recognition of this field’s potential. These moves underscore the increasing recognition of payment integrity as a critical area for innovation and cost savings. While the potential to improve medical and administrative loss ratios is substantial, realizing these savings requires overcoming significant challenges.

Health plan payment integrity teams face numerous obstacles, including:

• Manual error identification: Many teams still rely on manual, time-consuming processes to identify errors. This limits the accuracy and efficiency of error detection, often leaving a substantial portion of inaccuracies unaddressed.
• Vendor overload: The growing number of payment integrity vendors adds complexity to health plans’ strategies. Many plans rely on multiple vendors but struggle to measure their true value and manage them effectively with a cohesive strategy.
• Complex rule management: Managing and updating the complex rules governing billing and reimbursement is a significant drain on resources, diverting focus from core tasks and slowing down the overall payment integrity process with constant shifts to adapt to changing regulations and coding guidelines.
• Inadequate data integration: The lack of seamless correlation between prepay and post-pay activities creates fragmentation and information gaps, making it difficult for teams to gain a comprehensive view of payment integrity performance and identify errors effectively.
• Reactive approach: Addressing errors only after payments have been made is less efficient and more costly. It allows errors to persist undetected for extended periods, leading to increased financial losses.

These challenges often result in fragmented efforts and missed opportunities for optimization.

The financial stakes are undeniable. Industry research estimates that improper payments may be accounting for a staggering $935 billion annually, with $165 billion attributed to fraud, waste and abuse and $265 billion to administrative waste. Further emphasizing the urgency, the federal government reported an estimated $236 billion in improper payments during fiscal year 2023, with more than $175 billion in overpayments. Medicare and Medicaid alone accounted for more than $100 billion of these improper payments, highlighting the dire need for a more efficient system.

The traditional post-pay approach to payment integrity is inherently reactive and inefficient. With up to 70% of errors going unrecovered, the emphasis must shift to preventing improper payments before they occur. Advanced statistical models and AI can detect anomalous claims prepayment, learning from post-pay errors to create more effective prepay edits.

This proactive approach not only enhances payment accuracy, but it also minimizes financial losses associated with post-pay recovery efforts. Preventing improper payments at the outset also reduces the administrative burden on payment integrity teams that can then focus their efforts on more strategic tasks rather than on costly and time-consuming recovery processes. In essence, paying things right the first time holds significant value and should be the cornerstone of any forward-thinking payment integrity strategy.

Innovative payment integrity solutions have the potential to achieve significant cost reductions and efficiency improvements. Effective waste reduction strategies could lead to a potential 25% decrease in the total cost of healthcare waste, translating to roughly $171 per-member per-year savings for health plans. Additionally, implementing machine learning technology in claims processing can potentially reduce fraudulent claims by up to 50% and improve processing times by 30%.

Here are steps health plans can take to get there.

By implementing advanced statistical models and machine learning algorithms, health plans can detect payment outliers and potential provider abuse through sophisticated data analysis. These models can uncover subtle patterns that manual processes might miss, such as providers who consistently bill at higher rates or submit claims with unusual coding combinations that deviate significantly from historical norms.

Impact: By proactively identifying these anomalies, health plans can address potential fraud and abuse before they escalate, leading to significant cost savings.

Leveraging AI to automate traditionally manual tasks can significantly enhance the accuracy and efficiency of payment integrity processes. AI can analyze complex medical records, reimbursement policies and claims data to provide accurate, real-time decision-making support.

Impact: AI-driven solutions can significantly enhance the accuracy and efficiency of payment integrity processes. This continuous learning capability allows health plans to stay ahead of emerging threats, ensuring high levels of payment accuracy and reducing administrative burdens. Natural language processing can be used to analyze medical records and coding for inconsistencies, further improving error detection capabilities.

Developing an intuitive, real-time payment integrity dashboard powered by AI can provide a comprehensive view of key metrics, enabling health plans to identify and address issues proactively. By using AI to perform trend analysis and highlight outliers, the system can automatically pinpoint areas of concern.

Impact: This dashboard would provide a comprehensive view of key payment integrity metrics, enabling health plans to identify and address issues proactively. These insights will allow health plan teams to pinpoint specific issues and execute corrective actions, such as adjusting prepayment edits or targeting specific providers for further review, to reduce financial losses and improve operational efficiency.

Key metrics to track include:

• Error-specific ROI: Measures the return on investment for identifying and rectifying specific types of errors
• Net value of payment integrity activities: Tracks the overall financial impact of payment integrity activities
• Cycle times of payment integrity reviews: Monitors the time taken to complete payment integrity reviews from identification to resolution
• Algorithm creation and update times: Measures the efficiency and speed at which new algorithms are developed and existing ones are updated

Fostering seamless integration between prepay and post-pay processes through AI-driven data sharing and automation can prevent repeat errors and enhance overall payment accuracy.

Impact: By using insights from post-pay audits to inform prepay edits, health plans can prevent repeat errors from occurring in the first place. This coordinated approach reduces the need for extensive post-pay audits and enhances overall payment accuracy, streamlining operations and cutting down administrative costs.

The payment integrity landscape is fraught with challenges, but it also presents immense opportunities for innovation. By adopting a phased approach that incorporates advanced statistical models, AI integration, real-time dashboards and prepay and post-pay coordination, health plans can modernize their payment integrity practices. These advancements not only promise substantial financial savings, but they also pave the way for more efficient, accurate and effective operations. As the healthcare industry continues to evolve, health plans that embrace AI-driven solutions will be well positioned to stay competitive and achieve sustainable success in payment integrity.

The journey toward a more efficient and effective system begins now, with a commitment to innovation, advanced analytics and AI-powered strategies for the future. Those who seize this moment can lead the health plan industry in both compliance and efficiency, while those who fail to adapt will confront escalating challenges in an increasingly complex healthcare landscape.