Enhance Health Plan Payment Integrity with Integrated AI Tools

Operating within the U.S. healthcare industry can be challenging. Decades of layered regulations, siloed processes, and a sprawling network of stakeholders have created an environment where payment errors and inefficiencies are inevitable. This intricate framework makes true payment integrity a constant battle for health plans, which are often forced into reactive, manual cycles of chasing down errors.

However, this ingrained complexity also presents a clear opportunity for transformation. With rising operating costs, shifting regulations, and growing member expectations, payers are turning to modern, integrated solutions to address these challenges more effectively.

Applying AI to Payment Integrity

For health plans, the biggest opportunity lies in catching errors before they become expensive problems. While most still rely on traditional methods, more than 90% see advanced technology and artificial intelligence (AI) as essential for payment accuracy. The leap in technology will help move the industry from a “pay and chase” model to a more proactive, preventive approach.

AI tools are built for this challenge. Instead of dedicating whole teams to cross-checking contracts, combing through fee schedules, and poring over regulations, AI-powered tools can translate provider contract terms into workflows, check billing codes, and flag inconsistencies. Using AI tools in health plan workflows can reduce processing times and increase payment accuracy—replacing weeks of manual review with speed, accuracy, and control.

Key Considerations before Adopting AI for Claims Processing

Putting AI to work for payment integrity means tackling more than just technology upgrades. Success starts with getting the basics right: clean, unified data and streamlined ingestion processes provide the foundation for reliable results. Modernization doesn’t stop at buying new software. It means consolidating systems, removing manual obstacles, and building a stable, cloud-based backbone. With these elements in place, AI can support the entire claims lifecycle and adapt to changing policies and increasing claim volumes. Without solid data governance and updated systems, even the most advanced AI will fall short.

When it comes to AI, ethical considerations also can’t be an afterthought. Algorithms need to be explainable and fair, especially when they weigh in on high-stakes or gray-area cases. Regular review and transparency keep systems in check. Mitigating bias and protecting data privacy are essential best practices. Solutions like HealthEdge Source™ are built with these safeguards in mind, providing frameworks that help teams avoid ethical missteps.

Strategic Implementation: Recommendations for Health Plans

1. Start small!

A successful approach starts with practical, incremental steps. Start small. Pilot specific use cases. Prove benefits with measurable outcomes, and build user confidence before expanding. Change management also matters. Position AI as a support tool, not a threat or added burden.

Ease of integration is critical. The best AI implementations fit into familiar workflows, remove manual tasks, and let teams focus on higher-value work. If new systems feel tacked on or disruptive, adoption will stall. Thoughtful integration streamlines operations and clears the way for broader benefits.

2. To build or not to build?

Deciding whether to build a solution in-house, buy off-the-shelf technology, or partner for integration really depends on what the organization is aiming to solve, how much control is needed, and the resources available.

Build when the challenge sits at the core of daily operations and demands a solution tailored to unique organizational needs. In these situations, customization, control, and adaptability are essential, especially when the health plan has proprietary intellectual property or data assets not available elsewhere.

For example, HealthEdge Source is building AI-driven enhancements to an existing Retroactive Change Manager tool. The technology will be able to identify and explain their root causes, such as policy changes or specific editing rules. This gives clear, actionable insights so that health plan teams can address errors while still maintaining control over valuable internal data and technology.

Buy or Partner when proven solutions already exist to meet the needs and objectives. Rather than reinventing the tools needed for detecting fraud, waste, and abuse, HealthEdge® partnered with Codoxo. The company’s AI-driven cost containment platform also helps accelerate the deployment of sophisticated analytics and provider education tools, reducing risk and expense while delivering measurable impact.

Integrate when the objective is to enhance an existing platform with advanced, specialized capabilities. HealthEdge Source DRG Guide, powered by Gynisus, exemplifies this approach by integrating complex Diagnosis Related Group (DRG) validation and guidance directly into payment integrity workflows. This integration provides precise, real-time decision support for DRG assignments, reducing costly errors in claims adjudication and improving payment accuracy. Integration lets health plans quickly leverage innovative approaches without the heavy lifting of full rebuilds.

To summarize, build if the need is highly specialized, in-house expertise is strong, and direct control over design is a must. Buy or partner if the problem is industry-wide, and speed to value is important. And integrate to enhance current systems with new tools that offer a performance boost without major disruption.

3. Safeguard operations

As AI extends reach and capability, security and compliance become even more important. Regardless of the model, strict controls are essential. Limit access, encrypt data in storage and in transit, and maintain detailed audit logs. Modern cloud platforms offer built-in security features, but effective governance frameworks are required to scale safely as data sets and automation expand. Strategic oversight ensures that growth in technology doesn’t introduce new vulnerabilities.

10 Key Questions Payers Need to Answer Before Adopting AI for Payment Integrity

1. What specific problem are we trying to solve with AI?
2. Do we have the right people and expertise to manage AI deployment?
3. Will this solution fit in smoothly with our current systems and processes?
4. How will we keep our data secure and meet compliance requirements?
5. What are the short-term and long-term costs of this investment?
6. How will success be measured, and what results do we expect?
7. Is there a clear plan for training staff and ensuring long-term adoption?
8. What risks could AI introduce, and how will we address them?
9. Are there reputable partners or vendors who can support our goals?
10. How will we keep the AI system up to date as needs and technology evolve?

The Future of AI in Payment Integrity

AI is starting to play a bigger role in healthcare—instead of staff having to enter data by hand, intelligent systems can now capture information from conversations, documents, and other communications in real-time. In addition, decision support tools can offer real-time administrative support, catching mistakes early and reducing repetitive administrative work. AI tools can run quietly in the background so staff can spend more time on challenges that need human insight, like handling complex decisions or building relationships with providers.

AI in payment integrity isn’t a flashy add-on. It’s a driver that allows health plan to finally break free from the old patterns of rework, error, and frustrations. Leading organizations are transitioning from fragmented, manual workflows to AI-driven, connected systems designed for accuracy and efficiency. Success will depend on making payment integrity systems smarter and easier to use as the industry evolves.

Is your health plan focused on streamlining claims management and enhancing payment integrity? Read our whitepaper, The Role of AI in Elevating Payment Accuracy.