Every PM and EHR vendor is currently being asked about their AI story. By customers, by boards, by competitors, by prospects. On denial prevention specifically, the pressure to add AI-driven features is intense and continuous.
The pressure is not wrong. AI in RCM is a real, growing capability that meaningfully improves denial prevention when deployed well. But the space is also crowded with tools that overpromise, underdeliver, or fail in ways that hurt customer relationships when the failure surfaces. Recent industry data shows 63% of RCM leaders now consider AI explainability and auditability mandatory. That number reflects how quickly the market has moved from AI curiosity to AI due diligence.
Before committing to an AI denial prevention roadmap, whether building in-house or partnering, five questions should shape the answer.
Question 1: What Is the Actual Customer Outcome, Not the Demo?
Every AI vendor demo shows impressive results on curated data. The relevant question is what happens in production, on your customer’s actual claim volume, across the payer mix they actually see.
Ask specifically: what is the measured denial rate reduction across a representative production cohort of at least a few months? What is the first-pass acceptance improvement? What is the rework labor reduction? If the vendor cannot answer with specific numbers from real customers, the technology may not be production-ready for the outcomes you are being asked to deliver.
Question 2: Is the AI Explainable and Appealable?
A denial you cannot explain is a denial you cannot appeal. If an AI tool flags a claim, denies it, or corrects it, your customer’s billing team needs to understand why. When the payer questions the correction, when an auditor asks about the decision, when a physician wants to know why their claim was reworked, someone has to be able to reconstruct the reasoning.
Explainability is not just a product feature. It is a foundational requirement for healthcare AI. AI tools that operate as opaque black boxes create liability exposure that most PM vendors have not fully priced in when they build them into their platform.
Question 3: What Is the AI’s Failure Mode?
Every AI system fails eventually. The question is not whether, but how, and what happens when it does. Three specific failure modes deserve attention.
- Silent failure. The AI makes a wrong decision and the platform keeps operating as if it were right. Denial rates climb, customer satisfaction drops, and the root cause is invisible until someone traces it.
- Cascade failure. The AI fails in a way that affects downstream systems, corrupting data, triggering wrong actions, or breaking workflows in ways that require manual reconciliation across many claims.
- Adversarial failure. The AI is manipulated by payer-side systems that identify and exploit patterns in its behavior. This is more common than the industry acknowledges, particularly as payers deploy their own AI in adjudication.
Ask specifically: what is the vendor’s failure detection posture? What are the alerts and escalation paths? How quickly can the platform revert to human review if the AI is producing wrong results?
Question 4: What Is the Total Cost of Ownership After Implementation?
AI implementation cost is often front-loaded, but ongoing operational cost tends to grow over time as the system requires retraining, monitoring, and adjustment. A few specific cost components to price in.
- Data pipeline maintenance. AI models require continuous data flow, and the pipelines that feed them require ongoing engineering support.
- Retraining and adjustment. Payer behavior changes, and the AI has to change with it. Retraining is not free, in either compute cost or engineering time.
- Monitoring and quality assurance. AI outputs need continuous oversight to catch drift, bias, or failure modes before they affect customer outcomes.
- Support and incident response. AI-driven decisions require a support model that can explain, adjust, and roll back when needed.
The fully loaded cost of maintaining AI features in production is often 2 to 3 times the initial build cost, spread over the following years. Pricing that in during the product decision matters.
Question 5: Build In-House or Partner?
For most PM and EHR vendors, the honest answer on AI denial prevention specifically is that partnering is more sensible than building, at least for the first generation of the capability. A few reasons.
- The core algorithmic work has been done. Vendors with 20 or more years of clearinghouse operations have the training data (transaction volume, payer response patterns, denial outcomes) that in-house AI teams do not have access to at meaningful scale.
- The regulatory posture is complex. Operating AI on protected health information at scale requires HIPAA and security investments most PM vendors have not budgeted for.
- The failure modes are consequential. AI on the denial-prevention layer directly affects customer revenue. Partnering with an established provider transfers the failure-mode risk to a party with deeper operational maturity.
- The reversibility calculation favors partnering. A partnership can be reevaluated at renewal. An in-house AI investment is harder to unwind.
The exception is when AI is core to the platform’s differentiation. If your product’s fundamental competitive story is the AI experience, build it. If AI is a supporting capability for denial prevention, partnering usually wins.
How Harris Secure Connect Approaches AI
Harris Secure Connect operates as the clearinghouse layer that AI tools depend on, and offers Claims Correct as our own AI-driven scrubbing capability. Claims Correct is built on 26 years of payer-specific transaction data, designed with explainability and audit trail as foundational requirements, and integrated cleanly with the connectivity infrastructure PM and EHR vendors are already partnering with us on.
If your platform’s AI roadmap has a denial prevention decision in the next 12 months, our team is happy to walk through what a partnership approach with Claims Correct would look like for your specific customer base.