Human-in-the-Loop AI: The Key to Accuracy and Trust in Payment Integrity

Artificial intelligence isn’t just reshaping healthcare; it’s rewriting the rules.

In payment integrity, the stakes couldn’t be higher. AI has the potential to process millions of claims in seconds, expose hidden fraud patterns, and eliminate labor-intensive administrative workflows. The potential is enormous, but so are the risks, and ignoring them could be catastrophic. 

Recent headlines illustrate how quickly AI can go wrong. In one case, researchers asked AI to analyze medical imaging, and the system “identified” a brain structure, one that doesn’t actually exist. It is a reminder that even advanced models can misinterpret data with convincing authority. Imagine the consequences if such errors are applied to patient care or payment decisions. This has already caused challenges in the prior authorization space.  In healthcare payment integrity, the stakes are too high to leave decisions solely to technology.

That is why Human-in-the-Loop (HITL) AI is not an option but a necessity.

The Controversy Around AI in Payment Integrity

AI’s ability to rapidly surface anomalies in claims is powerful, but controversy has followed its adoption. Relying solely on AI to identify payment integrity issues carries significant risks. While algorithms excel at processing large volumes of claims data, they can miss context, misinterpret clinical nuances, or flag legitimate claims as erroneous. Without human oversight, this can lead to provider abrasion, delayed reimbursements, and patient dissatisfaction. Moreover, biases in training data or incomplete datasets can introduce errors that undermine trust in the process. 

A host of factors can impact the ability of AI to operate efficiently, including:

• Evolving fraud schemes that don’t trigger detection
• Data quality issues
• Black box methodologies – many advanced AI models do not provide clear insights into their decisions, undermining trust
• Ethical and legal risks – Widely publicized cases against health plans have revealed how low-precision AI models could lead to denied care, sparking lawsuits and public and regulatory scrutiny.

A balanced approach combining AI efficiency with expert clinical and operational reviews, a human-in-the-loop (HITL) approach, helps mitigate these risks while ensuring fairness, accuracy, and transparency.

What Is Human-in-the-Loop AI?

HITL integrates human expertise directly into AI workflows. In payment integrity, rather than fully automating decisions, HITL ensures that clinicians, auditors, and coders remain active participants in key analyses by validating data, reviewing anomalies, and making context-driven clinical judgments.

This framework creates a continuous feedback loop. AI flags potential fraud or errors, humans provide clinical and regulatory insight, and their corrections improve future AI performance. The result is a system that not only works faster but also gets smarter and more reliable over time.

The Many Benefits of HITL for Payment Integrity

A human-in-the-loop approach strengthens the reliability of payment decisions. By:

• Ensuring Accuracy and Data Quality – Human experts verify anomalies, resolve ambiguities, and prevent systemic errors that AI might reinforce unchecked. 
• Empowering Insights into Complex, Unstructured Data – Clinical notes, imaging reports, and nuanced coding often require human interpretation. HITL ensures that context and medical judgment are not lost in translation.
• Building Trust and Transparency – HITL mitigates the “black box” problem by introducing explainable, defensible human review. Provider abrasion is reduced when humans, not algorithms, handle disputes and clearly communicate findings.
• Enabling Adaptation to Fraud and Regulatory Change – Fraud schemes evolve constantly, as do compliance requirements. Human reviewers can train AI to recognize new fraud patterns or apply updated regulations far faster than automation alone.
• Upholding Ethics and Accountability – HITL ensures that critical decisions about payments and care remain accountable to qualified professionals, aligning with requirements such as the Department of Health and Human Services’ HTI-1 rule mandating algorithmic transparency.
• Optimizing Efficiency and Resource Use – With AI automating routine tasks, human staff can focus on higher-value work. 

MedReview’s Perspective: HITL is Foundational

At MedReview, we believe that AI is a tool, not a decision-maker. Left unchecked, AI methodology risks repeating the mistakes of the past by denying care unfairly, straining provider relationships, or even “inventing” medical realities. But when combined with physician judgment and human expertise, AI becomes a powerful ally and enhances accuracy, increases efficiency, and safeguards fairness.

Our HITL approach ensures that automation never replaces accountability. Instead, it empowers payers and providers to work more collaboratively, reduce fraud and waste, and build a more transparent, trustworthy healthcare payment system.

The MedReview Commitment

AI is changing payment integrity, but speed without accuracy is dangerous. From fabricated brain parts to lawsuits over denied claims, we’ve seen what happens when machines are left to make decisions alone. Human-in-the-loop AI is not just a safeguard—it’s the most effective and ethical path forward.

At MedReview, we are committed to leading with this approach by blending the best of automation with the judgment only clinicians can provide, to deliver higher accuracy, consistency, and value in healthcare payment integrity.