AI Cannot Fix a Broken Claims Paradigm

ABSTRACT

We read with interest a feature article on AJMC.com, titled “AI Seen as Key to Reducing Health Care Claim Denials, Survey Finds,” that highlighted the anticipated impact of artificial intelligence (AI) on the claims process. According to Steinzor et al, claim denials are climbing due to data inaccuracies, staffing shortages, fragmented eligibility systems, and outdated technology, pushing providers to prioritize denial reduction. The authors asserted that economic pressures and declining collections are intensifying the demand for faster, cleaner claims processing. Although most providers believe AI can strengthen the claims life cycle, adoption generally remains low. The main concerns center around accuracy, compliance, and training. As policies and payer rules change, providers are forced to adjust, with some turning to AI-driven solutions in the hopes of stabilizing financial performance and streamlining operations. We assert that AI on its own cannot fix a broken claims paradigm.

Am J Manag Care. 2026;32(6):e183-e184. https://doi.org/10.37765/ajmc.2026.89958

Takeaway Points

This commentary provides additional perspective on how the current digital paradigm does not serve clinicians nor the greater health care system. We provide the following paradigm shift recommendations:

• The US health care claims process must be standardized.
• Electronic health record (EHR) vendors must work with payers to ensure that each certified EHR can implement the data request algorithm for each payer and run the algorithm on any patient for whom a claim is made.
• Clinical associations should review the algorithms to ensure that payers request only the data necessary to validate a claim.

It has now been more than 15 years since the nation began its shared journey toward digital health records, and yet health plans still embrace a paper-based claims paradigm.^1,2 The US government spent $38 billion in federal funds and health entities spent much more in private funds to make this physical transition.³ They now must think differently about everything they do based on the paper paradigm, including how claims are processed.

In the current health plan model, the payer has a financial incentive to find a reason not to pay a timely claim. To meet the Quadruple Aim,⁴ the US health care system needs a model where the cost of verifying a claim shifts to the payer and away from the clinical entity. When health plans are required to be accountable for the costs of the delays they create, there is an incentive to pay quickly. Artificial intelligence (AI) technologies may have the potential to automate workflows, enhance fraud detection capabilities, improve customer interactions, reduce processing errors, and accelerate claim settlements. However, these technologies focus on symptoms rather than fixing the problem’s underlying root cause.⁵ If health plans wish to delay payment, they simply change the process to block the success of the AI effort.

A 2025 feature article published on AJMC.com by Steinzor et al, titled “AI Seen as Key to Reducing Health Care Claim Denials, Survey Finds,” highlighted that claim denials are climbing due to data inaccuracies, staffing shortages, fragmented eligibility systems, and outdated technology, pushing providers to prioritize denial reduction.⁶ The authors asserted that economic pressures and declining collections are intensifying the demand for faster, cleaner claims processing. Providers are turning to AI-driven solutions in the hopes of stabilizing financial performance and streamlining operations.⁶ Although most providers believe AI can strengthen the claims life cycle, adoption generally remains low.⁵ The main concerns center around accuracy, compliance, and training.^7,8 We assert that AI on its own cannot fix a broken claims paradigm.

According to Steinzor et al, “By leveraging large-scale health data and AI-driven solutions, firms are aiming to help providers reduce the impact of denials, streamline appeals, and improve patient access to care.”⁶ The problems identified include missing or inaccurate data (50%), authorization issues (35%), and incomplete or inaccurate patient registration data (32%).⁶ A 16% increase in prescription drug denials between 2018 and 2024 driven by cost-containment strategies such as prior authorization was also noted.⁶ None of these excuses should be considered valid in the era of digital information.

Although we do not want to minimize the impact AI might have on the health care system, it cannot rectify delays and denials resulting from systemic issues like prior authorization. To improve approval rates, we must address the root causes of all denials.⁹ We need a paradigm shift to enable payer accountability.

How Might a New Paradigm Better Enable Payer and Provider Accountability and Streamline the Claims Process?

To enable accountability and streamline the claims process, the US health care claims process must be standardized. First, the health care system must shift the claim submission costs. Payers should be required to have specific, defined criteria for what data they require to pay a claim. There is a fixed number of claims, so the cost of creating these standards would be insignificant to the payers, regardless of the cost per rule. They could choose to work together to develop a single set of industry-wide standards or create their own proprietary rules. This would ensure that all data are requested once at the beginning of the payment request—no delays and no repeated interactions that waste clinical and administrative staff time.

It is often heard that health plans are concerned about upcoding and inappropriate billing. Although the data supporting this concern are limited, this new process simply automates the payer’s review; it does not eliminate payer oversight. The payer defines the data to be reviewed, pulls the data, makes the review, and returns its decision.

Second, electronic health record (EHR) vendors must work with payers to ensure that each certified EHR can implement the algorithm for each payer and run it on any patient for whom a claim is made. EHR vendors would charge payers for each rule change after initial implementation, creating a disincentive to change rules except when there is significant population financial savings for the payer, rather than whenever a claim is made. The EHR owner is permitted to charge a fee for the size of the data packet sent to the payer. This might be a penny or less per kilobyte, but with millions of claims, it can add up if unnecessary data to manage the claim are being requested.

Third, clinical associations would review the algorithms to ensure that payers request only the data necessary to validate a claim. This is needed to ensure that payers do not violate their beneficiaries’ privacy in ways that might harm patients.

Throughout this process, payers could insist on nondisclosure agreements and other legal measures to ensure that their algorithms remain private if they wish. The need for payer accountability outweighs the small risk to payers that a competitor will learn about their specific algorithm(s).

Once we change the paradigm to consistently manage health service claims, AI can be used when needed (with expert human supervision) to apply clearly defined parameters and algorithms, not as an extension of an arbitrary practice that creates excess expense and care delay. Through a fair and transparent claims process that leverages data already in the EHR and holds insurers accountable, we can deliver better care. 

Author Affiliations: School of Medicine and Joe C. Wen School of Population and Public Health, University of California, Irvine (MB), Irvine, CA; University of California San Diego Health (MK-M), San Diego, CA; Clinical Informatics, Inc (LO), Woodland, CA; Penn State Health (RS), Newport, PA.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (MB, MK-M, LO, RS); acquisition of data (MK-M); analysis and interpretation of data (MB, MK-M, RS); drafting of the manuscript (MB, MK-M, LO, RS); and critical revision of the manuscript for important intellectual content (MB, MK-M, LO, RS).

Address Correspondence to: Manijeh Berenji, MD, MPH, University of California, Irvine, 856 Health Sciences Quad, Ste 5600, Irvine, CA 92697. Email: manijehberenji@gmail.com.

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