The United States is facing a healthcare crisis that extends far beyond rising costs. Physician burnout is accelerating, access to care is eroding, particularly in primary care and rural or underserved communities, and administrative burden continues to crowd out time spent with patients. Clinicians are expected to diagnose and treat illness in addition to navigating an increasingly complex maze of documentation, billing rules and compliance requirements. That burden has become a major driver of workforce attrition across the system.
To cope, healthcare organizations are turning to artificial intelligence tools as a practical necessity. A.I. scribes are streamlining clinical documentation, while A.I.-enabled coding solutions are translating notes into accurate billing codes in real time. These technologies allow clinicians to focus more on care delivery and less on paperwork, an outcome that nearly every stakeholder agrees is overdue.
The operational benefits are clear. A.I. coding systems don’t just stop at reducing administrative workloads; they can also materially improve financial performance. Mercyhealth, for example, reported a 5.1 percent revenue increase after implementing an A.I. coding solution. Health systems using automated coding are also seeing meaningful reductions in claim denials, an issue that can cost large organizations up to $5 million annually. At a time when hospitals are operating on razor-thin margins, these efficiencies are not marginal gains.
Yet as providers increasingly adopt A.I. to stabilize their operations, payers are responding with suspicion. Insurers have begun to characterize the use of automated coding as “over-coding,” and executives at major companies, including UnitedHealthcare and Centene, have recently signaled plans to deploy additional A.I. tools to counter what they describe as aggressive billing practices. The result is an emerging A.I. arms race across the revenue cycle that risks deepening mistrust rather than fixing the underlying problem.
The issue is structural. The U.S. health insurance model is built around utilization management practices that deny, delay or reduce claims as a means of cost control. While insurance plays an essential role in society, its economic incentives are fundamentally misaligned with those of providers and patients. In response, clinicians and health systems have been forced to document and code with extraordinary precision simply to receive payment for care already delivered. What could be a straightforward process has evolved into a system defined by complexity, opacity and constant rule changes.
In this environment, manual billing and coding are no longer realistic. The volume of documentation requirements, regulatory updates and coding revisions has outpaced what even highly trained human coders can reasonably manage. A.I. is not a shortcut or a revenue manipulation tool. It is the only scalable way to navigate a billing ecosystem that has become too complex for human cognition alone. In the modern healthcare landscape, A.I. has become foundational infrastructure.
This tension is exacerbated by regulatory lag. Much of the U.S. reimbursement framework—largely shaped by the Centers for Medicare & Medicaid Services—was built for a manual, human-coded era. Yet those same rules now govern A.I.-assisted workflows without updated guidance on how automation should be evaluated, audited or incentivized. Without modernization, policy risks penalizing efficiency rather than rewarding accuracy, leaving providers stuck between outdated compliance standards and operational reality.
As a physician with firsthand experience, concerns that A.I.-enabled coding exists to inflate bills misunderstand both the technology and the problem it aims to address. Proper coding is not about embellishment. Automated systems ensure that services provided are captured correctly the first time, reducing the need for rework, appeals, and prolonged reimbursement cycles.
Several years ago, I became the chief physician at an assisted living facility for patients with dementia, to fill care gaps for residents who were no longer able to be seen by their primary care physicians. Despite enjoying the experience of caring for patients in their skilled-care environment, I struggled to grasp coding for the care delivered outside of my office. After nine months of endless delays and denials, I also realized I was earning about 25 cents on the dollar per patient visit. Thus, I “burned out” before one year had passed and resigned, entirely due to the burden of coding.
Expecting providers to accept underpayment for delivered care would be akin to asking a grocery chain to allow consumers to leave their store having paid for only part of what is in their cart. Delayed reimbursements and denied claims introduce far more cost into the system than accurate coding ever could—costs that ultimately ripple out to patients through reduced services, longer wait times and, in some cases, facility closures. This year alone, 23 hospitals and emergency departments have closed. From a policy standpoint, the accelerating closure of hospitals, particularly in rural and underserved areas, raises questions that extend beyond individual balance sheets. Reimbursement delays, denials and administrative drag are increasingly shaping which communities retain access to care at all. This is not a sustainable model for anyone.
Importantly, modern A.I. coding platforms are highly auditable systems in which every billing decision is traceable back to specific clinical documentation. This transparency provides a clear rationale for claims, offering a path toward greater accountability for both providers and payers.
Notably absent from this debate is equal scrutiny of how payers deploy A.I. themselves. Insurers increasingly use automated systems to flag, delay or deny claims at scale, often with far less transparency than provider-side tools.. Regulating one set of algorithms while leaving the other unchecked only deepens asymmetry and mistrust. A constructive path forward would focus on shared standards rather than mutual suspicion. Establishing clear guidelines for A.I.-assisted coding that define auditability requirements, documentation traceability and acceptable use across both payer and provider systems would replace escalation with a common framework for accountability.
Payers and providers ultimately share the same stated goals: delivering high-quality care, operating efficiently and maintaining financial viability. Treating A.I. coding tools as ammunition in an ongoing battle undermines all three. Used properly, these technologies offer an opportunity to simplify an overengineered system, reduce friction and refocus resources on patient care.
Ending the A.I. arms race will require a shift in mindset. Progress depends on collaboration and on recognizing that automation can be a shared tool for clarity, fairness and sustainability. Without that reset, the system risks continuing down a path that exhausts clinicians, destabilizes institutions and leaves patients with fewer places to turn.
