Data Analysis Tools Help Alleviate Administrative Burden, But Insurers Misattribute Coding Intensity

Hospitals are increasingly turning to AI tools to ease administrative burdens and improve patient care. One area where these tools make a significant impact is in billing, coding, and documentation.

However, some unsubstantiated claims have suggested that the use of AI tools has increased coding intensity, leading to higher healthcare costs. These allegations ignore underlying drivers of changes in coding intensity, including an aging population and rising prevalence of chronic disease.

Hospitals are caring for patients with higher acuity due to demographic shifts and advances in medicine. The AHA analysis found that 19% of hospital expense growth from 2019 to 2024 reflects caring for sicker, more complex patients. This trend is also reflected in the rising case-mix index – a standard measure of patient acuity.

The shift of lower-acuity care to outpatient settings has left hospitals with an increasingly complex and resource-intensive inpatient population. AI tools can support accurate coding and documentation by enhancing precision within established frameworks, but human validation remains essential for ensuring coding integrity and regulatory compliance.

Assertions that providers are upcoding due to the use of AI tools are striking given evidence of insurer-driven coding practices. Lawsuits, investigations, and reports from MedPAC, DOJ, and Congress have documented upcoding among large insurers, including adding diagnosis codes not supported by clinical care.

In 2025, MedPAC reported that upcoding contributed to $40 billion in overpayments to Medicare Advantage plans. One payer settled a lawsuit with the DOJ on upcoding allegations in March 2026, while another insurer is facing a state-level investigation for potentially fraudulent practices.

The AHA has urged policymakers to prevent insurers from systematically reducing reimbursement under the pretense of widespread inappropriate upcoding. This would help alleviate administrative waste and burden created by overly burdensome appeals processes.

AI tools are transforming care delivery in countless ways, supporting increased access, improved outcomes, and reduced costs. They offer tremendous potential for reducing more than $1 trillion spent annually on administrative functions. However, their use is not without controversy – particularly when it comes to coding intensity.

Some commercial insurers have asserted that AI tools used by providers are resulting in higher coding intensity. These accusations ignore multiple factors contributing to growth in coding intensity, including an aging population and increasing prevalence of chronic disease.

Hospitals maintain rigorous auditing and coding compliance programs to ensure they capture patient acuity levels accurately and meet regulatory requirements. Coding has always been governed by established guidelines, conventions, and expectations that define a compliant approach to code assignment.

Updates to evaluation and management coding guidelines have altered coding patterns, leading to more specific diagnoses being reported for higher-acuity patients. As providers adapt and data stabilize, these changes contribute to shifts in coding intensity.

The AHA has responded to concerns about AI tools used by providers, emphasizing the need for accurate representation of patient complexity. They argue that hospitals are not engaging in upcoding practices but rather accurately capturing acuity levels due to demographic shifts and advances in medicine.

In contrast, some insurers have been accused of using automated edits to unilaterally reduce reimbursement without reviewing medical documentation – a practice known as downcoding or partial denial. This can lead to burdensome appeals processes for providers seeking reimbursement for medically necessary care.

The AHA has urged policymakers to prevent such practices and ensure that hospitals are fairly reimbursed for the care they provide. They argue that accurate coding is critical for ensuring patient safety, quality of care, and regulatory compliance.

AI tools have tremendous potential for supporting data analysis in healthcare – from streamlining administrative tasks to improving patient outcomes. However, their use must be carefully managed to ensure accuracy, consistency, and fairness in reimbursement practices.