Report: Health Systems See Promise in Generative AI for Documentation and Revenue Cycle Accuracy But Adoption Gaps Persist

20% of organizations have not started their GenAI for documentation and RCM journey, indicating room for growth.

South San Francisco, Calif. — December 17, 2025 — A new survey by the Healthcare Financial Management Association (HFMA) and AKASA, the leading provider of generative AI (GenAI) for healthcare revenue cycle management, reveals that while health systems are optimistic about the potential of GenAI to improve revenue cycle performance, many remain in the early stages of adoption, even as they feel the financial impact of inaccurate or incomplete clinical documentation.

New insights reveal that inaccurate documentation is preventing many health systems from receiving proper recognition for the high-quality care they deliver. As patient needs become increasingly complex, hospitals are seeking revenue cycle innovations that close clinical documentation gaps, improve quality scores, and help patients avoid delays, billing issues, and other administrative challenges in their care experience.

In 2025, 80% of health systems are taking action, exploring, piloting, or implementing GenAI-powered tools for revenue cycle management — a 38% increase in less than two years. Despite increased adoption, more than one in five organizations (20%) have not yet begun their journey with generative AI for clinical documentation and revenue cycle management (RCM). The smaller health systems (those with revenues between $500 million and $1 billion) recognize the value of deploying GenAI capabilities, but are falling behind the adoption curve and remain in early adoption stages due to budget constraints and the challenges of implementing new technology at scale. About 20% of those organizations are piloting and implementing, compared to more than half of larger health systems (64%).

When asked about barriers to implementation, organizations most frequently cited integration with existing systems, cost and budget constraints, and concerns about data security. For larger health systems, cost and budget constraints were the single biggest challenge (52.5%), underscoring the complexity and expense of implementing new AI technologies at scale.

“These findings show that health systems recognize the promise of generative AI but are still navigating the realities of implementation,” said Malinka Walaliyadde, CEO and co-founder of AKASA. “As organizations continue to balance innovation with operational and compliance demands, the findings suggest that GenAI could become a crucial lever to both capture the quality of care delivered and improve revenue integrity. The opportunity to improve accuracy in documentation is clear, but it requires investment, trust, and a roadmap that integrates technology into existing workflows responsibly.”

When asked where GenAI could make the greatest difference, respondents identified three leading opportunities:

• Identifying missed reimbursement opportunities (nearly 60%)
• Uncovering gaps in clinical documentation (57%)
• Identifying missed quality indicators (33%)

The analysis revealed a clear pattern: organizations reporting the highest revenue risk also reported the greatest impact from documentation inaccuracies, highlighting a strong link between documentation performance and financial outcomes.

The survey also found that documentation and coding errors remain a pressing concern:

• 89% of organizations report that missed or inaccurate codes have significant impact on revenue.
• On average, respondents estimate that 8.49% of their revenue is at risk due to incomplete or inaccurate documentation.
• While 55% of health systems use clinical document integrity (CDI) vendors and 76% use coding vendors, adoption remains fragmented.
• Almost one third of respondents state incomplete and inaccurate documentation is the most significant pain point in the mid-cycle

“The report confirms what many of us in healthcare are experiencing,” said Jackie Josing, Vice President, HIM/HB and PB Coding, CDI NS RI at LCMC Health. “Success with AI depends on partnerships that bring people and technology together with purpose. At LCMC Health, that’s how we’re moving innovation forward.”

Looking ahead, respondents expect GenAI to play a key role in the transformation of CDI. Over the next five years, 72% of respondents expect the CDI function to evolve toward more preventive, collaborative objectives, including greater involvement in denial prevention and appeals, stronger integration with coding teams, and a shift toward prospective reviews.

About the Survey

Commissioned by AKASA, the survey fielded responses from 519 chief financial officers and revenue cycle leaders at hospitals and health systems across the United States through the Healthcare Financial Management Association’s (HFMA) Pulse Survey program in April 2025. The national survey was designed to assess how organizations are addressing documentation and coding challenges — and how they are using GenAI to close quality and efficiency gaps.

About AKASA

AKASA is the leading provider of generative AI (GenAI) for healthcare revenue cycle management. It uses the latest technology to help leading health systems and academic medical centers get credit for the services they provide, producing better financial and quality outcomes while delivering more precise care. Powered by the AKASA Platform, our technology combines deep revenue cycle knowledge with custom large language models (LLMs) trained on a health system’s own clinical and financial data. Recognized by Black Book Market Research as the #1 healthcare RCM startup, AKASA helps organizations address complex workflows and capture the full value of care to deliver accuracy, compliance, and efficiency at scale.