AKASA Launches First Autonomous AI Platform for Coding and Documentation

Company's customer base represents 1 in 10 U.S. inpatient discharges as it launches the autonomous mid-cycle for both inpatient coding and clinical documentation in healthcare's most complex and resource-intensive workflows

SOUTH SAN FRANCISCO, Calif., Oct. 2, 2026 /PRNewswire/ -- AKASA, a leader in generative AI for the healthcare revenue cycle, today announced the launch of its autonomous AI platform for the mid-cycle, expanding from AI-powered prebill review into autonomy for both inpatient medical coding and clinical documentation integrity (CDI).

The launch comes amid rapid expansion across AKASA's health system customer base, driven by their AI products. Inpatient volume processed by the company has grown almost 6x in the last year. Today, AKASA's customers represent more than $180 billion in aggregate net patient revenue and roughly 10% of the country's inpatient discharges.

AKASA fine-tunes AI models for individual health systems, thus accounting for differences in patient populations, clinical criteria, documentation practices, and care complexity. This approach ultimately supports complete documentation and accurate coding to better reflect care delivered and improve the patient care process.

"The incredible demand for healthcare is finally being addressed by advancements in AI. Multiple parts of the healthcare ecosystem will need to scale up, with documentation and coding being critical components," said Malinka Walaliyadde, CEO and co-founder of AKASA. "For years, an autonomous mid-cycle has been a holy grail in our industry. Today, AKASA is making it real."

Bringing autonomy to healthcare's most complex revenue cycle workflows

The mid-cycle is where a patient's clinical record is translated from documentation into codes that drive reimbursement, quality reporting, risk adjustment, and the integrity of the patient record. The work is highly complex, resource-intensive, and still predominantly manual.

That complexity has made reliable automation difficult. A 2025 peer-reviewed study in npj Health Systems cited medical coding error rates of up to 20%, while a July 2026 U.S. Government Accountability Office review identified verifiable accuracy as a central challenge as healthcare organizations adopt AI for medical notes and coding.

AKASA's autonomous technology is designed to fully code highly complex inpatient cases across all specialties with no human intervention. The company will soon introduce outpatient facility encounters to the platform as well. The platform is designed to expand workforce capacity, accelerate billing, and improve quality performance.

The company has conducted rigorous third-party, blinded evaluations comparing AI performance against medical coder performance. In these studies, the company tested inpatient encounters representing 65%–85% of all inpatient volume for health systems and demonstrated that AKASA's AI performance matched or exceeded that of human coders on key accuracy measures. These measures included MS-DRG assignment, principal diagnosis, clinical quality capture, and present-on-admission accuracy (all critical to coding quality, compliance, and reimbursement).

Typically, a coder takes 30–60 mins to code an inpatient encounter. In addition, coding workforce shortages and capacity constraints can leave health systems waiting several days after discharge for a coder to even start working on an account. In contrast, AKASA's AI can complete coding in less than 90 seconds after the patient is discharged, dramatically accelerating the billing process and reducing accounts receivable days.

Beyond medical coding, AKASA extends these capabilities upstream with CDI, helping ensure documentation completeness and creating a unified AI layer across clinical documentation, coding, and prebill review.

As AKASA rolls out the autonomous mid-cycle, it will work with health systems to design customized deployment plans to scale autonomous volume based on needs.

Building on proven results with leading health systems

Through its collaboration with AKASA, Cleveland Clinic has enhanced the accuracy and completeness of its clinical records using the AKASA prebill review products across coding and CDI. Cleveland Clinic intends to explore autonomous mid-cycle solutions with AKASA to further strengthen the accuracy and completeness of its clinical records and enhance operational efficiency.

"Our revenue cycle work is especially time-intensive because we care for many medically complex patients," said Rohit Chandra, Ph.D., chief digital officer at Cleveland Clinic. "With autonomous coding, we seek to improve speed and precision in these challenging processes under a compliance-first approach to this work."

AKASA's expansion into mid-cycle automation builds on years of work helping health systems modernize revenue cycle operations.

"AKASA has been a pioneer in generative AI and in revenue cycle solutions," said Jeff Francis, chief financial officer and vice president of finance at Nebraska Methodist Health System, who has worked with the company for several years. "We've moved with them step by step, and it shows up in the metrics I care about: revenue integrity, denials, write-offs, and how quickly we get paid. Autonomy feels like the natural next step as health systems look for ways to increase capacity and make complex revenue cycle tasks more efficient."

"Healthcare cannot meet the scale of demand ahead through human labor alone," said Julie Yoo, general partner at Andreessen Horowitz (a16z). "AKASA is turning deep technical research into frontier AI that can do complex work at scale inside the country's most sophisticated health systems. Its autonomous mid-cycle technology shows what's possible when AI is built for the specific demands of complex clinical documentation and inpatient coding."

About AKASA

AKASA builds AI for the most complex work in the healthcare revenue cycle. Its custom AI models are trained on each health system's own clinical documentation, case mix, and coding decisions. AKASA supports health systems at every stage of AI adoption. Its solutions range from AI that strengthens CDI and coding teams to fully autonomous workflows that code encounters end-to-end. AKASA's customers include leading health systems that together account for more than $180 billion in net patient revenue and roughly 1 in 10 U.S. inpatient discharges.

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SOURCE AKASA