AKASA Launches Autonomous AI Platform for Inpatient Medical Coding

Health technology company AKASA has launched an autonomous artificial intelligence platform for inpatient medical coding and clinical documentation integrity (CDI), marking an expansion of its revenue cycle management offerings beyond AI-powered prebill review.

The company said the platform is aimed at mid-cycle operations, where patient clinical records are translated into medical codes used for reimbursement, quality reporting, risk adjustment and maintaining the accuracy of patient records. AKASA executives said the initiative targets some of healthcare’s most complex and resource-intensive workflows.

Focus on Inpatient Coding

Inpatient medical coding differs from outpatient coding because it captures an entire patient stay, from admission through discharge, rather than individual procedures. The process requires extensive clinical documentation review and adherence to regulatory requirements.

Malinka Walaliyadde, AKASA’s chief executive officer and co-founder, said a typical inpatient stay consists of about 60 documents and 50,000 words that must be converted into medical codes selected from a pool of approximately 150,000 codes. He noted that coders generally spend between 30 and 60 minutes coding an inpatient encounter, while staffing shortages can delay work for several days after discharge.

“Fortunately, we’re now in a moment where AI has advanced enough that it can actually deliver against this exact need,” Walaliyadde said.

Performance Testing and Planned Rollout

AKASA said its AI can complete coding approximately 90 seconds after a patient is discharged. According to the company, the platform is designed to fully code highly complex inpatient cases across all specialties without human intervention.

The company conducted blinded third-party evaluations comparing AI performance with that of medical coders. AKASA said the studies included inpatient encounters representing between 65% and 85% of inpatient volume for health systems. The company reported that its AI matched or exceeded human coders on measures including MS-DRG assignment, principal diagnosis, clinical quality capture and present-on-admission accuracy.

Following internal testing and evaluation with a health system alpha partner, AKASA plans to make the platform available in the coming months. The company is also working with health system partners on methods to continue monitoring performance after deployment.

Expanding Beyond Coding

Beyond inpatient coding, the platform includes clinical documentation integrity capabilities intended to support documentation completeness. AKASA said these functions create a unified AI layer spanning clinical documentation, coding and prebill review activities.

The company also plans to add outpatient facility encounters to the platform. Walaliyadde said AKASA builds customized AI models for each health system it serves, allowing the technology to account for differences in patient populations, clinical criteria, documentation practices and care complexity.

Growth Across Health Systems

AKASA reported that inpatient volume processed through its systems has increased nearly sixfold over the past year. According to the company, its customers represent more than $180 billion in aggregate net patient revenue and roughly 10% of the country’s inpatient discharges.

Cleveland Clinic currently uses AKASA’s AI-powered prebill review products across coding and CDI and plans to deploy the autonomous mid-cycle solutions. Rohit Chandra, chief digital officer at Cleveland Clinic, said the health system intends to use the technology to improve the accuracy and completeness of clinical records while enhancing operational efficiency.

Customer Adoption

AKASA said its move into autonomous mid-cycle workflows builds on years of work with health systems seeking to modernize revenue cycle operations.

Nebraska Methodist Health System, which has worked with AKASA since 2019, said it has adopted the company’s technology over time and views greater autonomy as a way to increase capacity and improve efficiency in complex revenue cycle tasks. The health system said its experience with AKASA has been reflected in measures including revenue integrity, denials, write-offs and payment timelines.

AKASA has launched a new autonomous AI platform designed to transform inpatient medical coding and clinical documentation workflows. The company announced the platform on October 2, 2026, expanding its healthcare revenue cycle technology from AI-assisted processes toward greater automation.

The new AKASA platform focuses on the healthcare mid-cycle, where clinical documentation and medical coding take place between patient care and billing. Inpatient coding is particularly complex because coding teams must review extensive clinical records and translate patient encounters into appropriate medical codes.

How AKASA’s Autonomous Coding Works

The AKASA platform is designed to read and reason across the complete patient record before autonomously coding an encounter. According to the company, the system identifies diagnoses and procedures and connects codes with supporting clinical evidence.