AKASA Launched Autonomous AI for Inpatient Coding
Hospital operators gain a new tool to automate complex clinical documentation after patient discharge.
Updated on Oct. 2, 2026 in Healthcare
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Do you trust artificial intelligence to accurately handle complex medical coding for patient records?
AKASA has debuted an autonomous AI platform designed to handle inpatient medical coding and clinical documentation without human intervention. The technology reduces coding time to 90 seconds per patient encounter, targeting a workflow that typically requires 30 to 60 minutes for manual processing.
Why it matters
Health systems face chronic coding workforce shortages and growing clinical complexity that threaten revenue cycle efficiency. Automating these tasks allows systems to manage increased care volumes without scaling their administrative headcount.
AKASA processes inpatient volumes for customers representing over $180 billion in aggregate net patient revenue, which equates to 10% of total U.S. inpatient discharges. The platform addresses a complex documentation load that typically involves 50,000 words across 60 documents per stay.
The players
AKASA
A technology provider specializing in AI-driven automation for healthcare revenue cycle and clinical documentation.
Cleveland Clinic
A prominent non-profit academic medical center and health system planning to deploy the autonomous mid-cycle solution.
The details
The platform builds a custom AI model for each health system client, utilizing generative AI trained on specific patient populations and clinical criteria. To ensure accuracy, the company performs blinded evaluations comparing the AI against human medical coders regarding MS-DRG assignment and principal diagnosis. The system is designed to navigate an available medical code set of 150,000 options to streamline the mid-cycle revenue process.
Timeline
October 2, 2026: AKASA debuted the autonomous AI platform.
Last year: AKASA saw 6x growth in processed inpatient volume.
Next several months: The platform will be made available to health systems.
Market Landscape
Healthcare providers are increasingly turning to generative AI to bridge the widening gap between complex documentation requirements and stagnant administrative labor pools. This initiative follows a documented industry trend of hospitals automating mid-cycle solutions to improve speed-to-revenue.
Operators should review current medical coding cycle times to determine if their existing workflows meet the 30-to-60-minute industry benchmark. If capacity constraints are causing delays, explore whether specialized AI models can reduce documentation backlogs and improve revenue cycle throughput.
The takeaway
Autonomous coding represents a shift toward hands-off clinical documentation processing that could redefine efficiency for large-scale health systems. Monitor Cleveland Clinic's implementation to see how effectively the system handles complex inpatient cases compared to manual human performance.
Further reading
For more on administrative technology, see our coverage in Healthcare.
Live Poll
Do you trust artificial intelligence to accurately handle complex medical coding for patient records?
