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AKASA Mid-Cycle Optimization Suite 2026
Applying AI to Inpatient Coding & Clinical Documentation
Author
Connor Northrup
Author
Drew Partridge
AKASA’s Mid-Cycle Optimization Suite is intended to help healthcare organizations effectively and safely use AI-powered human-in-the-loop automations and workflows to reduce manual input, improve coding accuracy, and minimize data and documentation gaps. To highlight the solution’s strengths and opportunities for improvement, this case study examines one early adopter’s experience.
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Project Manager
Kyle Chilton
This material is copyrighted. Any organization gaining unauthorized access to this report will be liable to compensate KLAS for the full retail price. Please see the KLAS DATA USE POLICY for information regarding use of this report. © 2026 KLAS Research, LLC. All Rights Reserved. NOTE: Performance scores may change significantly when including newly interviewed provider organizations, especially when added to a smaller sample size like in emerging markets with a small number of live clients. The findings presented are not meant to be conclusive data for an entire client base.