Cleveland Clinic is scaling up its use of Akasa’s GenAI tools to streamline medical coding and documentation workflows. The move promises faster, more accurate billing and improved documentation integrity, especially in complex inpatient settings where the burden of record review is heavy.
Glimpse:
Cleveland Clinic has expanded its collaboration with Akasa to deploy AI-powered tools for coding and Clinical Documentation Integrity (CDI) across all its U.S. hospitals. Traditionally, coders there review over 100 clinical documents per patient encounter progress notes, discharge summaries, pathology reports and then pick among tens of thousands of codes. The process can take up to an hour for some encounters. With the new AI assistant tools, much of that document processing is streamlined: one tool supports coding by interpreting clinical context and complexity; another helps ensure documentation accurately reflects care (CDI). Early trials show dramatic speedups, improved accuracy, and promise for better revenue cycle efficiency.
In the modern hospital, the paperwork is as thick as the patient charts and nowhere is that more evident than in revenue cycle management. Now, Cleveland Clinic is leaning into Generative AI (GenAI) to make a big dent in that load.
The partnership with Akasa deepens an existing effort: deploy AI tools in the mid-revenue cycle, the stage between patient care and billing, where documentation and coding happen. Here, clinical documentation is enormous, complex, and often idiosyncratic. Coders might sift through over 100 different clinical notes per case, choosing from over 140,000 possible codes. It’s not just tedious it’s expensive, time-consuming, and prone to error.
Overall, Cleveland Clinic’s expanded rollout of Akasa’s GenAI-powered tools signals a maturation of AI use in healthcare operations. As hospitals push to reduce administrative burdens and improve financial sustainability, these kinds of tools may become standard rather than experimental—especially where documentation volume is huge and complexity is high.
“Because we treat some of the highest-acuity patients in the country, our revenue cycle activities are incredibly complex. Through autonomous coding, we aim to bring greater efficiency and accuracy to these complicated and time-consuming tasks something that AI is ideally suited to address,”
By
HB Team
Related News
