Hospitals lose millions to medical billing errors. Discover how AKASA uses AI in revenue cycle management to cut claim denials and capture lost revenue.
With hospital operating margins often sitting in the single digits, the complex maze of medical billing is silently costing health systems millions in uncollected revenue. In this episode of HealthTech Remedy, we sit down with Benjamin Beadle-Ryby, co-founder of AKASA, to explore how generative AI is fundamentally transforming revenue cycle management (RCM). You will discover how shifting from manual coding to an autonomous revenue cycle can drastically reduce claim denials, uncover missed charges, and ultimately protect a hospital's financial mission.
The conversation unpacks the structural complexity of hospital reimbursement, where a single inpatient encounter can generate 50,000 words of documentation that human coders must manually distill into precise ICD-10 and DRG codes. Benjamin shares the framework behind AKASA’s custom large language models, revealing how fine-tuning healthcare AI on a health system's specific historical data can seamlessly surface missed quality indicators in up to 10% of claims. We also debate the delicate balance of AI automation and human oversight, questioning what happens to the highly specialized knowledge of medical billers as these tools evolve. You’ll have to listen to find out why traditional rules-based systems are failing and exactly how generative AI could drive the cost to collect below one percent by 2030.
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