80% of health systems are taking action on GenAI in the revenue cycle. READ THE NEW REPORT.

Margins are getting tighter. Mid-cycle matters more than ever.

New Medicaid revenue pressure demands a proactive strategy. Protect your health system’s margins with AKASA generative AI. We review 100% of encounters, identifying gaps in coding and documentation that affect revenue, compliance, and quality outcomes.

Fill out the form to get connected with an AKASA AI expert. They can help you figure out how this technology can best fit your revenue cycle. You can’t afford to miss out.

Dig into Genai

Medicaid fallout:
A looming margin crisis

The One Big Beautiful Bill is reshaping Medicaid — and the ripple effect on health systems is massive. These cuts won’t be offset anytime soon. And for many systems, margins are already razor thin.

$0B

in projected uncompensated care over the next 10 years

Americans expected to
lose Medicaid coverage

drop in hospital operating margins (and more for rural systems)

New Report:
Fixing the Revenue Cycle With GenAI

Learn how GenAI strengthens revenue integrity and documentation accuracy in a practical three-phase roadmap for health systems.

DOWNLOAD

Cuts to federal healthcare spending of this magnitude are likely to have consequences for hospitals, and could lead some to lay off staff, offer fewer services, or close altogether...Hospitals could respond by operating more efficiently in ways that don’t necessarily harm patient care.

Zachary Levinson

Project Director, Project on Hospital Costs at KFF

Zachary Levinson

Project Director, Project on Hospital Costs at KFF

Zachary Levinson

Project Director, Project on Hospital Costs at KFF

two female medical coders, one brunette and one blonde, working at computer monitors

The smartest move?
Mid-cycle optimization

You can’t control policy. But you can control mid-cycle performance. Your biggest lever for financial resilience. AKASA GenAI gives health systems the ability to:

Improve DRG accuracy and case mix

Prevent denials before they happen

Increase reimbursement without adding staff

Find revenue opportunities missed by rules-based tech

Purpose-built for mid-cycle

AKASA GenAI is trained on 43M+ clinical documents and refined specifically for mid-cycle tasks like coding and CDI. Each model is trained on your health system’s documentation patterns, case mix, and specialties — ensuring the most accurate, relevant results possible.

Reviews every encounter

Unlike human-driven reviews and rules-based tools, AKASA analyzes 100% of encounters and identifies missed documentation, unsupported codes, and quality risks. Every suggestion is backed by clinical references, with quotes and links directly to the chart. We show our work.

Comprehensive impact

AKASA improves DRG accuracy, reduces denial risk, and strengthens quality metrics. With just minutes of review time per case, you ensure accurate, compliant financial performance — without adding headcount or disrupting workflows.

Cleveland Clinic has embraced artificial intelligence to enhance the experience of patients and caregivers, and with our collaboration with AKASA we are bringing AI-powered enhancements to our mid-revenue cycle. Because we treat some of the highest acuity patients in the country, our revenue cycle activities are incredibly complex. We aim to bring greater efficiency and accuracy to these complicated and time-consuming tasks, something that AI is ideally suited to address.