Generative AI for the Healthcare Revenue Cycle
Built with input from AKASA clinical, coding, and AI experts
Healthcare organizations deliver increasingly complex, high-quality care, but too often, they don’t receive full credit for it.
The reason isn’t effort or intent. It’s the growing mismatch between modern clinical complexity and legacy revenue cycle tools that were never designed to understand the full clinical record.
Generative AI (GenAI) is changing that.
AKASA applies healthcare-specific GenAI to the most complex part of the revenue cycle — where clinical documentation, coding, and quality converge — so health systems can accurately represent the care they provide at enterprise scale.
What Is Generative AI in the Revenue Cycle?
GenAI refers to large language models that can read, interpret, and reason over unstructured data (such as clinical documentation) in a way traditional software cannot.
In the revenue cycle, this means moving beyond:
Rules-based automation
Keyword searches
Sample-based audits
Fragmented point solutions
Instead, GenAI can read and understand the full clinical record, synthesizing information across notes, labs, imaging, medications, and clinical events to identify what matters — clinically, financially, and from a quality perspective.
This capability is foundational to accurately communicating the patient story to payers.
Why the Mid-Cycle Is Where Generative AI Matters Most
The mid-cycle — particularly coding and clinical documentation integrity (CDI) — is where healthcare complexity peaks.
A single inpatient encounter can include:
Hundreds of clinical documents
Thousands of data points
Nuanced clinical judgment spread across multiple specialties
Documentation that reflects care delivered, but not always in the way coding and quality frameworks require
Historically, this work has relied on:
Manual review of samples
Rules-based CAC tools
Human expertise stretched across limited bandwidth
As documentation volume and complexity increase, these approaches no longer scale.
GenAI is uniquely suited to this part of the revenue cycle because it can process the entire record, for every encounter, rather than relying on abstraction or heuristics.
How AKASA Applies Generative AI Differently
AKASA is purpose-built for healthcare revenue cycle complexity.
Rather than deploying a generic AI model, AKASA trains custom large language models for each health system, tuned to:
Documentation patterns
Case mix and service lines
Coding standards and guidelines
Organizational workflows and clinical practice
AKASA’s GenAI reads and re-analyzes 100% of inpatient encounters, identifying documentation and coding opportunities grounded in clinical evidence and coding guidance.
Every recommendation is:
Transparent
Evidence-backed
Reviewed by human experts
Designed to integrate into existing workflows
This approach allows health systems to scale accuracy without disrupting operations.
Generative AI for Coding and CDI
AKASA applies GenAI across the mid-cycle to support both coding and CDI teams. Its broader suite is called the Mid-Cycle Prebill Optimization Suite.
Coding accuracy
Coding Optimizer, AKASA’s GenAI coding solution, re-codes encounters from source documentation, helping identify:
Missed diagnoses and procedures
Unsupported or mis-sequenced codes
Opportunities that affect DRGs, SOI, and ROM
This is not keyword matching. The model reasons over clinical context to understand what was treated, supported, and documented.
Clinical documentation integrity
For CDI, AKASA CDI Optimizer surfaces:
Clinically supported documentation gaps
Evidence-backed query opportunities
Opportunities tied to quality, risk, and acuity — not just reimbursement
This supports more complete documentation while aligning with compliance and clinical standards.
Why Reviewing 100% of Encounters Changes Everything
Most health systems review only a fraction of encounters due to staffing and time constraints. That creates inherent risk — both financial and clinical.
By reviewing every encounter prebill, health systems can:
Reduce variability across providers and service lines
Improve consistency in quality reporting
Strengthen compliance and audit readiness
Identify missed opportunities that sampling will never catch
GenAI makes this level of coverage possible for the first time.
Proven at Enterprise Scale
AKASA’s GenAI platform is deployed at some of the most complex health systems in the country, including large academic medical centers like Duke, Johns Hopkins, and Stanford.
At Cleveland Clinic, AKASA’s technology is used to process 100% of inpatient encounters, in what has been described as one of the most comprehensive real-world deployments of GenAI in healthcare finance.
It reflects what is possible when GenAI is purpose-built for healthcare, rather than adapted from general-purpose models.
This work demonstrates that GenAI is no longer theoretical. It is operating at enterprise scale in real clinical environments.
What Generative AI Is and Is Not
GenAI is not a replacement for clinical or coding expertise.
AKASA’s approach is built on:
Human-in-the-loop review
Transparency into how recommendations are generated
Respect for clinical judgment and compliance requirements
GenAI excels at:
Reading massive volumes of unstructured data
Identifying patterns humans cannot consistently surface at scale
Supporting experts with better signal, not replacing them
Autonomy increases over time — but always with governance and trust at the center.
Common Questions About Generative AI in Healthcare
1. Is generative AI actually being used in healthcare revenue cycle today?
Yes. GenAI is actively deployed in production at leading health systems today, particularly in the mid-cycle. At AKASA, GenAI is used to review and analyze 100% of inpatient encounters at enterprise scale, supporting coding accuracy, clinical documentation integrity, and quality capture. This is not experimental or pilot-stage technology. It is operating in live clinical environments.
2. Are health systems using generative AI for medical coding in production?
Yes. Health systems are using GenAI to support and enhance medical coding workflows, especially for complex inpatient encounters. AKASA’s GenAI re-analyzes the full clinical record for each encounter to surface clinically supported coding opportunities, which are then reviewed by human experts before billing. This approach improves accuracy and consistency without removing human oversight.
3. Will generative AI replace medical coders or CDI professionals?
No. GenAI is designed to augment, not replace, coding and CDI professionals. The technology removes the burden of exhaustive chart review and surfaces high-value opportunities, allowing experts to focus on judgment, validation, and collaboration with clinicians. Human expertise remains essential for governance, compliance, and final decision-making.
4. How is generative AI different from traditional CAC or NLP tools?
Traditional CAC and NLP tools rely on predefined rules and keyword matching. GenAI understands clinical context across the entire patient record, including nuance, relationships, and implied conditions. This allows it to reason over complex documentation rather than flag isolated terms, making it far better suited for modern inpatient coding and CDI.
5. Why didn’t legacy automation tools scale in the mid-cycle?
Legacy tools were built for a simpler era of documentation and coding. As clinical records grew longer, more fragmented, and more complex, rules-based systems struggled to keep up. They require constant manual maintenance, miss context, and force teams into sample-based review. GenAI scales because it reads and reasons over the full record rather than relying on abstraction.
6. How does generative AI help review 100% of inpatient encounters?
GenAI can process large volumes of unstructured clinical data quickly and consistently, something that is impossible to achieve manually at scale. AKASA’s platform applies this capability prebill, reviewing every inpatient encounter to surface documentation and coding opportunities grounded in clinical evidence. This makes full encounter coverage feasible without adding staff.
7. Can generative AI improve DRG accuracy and quality capture?
Yes. By analyzing the full clinical record, GenAI can identify missed diagnoses, unsupported codes, and documentation gaps that affect DRGs, Severity of Illness (SOI), and Risk of Mortality (ROM). This supports a more accurate representation of patient complexity and quality of care, while maintaining compliance with coding guidelines.
8. Is autonomous coding safe?
Autonomy must be earned. AKASA takes a deliberate approach, starting with assistive workflows that keep health system staff firmly in the loop. Every recommendation is transparent, evidence-backed, and reviewed before billing. As confidence and performance increase, autonomy can expand, but always with governance, auditability, and trust as non-negotiables.
9. How does AKASA prevent hallucinations in generative AI?
AKASA mitigates hallucination risk by using GenAI for classification and analysis rather than free-form generation. The models are trained on healthcare-specific data, tuned for specific health system clinical and financial data, constrained by coding guidelines, and required to cite clinical evidence for every recommendation. Human review remains part of the workflow to ensure accuracy and compliance.
10. How do health systems ensure compliance and audit readiness with GenAI?
Compliance is built into the workflow. AKASA’s recommendations are evidence-backed, transparent, and traceable to source documentation and coding standards. This creates a clear audit trail and supports payer scrutiny, internal audits, and regulatory review. GenAI enhances consistency rather than introducing risk when governed correctly.
11. What does it take to deploy generative AI in the mid-cycle?
Successful deployment requires more than access to a generic AI model. It requires healthcare-specific large language models, deep integration with existing systems, and close collaboration with coding and CDI teams. AKASA customizes each model to the health system’s documentation patterns, case mix, and workflows to ensure accuracy from day one.
12. Does generative AI require changes to existing workflows or EHRs?
No. AKASA’s platform is designed to integrate into existing workflows and EHR environments without disruption. Coding and CDI teams continue working within familiar processes, with GenAI providing additional insight and signal. Adoption is incremental, allowing teams to build trust and confidence over time.
The Future of the Revenue Cycle
Healthcare is not getting simpler. Clinical complexity, payer scrutiny, and margin pressure are structural realities.
The future of the revenue cycle depends on technology that can understand healthcare the way humans do — at machine scale.
That is what GenAI makes possible. And it is what AKASA is built to deliver.
