Why Leading Health Systems Partner With AKASA

Generative AI is reshaping the revenue cycle faster than any technology that came before it.

But as leaders evaluate vendors, the core question has shifted from “Does the technology work?” to “Who can we trust to bring this into our organization safely, accurately, and at scale?”

That’s where partnering with the right company matters just as much as choosing the right product.

Across conversations with HIM, CDI, coding, finance, and IT leaders, the reasons organizations choose AKASA tend to fall into a common set of themes — ones grounded in real needs, not marketing claims.

Below is a deeper look at what sets AKASA apart from the broader vendor landscape and why many health systems and academic medical centers (Cleveland Clinic, Johns Hopkins, Stanford, Duke, etc.) see AKASA as the long-term partner for mid-cycle transformation.

1. Built for healthcare 

Many AI vendors in the market today began as general-purpose AI companies or automation platforms trying to retrofit their tools to healthcare.

AKASA took the opposite path.

From day one, we combined:

• Seasoned clinical experts (coders, CDI specialists, nurses, auditors)

• Healthcare-native machine learning engineers

• Leaders who built revenue cycle products before GenAI existed
  
  

This blend matters. It means the system’s reasoning reflects real-world clinical patterns, guideline-based logic, and documentation nuance — not generic language modeling.

As one HIM leader put it:

“Most AI companies try to understand healthcare. AKASA starts from healthcare.”

2. Proven in the most complex environment: inpatient

Inpatient is where most tools fail.

Charts often exceed 50,000 words, span multiple days, and involve multiple providers and conflicting documentation.

Inpatient encounters are the most complex, most nuanced, and most variable environments for documentation. They are also the area where legacy tools like CAC and NLP consistently underperform.

This is where AKASA operates.

AKASA’s technology has demonstrated the ability to:

• Read and reason across 50,000-word patient encounters

• Interpret multi-day timelines

• Integrate labs, notes, consults, imaging summaries

• Understand multiple treating teams

• Surface conditions with evidence and context

• Support reviewers with accuracy and defensibility

This achievement distinguishes AKASA from both traditional vendors and newer AI entrants that are still focused on outpatient or narrow DRG subsets.

More importantly, it does this at scale. At one $10B+ health system:

• 7.3% of encounters had confirmed opportunities

• Accepted changes led to a 1.17 increase in MS-DRG weight

• $69M in potential annual recovered revenue was identified

This is the difference between partial insight and complete interpretation.

3. A reasoning engine, not a suggestion one

Health systems leaders are always calling out the same struggle: “We don’t want more suggestions. We want clarity.”

CAC tools guess. NLP tools extract. Rules engines match patterns.

AKASA does something different: it models clinical reasoning — the “thinking process” coders and CDI specialists use to understand the patient story.

This includes:

• Identifying clinical indicators

• Understanding sequence and timing

• Comparing conflicting documentation

• Contextualizing labs, vitals, and orders

• Supporting POA and specificity

• Tying every conclusion to evidence

The result is a system that teams trust because they can see the why, not just the what.

“If you don’t capture the full patient story accurately, it creates friction across the entire revenue cycle. We remove that friction.”

~ Malinka Walaliyadde, CEO and co-founder of AKASA

4. Tailored to each health system 

Every organization documents differently. Every provider documents differently. Every service line has its own patterns.

A generic large language model (LLM) can’t address that.

We’re not a one-size-fits-all model. AKASA’s model adapts to your:

• Case mix

• Documentation patterns

• Physician behavior

• Coding practices

• Quality and compliance expectations

This tuning enables AKASA to deliver stable reasoning across multiple hospitals, service lines, and encounter types, and to continually improve over time.

Read more about the AKASA Platform.

5. Built for defensibility 

Health systems are not just trying to improve accuracy. They’re trying to protect audit defensibility in an environment of rising scrutiny.

AKASA was designed with that in mind.

Our platform provides:

• Clinical evidence citations

• Transparent reasoning steps

• Rationale for CC/MCC and POA

• Alignment with guidelines

• Support for compliance workflows

• Consistency across reviewers

This is why compliance officers and HIM directors often become some of our strongest champions. Because the output isn’t just accurate — it’s defensible.

6. Fits into existing workflows

Adoption is where most solutions fail.

• If a solution requires workflow redesign, adoption slows or fails

• If it adds noise, reviewers reject it

• If it increases the workload, teams burn out faster

AKASA avoids all three.

The platform overlays Epic- and Oracle-based workflows, supporting staff, not changing how they work.

Coders and CDI specialists see clarity, not noise.

Leaders see value, not friction.

That’s why health systems describe AKASA’s rollout as “lighter than expected” — and why our roadmap resonates across organizations.

7. Predictable implementation with early measurable value

One of the biggest barriers to adopting GenAI is uncertainty. Will it work at our health system?.

AKASA addresses this with:

• Structured data onboarding

• A retrospective review

• Early leadership insights

• Evidence-based tuning

• A model that improves before go-live

Health systems consistently say the retrospective review is a turning point because the value becomes tangible before formal rollout.

8. One clinical foundation across CDI and coding

Many solutions still treat CDI and coding as separate processes, which leads to:

• Conflicting interpretations

• Duplicated work

• Inconsistent reasoning

• Rework loops

AKASA provides a single reasoning engine and a single clinical picture for both teams, dramatically improving alignment and reducing back-and-forth.

This is one of the most appreciated benefits among HIM leaders.

“If we can get it right the first time, before the bill goes out, everything downstream becomes easier.”

~ Jeff Francis, CFO at Nebraska Methodist Health System

Find out how AKASA is helping Cleveland Clinic align its CDI and coding teams with AI in this ACDIS webinar.

9. A partnership model, not a handoff

GenAI in healthcare is not plug-and-play. It requires ongoing tuning, clinical alignment, evidence review, and operational coordination.

AKASA teams stay engaged across:

• Implementation

• Model refinement

• Reviewer validation

• Service line expansion

• Performance reporting

• Quality and compliance needs

This is why leaders repeatedly describe working with AKASA as a “true partnership” — not a technology transaction.

Read about one of our long-term partners and why they work with AKASA.

10. Built for what comes next

The mid-cycle is only getting more complex. Documentation is growing. Scrutiny is increasing. Expectations for accuracy are rising.

AKASA isn’t building tools. It’s building a foundation for:

• Evidence-based reasoning

• Enterprise-scale accuracy

• Defensibility

• Interoperability with new data pathways

• Future modules that extend upstream and downstream

Health systems choose partners who can evolve with them, not just deploy a point solution.

Choosing the right partner matters more than ever

As documentation grows more complex, staffing grows tighter, and payer scrutiny increases, the mid-cycle has become one of the most important levers in healthcare operations.

GenAI brings a new level of clarity and consistency, but only when paired with a partner who understands the clinical, operational, and compliance realities of your organization.

That’s why leading health systems choose AKASA.