Points of view

The One Big Beautiful Bill Act (OBBBA) is often treated as a 2027 problem - a set of Medicaid changes that take effect down the road. That is exactly why many finance leaders risk underestimating its impact on the revenue cycle today. The heaviest provisions do land in 2027, but the coverage shift has already begun: the ACA’s enhanced premium tax credits expired at the end of 2025, and the law is projected by the Congressional Budget Office to cut federal Medicaid spending by $911 billion over a decade and increase the uninsured by 10 million by 2034, rising to more than 14 million once the subsidy expiration is counted. For CFOs and Revenue Cycle leaders, OBBBA is a live test of eligibility, coverage retention, self-pay readiness, and denial prevention.

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What is changing and when | What Is Changing And When

The clock is specific, and each provision translates into a front-end operational demand, not just a budget line:

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The eligibility provisions carry the heaviest weight: the work requirements alone account for the largest projected coverage loss, about 5.3 million people, and begin January 1, 2027, with states needing major systems and workforce changes to be ready.

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Why revenue is at risk | Why Revenue Is At Risk

Coverage churn moves accounts from Medicaid to self-pay. As redeterminations tighten and work requirements take hold, patients drop off Medicaid -and many drop subsidized Marketplace coverage as premiums rise. More than 10.3 million people are likely to lose Medicaid, and the combined uninsured increase exceeds 14 million by 2034. Those patients do not stop needing care; they arrive uninsured.

Self-pay is significantly harder and costlier to collect. Even today, McKinsey finds the revenue cycle typically costs an at-scale health system 3 to 4 % of revenue, with systems collectively spending more than $140 billion a year just to get paid. A larger uninsured and self-pay population makes collection slower, more expensive, and less certain- and pushes uncompensated care up.

Procedural disenrollment creates avoidable revenue loss. During the post-pandemic Medicaid unwinding, more than 25 million people were disenrolled, and 69% lost coverage for paperwork or procedural reasons rather than being found ineligible. A KFF survey found nearly two-thirds of enrollees had no change in income or circumstances that would have made them ineligible. Much of the coming loss is administrative, not inevitable -which is exactly why it can be protected against.

HFMA reaches the same conclusion: under OBBBA, it argues, coverage retention and accurate exemption management have become core revenue-cycle performance measures, and automated, continuous eligibility verification is now foundational to keeping records aligned with state systems and reducing avoidable disenrollments.

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What revenue cycle leaders should do | What Leaders Should Do

A strong response starts at the front end and works backward through the cycle:

• Verify eligibility continuously. Coverage can change between visits -verify in real time at every encounter and reconcile against state systems.
• Build renewal and exemption workflows. Proactive outreach and exemption tracking keep eligible patients enrolled. With work requirements verified at application and at each renewal, this is the highest-yield revenue action available under OBBBA.
• Prevent denials upstream. HFMA reports that roughly 15% of claims are initially denied, about 90% of denials are preventable, and nearly half are linked to front-end functions such as registration, eligibility verification, and authorization. Fix those defects before the claim is created.
• Strengthen financial counseling. A larger self-pay population needs earlier, clearer financial navigation and assistance screening.
• Budget now for financing pressure. The FMAP expansion incentive ends in 2026 and the provider tax safe-harbor reduction begins October 1, 2027 - quantify the effects on supplemental payments early.

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How technology should respond | How Technology Should Respond

OBBBA’s failure points are specific, so the technology response should be too. Four capabilities do most of the work:

• Real-time eligibility and coverage discovery: Checking coverage at every encounter and surfacing active coverage at registration, not after the denial arrives.
• Renewal and documentation support: Calendar-driven outreach, exemption tracking, and documentation workflows that keep eligible patients enrolled through six-month cycles and work-requirement reporting.
• Predictive denial prevention: Flagging the registration, eligibility, and authorization defects most likely to trigger a denial and resolving them before submission.
• Human-in-the-loop oversight and auditability: Automation clears the clean volume while trained staff handle exemptions, frailty, and conflicting documentation, with every decision explainable and auditable.
• Technology alone is not the answer, and the record proves it: McKinsey notes that despite billions invested in automation, most health systems still rely on manual billing work across a patchwork of loosely connected systems. Tools deliver only when integrated into governed workflows with people accountable for the exceptions - and under OBBBA, the exceptions (exemptions, documentation timing, status changes between renewals) are exactly where eligible patients are lost.

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Conclusion | Conclusion

OBBBA changes the operating environment for the revenue cycle, not just the budget. The dates are fixed, the coverage shift is already underway, and the pressure concentrates at the front of the cycle. But because so much of the loss is procedural, much of it is preventable. The organizations that act now - tightening eligibility, retention, and denial prevention before 2027 - will protect more revenue in 2027 and beyond and carry steadier margins and stronger patient trust into the years that follow.

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How we can help | How We Can Help

Vee Healthtek helps health systems protect revenue through continuous eligibility verification, real-time coverage discovery, and proactive renewal and exemption support. Our teams help keep patient records aligned with state systems, reduce avoidable coverage loss, and minimize eligibility-related denials.

By combining revenue cycle expertise, technology-enabled workflows, and operational governance, we help organizations strengthen coverage retention, improve front-end performance, and support compliant, auditable processes.

The next financial advantage in revenue cycle starts before the claim. By the time a denial appears, many of the warning signs have already moved through patient access, where coverage risk, contract terms, authorization friction, and charge accuracy shape the financial outcome long before billing occurs.

AI is turning those early signals into actionable revenue cycle intelligence. When applied across the core patient access functions, it helps healthcare leaders predict risk, prioritize action, and improve financial predictability. As a result, patient access becomes the first intelligence layer of the revenue cycle.

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Connected Revenue Cycle Intelligence Creates Financial Control | Connected Financial Control

AI applied to a single function delivers incremental gains, but AI applied across connected functions produces financial control.

Most revenue leakage happens in the space between workflows: a contract insight that never reaches scheduling, an eligibility signal that never informs authorization, or a denial pattern that never shapes charge capture. These disconnects will cost the organization no matter what technology sits on top of it.

Connected intelligence changes the operating model by allowing signals to travel across the revenue cycle:

• Contract terms inform eligibility and authorization rules in real time
• Scheduling data adjusts based on payer behavior and prior denial patterns
• Eligibility risk shapes authorization prioritization and documentation readiness
• Authorization outcomes feed charge capture accuracy and denial prevention
• Charge and denial patterns loop back to strengthen contract negotiations

When a payer changes documentation requirements for a procedure, the impact should reach scheduling, eligibility, authorization, and charge capture at the same time. Fragmented tools can’t deliver that response. Connected intelligence is what turns patient access into a financial control point rather than a series of isolated tasks.

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Health Plan Contracts | Health Plan Contracts

Consider a payer that routinely reimburses a high-volume procedure below contracted rates. Without contract intelligence in the workflow, the variance blends into normal collection noise. With it, AI surfaces the pattern as it emerges, giving finance leaders evidence to recover underpayments and stronger footing for the next negotiation.

AI operationalizes contract terms by:

• Translating complex payer language into structured, usable rules
• Comparing expected reimbursement against actual payments by service line
• Identifying payer behavior that deviates from contracted terms
• Surfacing renegotiation opportunities based on volume, mix, and performance data

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Patient Scheduling and Registration | Scheduling and Registration

A patient scheduled for a high-cost service at a location with known payer friction is a familiar risk profile. The traditional workflow discovers the problem after billing. An intelligent workflow flags it at scheduling, pulling from eligibility trends, authorization outcomes, and denial history to give the access team time to close documentation gaps before the date of service.

That early intervention depends on AI:

• Detecting data anomalies in demographics, plan selection, and guarantor details
• Aligning appointment timing with authorization and documentation readiness
• Prioritizing high-value or high-risk encounters for earlier financial clearance
• Recognizing scheduling patterns tied to previous denials

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Eligibility and Benefits Verification | Eligibility Verification

A patient may appear eligible at scheduling and still generate a denial later since eligibility is only one point-in-time view of financial risk. AI creates a more proactive approach by analyzing payer trends, historical denial patterns, and account characteristics to identify risk before care is delivered and share those insights across authorization and charge capture workflows.

A more proactive view is built when AI:

• Monitors payer response trends and coverage changes over time
• Identifies accounts with elevated risk based on historical denial patterns
• Detects coordination of benefits issues before claim submission
• Prioritizes re-verification where financial risk is highest

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Prior Authorization | Prior Authorization

Take an imaging order from a specialty where payers frequently request additional clinical notes. A predictive workflow surfaces the requirement up front, prompts the team to attach the documentation, and feeds the pattern back into scheduling and eligibility so similar encounters are prepared before friction occurs.

AI makes authorization more predictable by:

• Evaluating documentation completeness against payer-specific requirements
• Predicting cases likely to require additional information or peer review
• Analyzing historical authorization outcomes by payer, procedure, and specialty
• Prompting earlier intervention on high-risk encounters

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Charge Capture | Charge Capture

A procedure type that consistently shows missed supply charges rarely surfaces in a single encounter review. Across thousands of encounters, AI identifies the pattern, quantifies the leakage, and gives finance and operations leaders a clear path to correct the workflow. Those insights also close the loop by informing contract performance analysis and future payer negotiations.

AI strengthens charge integrity by:

• Comparing documented services against captured charges to identify gaps
• Detecting patterns where specific procedures or supplies are frequently missed
• Flagging coding and charge inconsistencies before claims are submitted
• Analyzing charge lag by department to accelerate revenue capture

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How Vee Healthtek Supports AI-Driven Revenue Cycle Intelligence | How Vee Healthtek Helps

By combining AI-enabled workflows, revenue cycle expertise, and a global delivery architecture, Vee Healthtek helps healthcare organizations reduce preventable denials, protect reimbursement, and turn patient access into a structural determinant of growth and resilience. Our approach transforms fragmented revenue cycle processes into an intelligent, connected operating model that creates stronger financial outcomes.

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Revenue Cycle Intelligence Begins Before the Claim | Before the Claim

Preventable denials, clean claim performance, authorization delays, charge accuracy, and reimbursement timing all improve when early signals are connected across the revenue cycle.

For CFOs and revenue executives, this creates a more predictable model. Patient access becomes the first place revenue risk is identified and addressed, giving healthcare organizations the ability to act while there is still time to change the financial outcome.

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Key Takeaways | Key Takeaways

• Revenue cycle intelligence begins before claims are submitted
• AI is most valuable when it connects patient access workflows rather than automating individual tasks
• Eligibility, authorization, contract management, scheduling, and charge capture each contribute early financial signals
• Connected AI helps reduce preventable denials and improve reimbursement predictability

For many healthcare providers, the real cost in Revenue Cycle Management (RCM) is not denial volume alone. It is the amount of rework the system quietly accepts as normal. A missed authorization, incomplete benefits verification, ambiguous documentation, or a claim released before it is truly ready can trigger a familiar chain of downstream activity - edits, denials, appeals, rebills, delayed cash, avoidable touches, and administrative drag. What appears as separate operational problems often traces back to the same root condition - a revenue cycle designed to recover from failure more efficiently instead of preventing failure earlier.

That distinction deserves more attention in the C-suite. Rework is rarely contained within one department. It spreads across patient access, coding, CDI, billing, denials, patient financial services, and finance. It consumes labor, blurs accountability, weakens the business case for technology, and lengthens the time between care delivery and cash realization. The issue is not a lack of effort. Most health systems are working hard. The issue is where that effort is spent. Too much of it still sits downstream, correcting work that should have held upstream.

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Black Book Research’s findings point to the same operating problem | Operating problem

The latest Black Book Research findings make the industry’s direction clear. 78% of respondents identified payer friction as a top revenue cycle stressor. 74% said denial prevention now matters more than denial management. 71% ranked prior authorization among the most significant revenue cycle bottlenecks. Taken together, those signals point to a broader reality: the central challenge is no longer only how to move claims through the system faster. It is how to reduce avoidable breakdowns before those claims ever reach the back end.

That is why the front end is now a margin issue, not merely an access issue. Registration accuracy, eligibility verification, authorization readiness, insurance discovery, and financial clearance should therefore be viewed as financial controls. When those controls are weak, the rest of the revenue cycle inherits preventable variance and then spends time and money absorbing it later.

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Patient financial performance is shaped earlier than many leaders assume | Patient financial performance

The same pattern is visible in self-pay and patient responsibility. 72% of respondents said patient balances are becoming harder to collect. That is easy to frame as a collections problem. In practice, many of those balances were put at risk far earlier. Weak estimates, late affordability conversations, inconsistent charity pathways, and confusing financial communication all increase the odds that balances age into avoidable bad debt. By the time the statement enters collections workflow, the operational failure is often weeks old.

This is one reason the traditional split between consumer financial experience and revenue cycle performance no longer holds. The two are now operationally linked. Health systems that want better cash performance cannot treat affordability, estimates, payment options, and financial navigation as peripheral features. They are part of the same system that determines whether revenue is realized cleanly or recovered painfully.

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More automation has not automatically created better performance | Automation limits

Most organizations have not ignored these pressures. They have added automation, analytics, workflow tools, bots, and AI in various pockets of the revenue cycle. Yet the returns remain uneven. Black Book Research reports that 73% of organizations use automation in at least one workflow, but 58% describe that automation as fragmented, and only 41% say it is producing measurable ROI.

If the workflow is flawed, automation can simply accelerate flawed execution. More dashboards can improve visibility without reducing breakdowns. More bots can increase task speed without improving first-pass quality. More tools can create a more instrumented system while leaving the underlying architecture of rework intact. This is why so many modernization efforts feel busy but incomplete. Activity has increased. Reliability has not always increased with it.

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The stronger model is prevention, not faster cleanup | Prevention model

The more serious strategy now is prevention as an operating model. That means authorizations completed correctly before service. Documentation clarified before billing. Claims built clean the first time. Denial intelligence translated into upstream correction rather than larger downstream recovery queues. Revenue integrity treated as a bridge across documentation, coding, charge capture, compliance, and reimbursement quality rather than as a narrow retrospective function.

For CFOs and heads of revenue cycle, this reframes the agenda. The question is no longer simply how to manage denials more efficiently. It is how to build a revenue cycle that produces fewer avoidable exceptions in the first place. That shift has direct implications for net revenue realization, labor productivity, cost to collect, and forecast reliability. It also changes how AI and automation should be judged. The market is signaling a clear preference for governed execution, not black-box activity.

Healthcare providers that outperform in the next few years are unlikely to be the ones with the largest denial teams or the widest collection of point solutions. They will be the ones that make revenue cycle performance quieter, cleaner, and more predictable because they have learned to prevent more of the work that currently has to be rescued downstream. That is what first-pass performance really means in practice: fewer surprises, fewer repeat touches, and a revenue cycle that stops fighting itself.

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How can we help | How we can help

We help healthcare providers move revenue cycle rework to first-pass performance. Through tech-enabled operations, disciplined workflows, clinical and revenue expertise, and accountable governance, we help prevent avoidable denials, strengthen front-end controls, improve coding and billing quality, reduce downstream rescues, and make cash flow more predictable - so finance, revenue cycle, clinical, and patient-facing teams spend less time fixing breakdowns and more time sustaining performance.

Hospital price transparency is often discussed as a compliance requirement that started years ago. That is exactly why many finance leaders risk underestimating its importance in 2026. The original rule may date back to 2021, but CMS began enforcing the new 2026 hospital price transparency requirements on April 1, 2026. At the same time, CMS has made clear that hospitals must ensure their machine-readable files conform to the revised standards. For CFOs and Revenue Cycle leaders, that means hospital price transparency is no longer just a policy obligation. It is now a live test of pricing data quality, reimbursement logic, and patient estimate readiness.

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What changed in 2026 | What Changed In 2026

The reason this topic is relevant now is that the operating standard changed. In the CY 2026 OPPS and ASC final rule, CMS replaced the earlier estimated allowed amount approach with more precise allowed-amount data elements. Hospitals are now expected to publish the median allowed amount, the 10th percentile allowed amount, the 90th percentile allowed amount, and the count of allowed amounts used in the calculation. CMS also updated attestation requirements and required inclusion of organizational NPIs in the machine-readable file. These changes raise the bar from posting data to proving data credibility.

That matters because these changes are not abstract regulatory edits. They are operationally demanding. The new disclosures depend on payer-specific charge logic, remittance-derived calculations, and consistent source-data governance. If those building blocks are weak, the resulting transparency file will be incomplete, inaccurate, or difficult to defend. In practical terms, the file has become a byproduct of revenue cycle discipline.

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Why this is now A revenue integrity issue | Revenue Integrity Issue

Hospital price transparency now sits much closer to revenue integrity than many organizations realize. To produce the required pricing outputs, hospitals must accurately interpret payer reimbursement, connect remittance data to item and service definitions, and maintain dependable pricing logic across contracts and plans. Those are not compliance-only tasks. They sit squarely inside the financial operating model of the healthcare provider.

For CFOs, this changes the leadership question. The issue is no longer whether the organization has a file posted online. The real question is whether the healthcare provider can produce pricing data that is traceable, supportable, and aligned with how reimbursement actually works. Revenue integrity leaders, managed care leaders, and revenue cycle leaders should all recognize that the machine-readable file now exposes the maturity of the underlying pricing and payment infrastructure.

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Why this is also A patient estimate issue | Patient Estimate Issue

The topic also matters because price transparency and patient estimates are increasingly part of the same conversation. CMS has consistently emphasized that hospital price transparency should help patients understand what care may cost before they receive it. That goal cannot be achieved if the data behind payer-specific charges is unreliable or disconnected from the estimate logic used at the front end of the revenue cycle.

When estimate inputs are weak, patients receive financial guidance that may be technically available but not practically useful. That creates avoidable confusion before service, weakens trust in financial communication, and makes it harder for organizations to improve collection performance in a consumer-sensitive way. In contrast, healthcare providers that treat pricing data as an enterprise capability are better positioned to deliver clearer estimates, more predictable conversations, and stronger patient financial experience.

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Why CFOs and revenue cycle leaders should care now | Why Leaders Should Care

This issue deserves executive attention for three reasons. First, transparency accuracy now depends on the same operational muscles required for effective revenue cycle performance: clean contract logic, accurate payment interpretation, dependable data governance, and cross-functional coordination. Second, pricing transparency now has direct reputational and patient experience implications, not just regulatory implications. Third, the organizations that strengthen pricing accuracy today will also strengthen adjacent capabilities such as patient estimates, managed care analytics, and reimbursement visibility.

Recent CMS resources underscore that hospitals should make sure their machine-readable files conform to the new requirements and use the updated templates, tools, and validator support now available. That means the work is immediate, not theoretical. Finance and Revenue Cycle Management leaders should view this as a current operating priority that belongs on the executive agenda. CMS provides updated hospital price transparency resources and tools here.

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What healthcare providers should do next | What Providers Should Do

A strong response starts with a practical assessment of pricing data readiness. Hospitals should evaluate whether remittance data, payer mappings, charge logic, and contract interpretation are robust enough to support the new allowed-amount disclosures. They should also assess whether patient estimate workflows are drawing from the same dependable source logic that underpins transparency reporting. If those processes sit in different silos, leaders should close the gap quickly.

The next step is governance. Price transparency should not be owned as a narrow website-publishing task. It should be managed as a cross-functional financial capability spanning revenue integrity, managed care, patient access, analytics, and compliance. The hospitals most likely to succeed will be those that align these functions around a shared definition of pricing accuracy and a shared accountability model for sustaining it over time.

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Conclusion | Conclusion

Hospital price transparency in 2026 is relevant because the enforcement environment changed, the data requirements became more precise, and the operational implications became much broader. This is no longer just about posting a file. It is about whether the healthcare provider’s pricing and reimbursement engine can support accurate, comparable, and patient-relevant pricing information. For CFOs and Revenue Cycle leaders, that makes transparency a finance and operations priority. The organizations that recognize this shift now will be better positioned to strengthen revenue integrity, improve patient estimates, and lead with more confidence in an increasingly data-driven reimbursement environment.

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How we can help | How We Can Help

Vee Healthtek helps healthcare providers strengthen pricing accuracy and avoid rework by aligning revenue integrity, analytics, and modular Revenue Cycle operations to support cleaner transparency data and more dependable patient estimates. When the revenue cycle is resilient, clinicians can better focus on care and patients progress with confidence.

At the HFMA Annual Conference, health system CFOs repeatedly highlighted a shared challenge: strong performance within individual functions doesn’t reliably translate into stable end-to-end outcomes. Each team optimizes its own piece. CDI, coding, utilization review, and billing all hit their targets, but the same clinical encounter gets reinterpreted differently at every handoff. With no shared logic, different rulesets and decision frameworks are applied at each stage, creating variation and rework that affect downstream financial outcomes.

That’s where performance erodes: between functions rather than within them.

This disconnect is driving the surge in CFO interest in mid-cycle AI. The goal has moved past fixing isolated workflows toward keeping decisions consistent as they travel through the cycle, so what gets decided in one process holds up in the next.

Here’s what CFOs are saying.

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Why the mid-cycle is gaining CFO attention | Why mid-cycle matters

The mid-cycle sits between clinical care and financial performance, where reimbursement is defined. Decisions made here shape downstream outcomes like denials, payment variance, and A/R, while also feeding back into upstream processes like documentation quality and authorization workflows. This is where redundancy becomes the most visible and costly. Because the same clinical encounter is evaluated by multiple teams using different rulesets, each re-interpretation introduces variation and delay as information moves between CDI, coding, utilization review, and billing.

That’s why CFOs are turning to mid-cycle AI applications to minimize rework and align handoffs. AI deployed here learns from payer behavior, clinical context, and historical resolution patterns, then applies shared logic across processes instead of as isolated, function-specific rules. This approach shifts the revenue cycle from fragmented decision-making to an integrated system that learns and adapts.

We’ve reached a point where the real constraint in revenue cycle is how consistently information, decisions, and context move upstream into patient access or downstream into reimbursement. That’s where the financial impact is either realized or lost.
- CFO, Midwest academic health system

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Mid-cycle AI use cases gaining traction | AI use cases

Conference attendees made clear that the most effective mid-cycle AI applications are not broad automation platforms, but targeted decision-support tools embedded directly into existing clinical and financial workflows.

What’s gaining the most traction are the use cases that reduce rework at key handoffs, improve the completeness of clinical information before downstream processing, and help teams operate from a shared view of the same encounter. Value is showing up fastest where AI is tightly scoped, uses the human-in-the-loop approach, and connects to measurable financial outcomes.

Four areas were repeatedly cited as where AI is already delivering results in production environments:

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1. Clinical Documentation Integrity (CDI) | CDI

AI in CDI is reducing documentation-driven denials, increasing first-pass yield, lowering coding rework, and decreasing cost per case by resolving gaps earlier in the revenue cycle, often before coding or denial activity occurs. AI surfaces risk, missing specificity, and inconsistencies, while clinicians and CDI specialists determine clinical validity and documentation actionability.

The AI making a difference:

• LLMs for documentation gaps and leakage prevention
• NLP and embeddings for denial, underpayment, and case mix pattern linkage
• RAG systems for policy- and outcome-aligned reimbursement decisions

More of the documentation issues that used to show up as coding delays or denials are being resolved earlier in CDI, which is improving first-pass yield.
CFO, Southern academic health system

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2. Medical coding | Medical coding

Coding directly drives reimbursement accuracy, DRG assignment, denial risk, and case mix stability, making it one of the most financially sensitive mid-cycle functions. AI adoption is improving coding speed, reducing denial rates, increasing throughput, and lowering cost-to-collect by reducing manual work.

Most mature AI solutions:

• Learning models trained on historical coding decisions and outcomes
• NLP for extracting diagnoses, procedures, and clinical entities from unstructured notes
• Rule-based overlays for payer-specific coding constraints and local policy variation

The coders are still making the final calls, but a larger share of their time is spent validating recommendations versus assembling the full clinical picture from scratch. The human-in-the-loop model has been a game-changer for efficiency and accuracy.
CFO, Midwest hospital

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3. Denial management | Denial management

AI insights generated through denial management can be applied upstream to improve documentation, coding, and utilization review practices, helping increase first-pass payment yield and reduce margin leakage.

Top AI investments:

• AI models that detect denial patterns across payers and care settings
• Predictive models for denial risk and preventable revenue loss
• Systems linking denial drivers to upstream clinical and coding decisions

We’re using denial data more strategically now with AI. It’s feeding back into upstream documentation and coding decisions instead of just sitting in reporting dashboards.
CFO, Southern health system

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4. Utilization review | Utilization review

Utilization review has a direct impact on authorization success rates, DNFB days, reimbursement timing, cash acceleration, and revenue predictability. AI is improving clinical completeness before payer submission, reducing preventable denials and accelerating reimbursement cycles.

Impactful AI:

• Payer rules engines encoding medical necessity criteria and authorization requirements
• Supervised learning models trained on historical authorization outcomes
• Queue optimization models for prioritizing high-risk or high-variability cases

AI is surfacing missing details earlier in the process. Fewer cases are entering the denial pipeline because utilization review is more complete before submission.
CRO, Northeast hospital

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Mid-cycle AI and the next era of revenue cycle | Next era

What’s becoming clear from CFO discussions is that revenue cycle performance is no longer constrained by the effectiveness of individual functions but by how reliably information moves across them. Mid-cycle sits at the center of that dynamic, where data is fluid enough to influence downstream financial outcomes, yet structured enough to affect what happens upstream.

For CFOs, this reframes what mid-cycle AI is actually buying. Not faster individual functions, but a system where one team’s output actively shapes the others.

Many revenue cycle partnerships begin with strong alignment and shared intent, but over time, trust thins, clarity crumbles, and transparency tails off. Solutions providers and healthcare organizations see expectations shift unevenly as communication becomes scarce and accountability starts to live between organizations rather than within them.

What gets lost in the drift is ownership. Ownership in how work is approached, how decisions are made, and how results are shared across both organizations.

During IAOP’s “Breaking Bad Outsourcing Relationships” panel, Vee Healthtek’s Mohini Dhanekula, Senior Managing Director, CDI, and Divya Devi, Client Operations Director, emphasized that ownership must be intentionally embedded into partnership design and day-to-day execution, from expectations to performance visibility. They shared that successful relationships are built on 3 self-reinforcing foundations that operationalize ownership:

Ownership and High-Performing Revenue Cycle Partnerships | Mohini Dhanekula | Divya Devi | IAOP Vee Healthtek

• Shared accountability for outcomes between the healthcare organization and the solutions provider
• Transparency that enables trust from both sides
• An operating model where both organizations remain focused on the same goals, not parallel priorities

Together, these principles create the conditions for ownership to be sustained and translated into measurable performance through the partnership.

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Revenue cycle success starts where task completion ends | Task completion ends

Revenue cycle is no longer a series of disconnected administrative functions. Front-end workflows influence denials. Documentation quality impacts reimbursement accuracy. Payer communication affects cash acceleration. Operational inefficiencies in one area quickly create financial consequences elsewhere.

In this environment, execution is the baseline expectation. Ownership is what determines whether execution translates into improved outcomes.

In revenue cycle partnerships, ownership means understanding how individual activities affect larger financial outcomes. It means identifying issues before they become problems, recommending improvements when opportunities arise, and viewing performance through the lens of business results rather than production metrics alone. The partnerships creating the most value are built around that mindset.

Clients are not buying task completion. They’re buying your confidence. The difference is between completing the task and owning the task.
- Mohini Dhanekula, Senior Director, CDI

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Strong ownership depends on transparency | Transparency builds trust

Operational friction rarely begins with a major issue. More often, it develops gradually through unclear expectations, fragmented communication, or gaps in accountability. After all, by the time it feels like a problem, it’s been a problem for too long. These signals appear when expectations around visibility are not aligned on both sides. In effective partnerships, trust is built on a foundation of mutual transparency.

On the revenue cycle partner side, this means showing what’s happening behind the curtain. Solutions providers need to make their work visible through clear reporting, consistent operational updates, and proactive identification of risks and opportunities. There can be no “black box.”

On the health system side, trust is demonstrated by focusing oversight on outcomes rather than activity. Instead of monitoring every task in a workflow, the most successful organizations concentrate on whether agreed-upon financial and operational results are being achieved, and whether performance is improving over time. This shift reduces oversight burden and gives internal teams back time to focus on higher-value priorities.

This isn’t always as easy as it sounds. Trust, after all, is a two-way street. When revenue cycle partners are transparent about execution and performance, health systems are better able to trust outcomes without the need to over-manage the process. Otherwise, oversight expands into day-to-day activity management, slowing decision-making and reducing the very capacity gains the partnership was meant to create. At that point, they have effectively outsourced the work and insourced the stress.

Trust erodes when teams are monitored for activity rather than trusted with outcomes.
- Divya Devi, Client Operations Director

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The best partnerships feel embedded within the organization | Embedded partnership

Operational performance depends on coordination across workflows, technologies, and teams. When communication is inconsistent and information is siloed, inefficiencies compound quickly.

In these cases, moving beyond a buyer-vendor mindset is essential because the strongest relationships are built on shared understanding of priorities, constraints, and definitions of success. Over time, the most effective organizations invest in deeper integration across leadership and operational teams, creating a structure where performance discussions are continuous and both sides are actively engaged in improving, not just reporting, results.

In these models, performance is not external to the organization, but embedded in the way work is executed, reviewed, and improved. You stop asking “how is the partner doing?” and start asking, “how are we doing.”

Maintaining communication effectively and regularly is very important on both sides. Clients should think of us as an extension of their staff.
- Mohini Dhanekula, Senior Director, CDI

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Building a partnership around ownership: A practical model | Ownership model

Building a partnership grounded in ownership requires intentional design from the start. It depends less on contract structure or stipulations and more on how the relationship is activated, governed, and operated day to day. Some tried and true advice:

1. Anchor on shared outcomes

Knowing the destination tends to help with the navigation. Define success in terms of financial and operational outcomes such as denials reduction, cash acceleration, coding accuracy, and A/R performance. When partners understand the “why” behind the work, they are better positioned to take ownership of results rather than simply executing tasks.

2. Establish transparency in execution to create trust

Include partners in relevant performance discussions, planning sessions, and operational reviews. When both sides are working from the same data and priorities, issues are identified earlier and improvement opportunities are addressed collaboratively rather than reactively. Shared data has a way of producing shared urgency.

3. Operate as a unified team

When both sides share priorities, constraints, and a common view of performance, communication shifts from status reporting to joint problem-solving. Over time, this creates a working model where improvement is continuous and both sides are actively engaged in driving outcomes, not just tracking them. Two organizations, one team.

In May 2026, two frontier AI platforms announced new PE-backed services ventures designed to help enterprises move from pilots to production. The signal was important: the market agrees that AI’s value depends as much on deployment, workflow redesign, and scaling discipline, plus the governance required to make automation safe and auditable, as much on model access.

This matters immediately to health systems because Revenue Cycle Management (RCM) is one of the most workflow-dense, exception-heavy operating environments in the enterprise. Revenue cycle performance is determined by how reliably information, decisions, and supporting evidence move across patient access, documentation, coding and billing, claim submission, payer follow-up, remittance, and patient responsibility. Put simply, revenue cycle does not struggle for lack of tools; it struggles when handoffs break, rules vary, and work is pushed from one queue to another.

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What revenue cycle management is | What Revenue Cycle is

Revenue Cycle Management (RCM) is the set of processes health systems use to track revenue from the patient’s initial encounter through claim submission, reimbursement, and final balance resolution. The cycle is linked end to end: a small upstream variance can become a denial, a rework loop, a delayed remittance, or a poor patient financial experience downstream.

That is why optimizing a single function in isolation rarely solves the broader operating problem. The AMA’s workflow guidance makes the same point in practical terms: revenue cycle performance depends on disciplined workflows, clean claims, checkpoints, and active follow-up across the lifecycle.

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What the services-venture trend implies for revenue cycle operations | Services-venture trend

First, buying criteria are shifting from feature lists to deployment capacity. Health systems will increasingly judge partners on integration into systems of record, practical change management, and time-to-value because implementation is now the bottleneck. That aligns with broader enterprise findings that the organizations realizing more value are the ones that redesign workflows around new capabilities, rather than layering technology onto legacy operating habits.

Second, exception-handling becomes the heart of the operating model. Payer pressures, including denials, prior authorization delays, unclear rationales, and documentation demands raise the cost of ambiguity. That means leaders need more than task automation. They need standard work, intelligent routing, evidence assembly, and clear escalation paths that prevent work from ricocheting across teams.

Third, governance moves from an IT concern to a revenue-protection discipline. As automation becomes more agentic, scaled use requires controls, monitoring, and explicit human validation points. Revenue workflows are financial and auditable by nature, so black box automation is not enough. The approach has to be traceable, governable, and explainable in day-to-day operations.

Fourth, hybrid delivery models become more attractive. The recent news about services ventures mirrors a reality revenue cycle leaders already know: the best outcomes often come from combining technology with embedded expertise that can resolve exceptions, redesign workflows, and reinforce accountability.

The competitive advantage comes from deploying intelligence into workflows with governance, so throughput improves without simply shifting work into new queues.

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A deployment-first playbook for revenue cycle leaders | Deployment playbook

Start by mapping the highest-friction handoffs rather than evaluating departments in isolation. Then standardize the inputs that create avoidable downstream volatility - eligibility, authorization, documentation readiness, coding integrity, and claim preparation.

Use automation for repeatable volume work but engineer exception handling for payer variability and clinical nuance. Build governance from day one: logs, access controls, monitoring, and human-in-the-loop rules should be treated as operating requirements, not post-implementation clean-up.

Finally, measure in CFO language. Baseline the operational metrics that matter, then track sustained movement after redesign and adoption. Durable value depends on disciplined scaling and governance is a critical enabler as deployment broadens.

A practical discipline here is metric design. Health systems should decide upfront which indicators prove that the deployment is improving the operating model: clean claim rate, denial friction, days in A/R, appeal cycle time, preventable write-offs, and cost-to-collect are more meaningful than counting automations launched. That keeps teams focused on business outcomes rather than activity volume, and it gives finance, operations, and IT a shared scorecard for discussing progress and accountability.

Rather than asking, “Where can AI automate a task?” CFOs and Revenue Cycle Leaders should ask, “Where can a deployment-ready operating model reduce friction across revenue cycle handoffs?” That is how capability turns into reliability, resilience, and financial performance.

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Decision checklist | Decision checklist

• Where can the approach integrate with EHR, clearinghouse, and payer workflows, with auditable logs?
• Where will it reduce exceptions across handoffs, not just automate one task?
• What governance controls are built in: role-based access, monitoring, and human validation?
• What is the implementation capacity to move from pilot to production?
• How does the model address payer friction with playbooks and continuous improvement?

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Bottom line | Bottom line

The real takeaway from the recent services-venture announcements is not that AI vendors are adding consulting revenue. It is that deployment is now the differentiator. For revenue cycle leaders, that means the winners will not be the organizations with the most tools. They will be the ones that can deploy intelligence into revenue cycle operations with governance, measurable accountability, and enough operating discipline to translate capability into cash.

Healthcare finance is undergoing a structural shift, and no role is feeling it more than the Chief Revenue Officer (CRO). At The Health Management Academy's Spring 2026 Chief Revenue Cycle Officer Forum in Frisco, the headline wasn’t AI theater or point solution stacking. It was role redefinition.

Revenue cycle leadership is moving out of its traditional operating lane and into a position that directly shapes how financial performance is structured from the ground up. Think less “clean up on aisle four” and more “who designed this store?” For finance leaders, this evolution changes everything about how revenue cycle gets evaluated, governed, and wired into enterprise decision-making.

The CRO role was built for a different system | Different system

Historically, the CRO function was structured around execution. Growth decisions were made, contracts were negotiated, volumes were projected, and then revenue cycle was responsible for converting that activity into cash. The model worked when the system itself was relatively stable.

Today, financial outcomes are shaped much earlier in the process. Payer contract design, reimbursement terms, clinical documentation integrity, and authorization logic are determining outcomes before claims ever reach the back end. At the same time, margin pressure has reduced tolerance for rework. There is less capacity to absorb inefficiency, and fewer opportunities to correct issues after the fact. Layer in AI, which introduces continuous decision-making across workflows, and the limitations of the previous CRO role become clear.

This operating model reshapes how CROs engage with revenue cycle partners. Partners are no longer operating at the edge of the function, but from within the workflows themselves, raising expectations around transparency, integration, and impact.

“We are not just thinking about automation of tasks. We are redefining roles, skill requirements, and oversight structures so that humans and systems can operate together effectively.” - CRO, Southwestern health system

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Why CROs are being pulled upstream | Pulled upstream

Revenue cycle is showing up earlier in strategic decisions than ever before. After all, the sooner the CRO shows up, the fewer surprises finance has to explain later.

“We’ve never been involved in conversations this early in deal structure, growth planning, payer strategy, and service line decisions. This is what’s pulling the CRO role beyond execution and into decision design.” - CRO, Southern health system

More than a marginal change in collaboration, this change reflects a recognition that financial assumptions embedded in growth decisions are increasingly dependent on revenue cycle feasibility. When a system evaluates expansion, acquisition, or new service lines, the viability of those decisions depends on payer mix and contract structure, authorization and access workflows, documentation patterns, and the scalability of existing operations. If revenue cycle isn’t at the table early, someone will probably be cleaning up the mess later.

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From execution to enterprise design | Enterprise design

The CRO role is evolving from operational management to system design. Less air traffic controller and more airport architect. That shift is being accelerated by the introduction of AI into revenue cycle workflows. Now, it’s participating in decision-making, not just executing tasks. The question is no longer about what AI can do, but about what the system lets it do safely.

If decisions within the revenue cycle are driven by automated systems, then financial performance depends on how those systems are configured, monitored, and governed. That reorients the CRO’s responsibility toward:

• Defining decision frameworks within workflows
• Establishing oversight structures for automated processes
• Ensuring outputs can be explained and validated
• Aligning operational logic with financial objectives

The transition toward broader system ownership also changes engagements with revenue cycle partners, since CROs now carry greater responsibility for ensuring solutions align with internal governance structures, support system-level visibility, and contribute to financial outcomes rather than isolated task execution.

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Enabling CRO leadership in system design | System design

As the CRO role expands into true system-level design, the focus moves beyond incremental improvement toward true alignment across workflows, decisions, and financial outcomes. But expanding scope without rethinking how the model is designed introduces new risk. Closing that gap requires a more deliberate approach to how revenue cycle is designed and operated. Vee Healthtek works with finance leaders to transform revenue cycle into a structural determinant of growth and resilience where processes, decision logic, and governance are connected from the outset. Our teams integrate global delivery architecture and proven execution with practitioner insights and engineered workflows to create a more reliable connection between strategy and financial outcomes. For CROs, this means full confidence that revenue cycle performance is consistent, transparent, and built to scale.