< + > Addressing Interoperability and Data Standardization Challenges that Continue to Hinder Payer-Provider Integration

We have come a long way in improving the world of healthcare – largely thanks to improving interoperability and data standardization. However, the work is not yet complete. Every day, we continue to push forward, think, and try out new things. This push can either lead to a new solution that further improves healthcare, or it can lead to new challenges or further exploit old challenges. So what does this look like in terms of payer-provider integration? What challenges still linger, and what is being done about them?

We reached out to our brilliant Healthcare IT Today Community about this, and asked — what interoperability or data standardization challenges continue to hinder payer-provider integration, and how are they being addressed? Below are their responses.

Robert Connely, Global Industry Market Leader, Healthcare at Pega
While data standardization and interoperability are critical, the underlying problems often have more to do with data sharing rights and consent to access the data beyond integration. This is where programs like Value-Based Care offer unique opportunities to enable informed consented relationships between patients and providers to engage and share data. This is a big part of care management systems today. They are continuing to evolve and becoming core capabilities with modern orchestration platforms.

Ben Maisono, SVP Strategy at Tendo
Despite progress, interoperability challenges remain significant—particularly around data completeness, standardization, and workflow integration. A major issue is that clinical data and claims data are often structured differently and captured for different purposes. Even when organizations exchange data via FHIR APIs or HL7 feeds, the underlying variation in coding practices, documentation quality, and system configurations can limit usability. Another ongoing challenge is patient matching across systems, especially when data is coming from multiple provider networks or community-based organizations.

To address these barriers, many organizations are investing in: data normalization and governance frameworks, industry-wide adoption of FHIR-based exchange, trusted health information networks and exchanges, and improved consent management and identity resolution tools. The focus is shifting from simply “sharing data” to ensuring the data is meaningful and actionable at the point of care.

David B. Snow Jr., Founder and Head of Value-Based Care at Cedar Gate Technologies, an IQVIA business
Data interoperability is an ongoing challenge in healthcare. In a competitive industry where everyone wants to shield their own data—while also adhering to strict privacy and data protection laws—the data sharing barriers are extensive. Federal initiatives like FHIR and HL7 are aimed at making it easier to exchange data across disparate systems, but like many healthcare initiatives, government mandates won’t be enough. Private industry must step in and develop enterprise data capabilities that can bring it all together effectively.

It is a monumental task. Effective data sharing and interoperability requires tools to pull from hundreds of data sources into one single place, as well as the ability to cleanse, enrich, and normalize the information into a single, usable format. The good news is that we now have the capabilities to do it at scale—resulting in accurate, homogenized data that both payers and providers can trust to implement complex VBC and risk-based models. As healthcare IT advances, the process of creating effective, integrated data systems will become even faster and more accurate, and the ability to scale up to handle extensive datasets in healthcare will increase.

Maxim Abramsky, Vice President of Product Management at Cotiviti
Today, payer-provider integration is hindered by basic misalignments. Direct API-to-API communication between many payer systems and provider EMRs is limited, and when these systems do talk to each other, the underlying data models and workflows aren’t always aligned. That means what should be “standard” exchanges in theory can behave very differently in reality. Intermediaries (clearinghouses, proprietary networks, and vendor-specific gateways) often introduce extra translation layers, fees, and operational constraints, which can increase implementation complexity and reduce end-to-end transparency.

These issues are being addressed through broader adoption of HL7 FHIR + SMART-on-FHIR, payer/plan-facing interoperability gateways, and evolving regulatory initiatives (e.g., CMS Interoperability and Prior Authorization rules). These approaches encourage more real-time, standards-based exchange and reduce reliance on manual processes like fax.

Overall, the industry continues to move closer to standardizing both EMR capabilities and payer interoperability layers. As technology advances, perspectives shift, and the results of streamlining begin to speak for themselves. As organizations increasingly evaluate and adopt standardized, API-driven exchange, there is potential for improved adoption and reduced administrative burden across the industry.

Paul L Wilder, Executive Director at CommonWell Health Alliance
Trust in interoperability is still the biggest barrier to seamless payer-provider integration. Other barriers include confusion around standards and what to use where, from CMS’s FHIR-based APIs to HEDIS and other quality programs that require document-based exchanges. Payers who want to be ahead of the curve should take action now, testing and scaling when and where it most benefits their needs. Getting involved earlier also helps them more strongly influence future roadmap development. Once trust is earned, interoperability will serve as the essential platform that brings together not only payers and providers, but also patients and public health initiatives.

Marie Mitri, Director of Business Development at Navina
While standards such as FHIR have significantly improved system connectivity, challenges remain around data completeness, timeliness, and reliability. Payer data often reflects claims-based information with inherent lag, while provider documentation can vary in depth and structure, which limits how actionable shared data is at the point of care. Addressing these gaps requires reconciling discrepancies between clinical and claims data and ensuring information is presented in a way clinicians trust and can act on. Organizations making sustained progress are those that focus not just on exchanging data, but on improving its accuracy and usability within everyday clinical workflows.

Ashley Basile, Chief Product Officer at Availity
Despite progress with interoperable data standards like FHIR, payer-provider integration is still challenged by inconsistent data quality and varying implementation standards. Even when standards like FHIR are in place, differences in implementation and incomplete clinical context can limit the real-world usability of data. The industry is addressing this by focusing less on point-to-point integrations and more on shared networks, normalization and translation layers that sit between source systems and end users, and trust frameworks that scale. Success depends on translating standards into usable, action-oriented data that fits naturally into payer and provider operations and shifting from simply “moving data” to making data trustworthy, usable, and actionable across the ecosystem.

Denis Whelan, CEO at Documo
One of the biggest barriers to stronger payer-provider integration isn’t the lack of APIs or standards – it’s the reality that so much critical information still arrives as unstructured documents. Prior authorizations, clinical notes, and supporting records often come in different formats, across different channels, with inconsistent fields and varying levels of completeness. Even in highly digital environments, that variability creates friction.

Interoperability works best when data is already structured. But when information enters the system as a PDF or scanned document, someone has to interpret and standardize it before it can move cleanly between systems. That manual orchestration between systems is where delays and errors often begin.

Intelligent Document Processing and automated document workflows help close that gap by turning unstructured content into structured, validated data at intake, then orchestrating the flow of that content across systems. By standardizing information as it enters the workflow, organizations strengthen data integrity and create a more reliable foundation for meaningful integration between payers and providers.

Hamid Tabatabaie, Founder and CEO at CodaMetrix
Interoperability isn’t failing because we lack APIs. We already have standards to exchange data. The real issue is upstream. Every provider configures their EHR differently — templates, workflows, mappings, definitions of quality. Clinical documentation is narrative and contextual. Revenue cycle data is abstracted and financially optimized. By the time data flows through “standardized” interfaces, it’s structurally clean — but semantically misaligned. We’re exchanging data, not shared meaning. The future isn’t just better pipes. It’s objective frameworks that normalize variation at the source and translate clinical nuance into consistent, high-integrity coded data. When definitions align, interoperability becomes shared understanding. And that’s when integration actually works.

Elevsis Delgadillo, SVP, Customer Success at KeenStack
The biggest challenge is not whether systems can connect, but whether organizations are willing to break down legacy silos. While the technology exists to unify clinical, claims, lab, and social determinants data, integration requires a commitment to shared standards and coordinated infrastructure across payer and provider environments.

Monte Sandler, Chief Operating Officer at WebPT
The transaction formats and EDI structures have not meaningfully evolved in decades, and they were not designed for today’s level of automation. The bigger challenge is the complexity layered on top, including multiple payers, unique rules, benefit designs, and coding requirements. Rather than waiting for new standards, providers and health IT organizations are using AI to interpret and operationalize the existing data more effectively. The advancement is in how the data is processed, not in the data itself.

Joanna Engelhardt, VP of Product Management at Health Gorilla
Despite progress, payer–provider integration is still slowed by inconsistent data standards, including variable data quality and fragmented governance. Organizations addressing this successfully are prioritizing shared standards and disciplined interoperability processes. Stable, trusted exchange is so much more than a technical requirement. It’s foundational to care coordination, payment accuracy, and patient trust.

Hilla Fogel, Ph.D, Founder and CTO at QuantalX Neuroscience
Interoperability between payers and providers continues to face several challenges. While standards such as FHIR have gained traction, adoption remains inconsistent, and implementation varies across systems. Even when data is successfully exchanged, semantic differences in coding, terminology, and documentation can limit true interoperability and usability. Many organizations still rely on legacy, siloed infrastructure that was not designed for modern, API-driven integration. Data quality issues, including incomplete, inconsistent, or unstructured information, further reduce reliability for analytics and quality reporting.

Additionally, privacy regulations and evolving consent requirements introduce operational complexity. Addressing these challenges requires more than technical upgrades; it demands stronger governance, standardized implementation practices, and greater alignment across the payer–provider ecosystem.

So many great insights here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

What interoperability or data standardization challenges do you think continue to hinder payer-provider integration? How do you think they are being addressed? Let us know over on social media, we’d love to hear from all of you!

< + > How Axia Women’s Health Cured Recall Anxiety and Payment Friction

In healthcare we often implement new platforms and technologies hoping for a better experience, only to discover we missed the human element entirely. Technology should reduce anxiety and awkwardness. When it fails to do that, you have to ask why.

Healthcare IT Today sat down with Kate Steele, Director of IT Applications at Axia Women’s Health. We explored the realities of scaling digital patient engagement and how tackling provider resistance, payment friction, and documentation fatigue, with the help of platforms like eClinicalWorks, ultimately drives a smoother operational footprint.

Key Takeaways

• Overcome clinical resistance with hard evidence, not opinions. When doctors hesitate to open their schedules for online booking, turn to the data. Showing the actual root cause of no-shows (ie: it has nothing to do with how the appointment was booked) changes the conversation completely. So too does online conversion rates.
• Frictionless payments bring humanity back to the front desk. Embedding payment options directly into the digital check-in process removes awkward financial conversations from the waiting room. Most patients want to pay. No one wants to negotiate at the reception desk.
• AI scribes cures physician recall anxiety. Removing the computer screen during visits improves the patient connection. It also eliminates the burden of deciphering sticky notes at the end of a long, overbooked day for physicians.

Data Silences Scheduling Pushback

When providers push back against open access online booking, IT and operations teams often hit a wall. The best approach is not to argue. Instead, you go straight to the numbers to find the real story. Axia Women’s Health looked at their metrics after consolidating their tech stack onto the healow platform, to prove that self-scheduling did not lead to an increase in no-shows.

Steele shared exactly how they changed minds. “Whether it’s colleague-scheduled or self-scheduled, if they’re waiting more than 14 days, they’re 75% more likely to no-show.” It is that type of hard evidence that helped Steele and her team “open up schedules for self-scheduling.”

Payments Should Not Be Awkward

No staff member enjoys asking a patient for money at the front desk. Similarly, no patient wants to ask for a financial accommodation or a payment plan. By building these options right into the digital check-in workflow, organizations give patients a more dignified experience and reduce the stress on staff. It literally takes the friction right out of the waiting room.

“Payment through check-in as well as payment plans really gives patients humanity back,” explained Steele. “Patients want to pay their bills and by digitizing the process removed any awkward conversations.”

Curing Recall Anxiety with AI Scribe

Expecting a provider to remember the specific nuances of twenty different encounters by the end of the day is a recipe for bad documentation. That’s one of the pressure releases that AI scribe technology, like Sunoh.ai, which Axia recently implemented alongside eClinicalWorks, offers physicians.

Steele described the impact this way: “It eliminates the need to sit down at the end of the day and try to recall which patient is which, which sticky note was that patient about. Our CMIO calls it recall anxiety.”

The Bottom Line for Health IT Leaders

Technology deployments fail when they ignore the practical and often messy realities of the people using them. Self-scheduling is supposed to be as easy as installing software, but the reality is that there is hesitation from administrators and physicians to open their schedules. Successful health IT projects don’t try to gloss over these problems, they address them in a manner that is appropriate for the organization. In the case of Axia Women’s Health it was using evidence to prove efficacy and being aware of the awkwardness around payments.

What Healthcare IT Leaders Are Asking

How do you overcome provider resistance to online scheduling? The most effective method is to present hard data rather than relying on opinions. By analyzing no-show rates and wait times, IT leaders can demonstrate the actual root causes of missed appointments – likely not related to whether the appointment was booked online or via the phone. When providers see evidence that schedule delays cause no-shows rather than the online booking mechanism itself, they are much more willing to open their calendars.

Why is digital payment integration critical during patient check-in? Embedding digital payment options and automated payment plans into the check-in process removes awkward financial conversations from the physical waiting room. It provides a more retail-like experience where patients can handle their financial obligations privately. This reduces administrative burden on front desk staff and accelerates revenue collection.

What is the primary operational benefit of ambient AI scribing? AI scribes technology significantly reduces cognitive load and recall anxiety for clinicians. By automatically capturing the patient encounter in the background, providers no longer have to rely on memory or paper notes to complete their charts at the end of the day. This improves documentation accuracy and allows clinicians to practice at the top of their scope.

Learn more about Axia Women’s Health at https://axiawh.com/

Learn more about healow at https://plus.healow.com/

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< + > Healthcare AI Needs a Job Description

The following is a guest article by Scott R Schell, PhD, MD, MBA, Chief Medical Officer at Cognizant

Healthcare AI headlines increasingly focus on what models can diagnose, predict, or outperform. That focus, while understandable, is misplaced. The more urgent question for healthcare leaders in 2026 is not how capable these systems appear to be, but whether their roles inside clinical and operational workflows have been clearly defined.

AI is still being evaluated as if it were a clinician. In practice, it behaves much more like infrastructure. Until that distinction becomes explicit, adoption will remain uneven, trust will remain fragile, and healthcare will continue to swing between inflated expectations and quiet disappointment, sometimes with real clinical consequences. Healthcare does not need smarter tools in the abstract. It needs tools with a job description.

Why Capability is the Wrong Lens

Model capabilities are unreliable predictors of real-world impact. Diagnostic performance, benchmark accuracy, and reasoning depth demonstrate technical progress, but say little about whether a system will actually improve care delivery. Outcomes only become clear after deployment, sustained use, and exposure to operational reality.

In practice, value appears downstream. It emerges in continuity, execution, and whether daily work becomes simpler or more complex. Systems fail not because they lack intelligence, but because they introduce extra steps, new uncertainty, or governance burdens into environments already operating near their limits.

Healthcare leaders have seen this pattern before. Technologies that promise transformation while ignoring how care is delivered tend to stall and quietly disappear at the pilot stage. They perform well in controlled settings, then struggle when exposed to the variability and interruptions of real care. Evaluating AI primarily through the lens of capability repeats this mistake.

The question is not whether AI can think. It is whether it can fit.

What the Job Actually Is

When viewed clearly, the job of healthcare AI is neither mysterious nor philosophical. It is clinical-adjacent infrastructure designed to support rather than replace human judgment. That role may be narrower than current rhetoric suggests, but it is far more durable.

Synthesis and Compression

Healthcare generates more information than any individual or team can reasonably process. AI can compress long histories, reconcile competing signals, and surface decision-relevant views without stripping away nuance.

Translation Across Domains

Clinical, operational, and financial perspectives often speak different languages. Much of healthcare friction lives in these handoffs. AI can help information move between these domains without distortion, from bedside to operations to reimbursement and back again.

Exposure of Uncertainty

Medicine is probabilistic by nature, yet many AI systems attempt to flatten uncertainty in the name of confidence. AI earns trust when it surfaces ambiguity, highlights gaps in evidence, and clarifies where clinical judgment is still required.

Decision Support, Not Decision Replacement

At this stage, the goal should not be autonomy but assistance. Systems should frame options, stress-test assumptions, and reduce cognitive load while leaving accountability where it belongs.

None of this requires AI to behave like a clinician. It requires AI to behave like an infrastructure that understands clinical reality, including its limits.

Governance is Part of the Job, Not a Constraint

Governance is often treated as something imposed after deployment. In practice, it is inseparable from usability. Explainability, auditability, and traceability are not abstract compliance ideals. They are operational requirements for trust at scale.

Systems that cannot clearly show their work will not survive long in healthcare. They fail under clinical scrutiny, executive oversight, or board-level review. This is not resistance to innovation, but how complex, high-risk industries protect themselves.

Human-in-the-loop design is how safety and adoption coexist. Oversight does not slow progress when it is engineered into the system. It enables progress by making behavior predictable, reviewable, and correctable over time.

For health system leaders, governance is not a brake on scaling AI. It is the mechanism that allows scaling to happen without eroding trust.

When AI is Doing Its Job Well, You Barely Notice

The most effective AI does not announce itself. It fades into the background.

This may appear as ambient documentation that removes cognitive load without disrupting the clinical encounter, upstream denial prevention that resolves issues before they trigger rework, or workflow connections that quietly reduce handoffs and delays rather than adding new interfaces.

The benefits are consistent: less friction, fewer interruptions, and more time spent on care rather than coordination. The absence of spectacle is not a failure of ambition. It is the signal that the system understands its role.

These are not the achievements that dominate headlines, but they endure. When AI works this way, clinicians do not talk about the tool. They talk about the day feeling more manageable and care moving more smoothly. That is how infrastructure succeeds.

From Job Description to Accountability

One barrier to healthcare AI reaching scale is that leadership focuses on what systems can do rather than what they are responsible for. Capability is interesting. Accountability is decisive.

Clear roles, defined scope, and auditable behavior matter. Approaching AI from a workflow-first perspective is how the industry moves beyond experimentation toward durable capability.

The next phase of healthcare AI will not be defined by autonomy. It will be defined by accountability. By systems that reduce friction, expose uncertainty, and earn trust through everyday use.

That is not a limitation. It is the job.

< + > Leading Pharmacy Consulting Firm PayerAlly Joins HealthScape Advisors

The New Partnership Strengthens End-To-End Payer Advisory Services and Advances Integrated, Outcomes-Driven, Total Cost-of-Care Solutions

HealthScape Advisors, a leading payer advisory firm and a Chartis company, today announced its acquisition of PayerAlly, an independent pharmacy consulting firm specializing in pharmacy benefit management (PBM) strategy, procurement, and optimization.

The acquisition enhances both HealthScape’s and PayerAlly’s ability to help health plans and progressive employers address one of the fastest-growing and most complex areas of healthcare spending, prescription medication costs, while advancing a more integrated, clinically informed, and member-centered total cost-of-care approach.

“Payers are under increasing pressure to manage rising pharmacy costs while improving outcomes and member experience,” said Dan Delaney, Managing Partner at HealthScape Advisors. “PayerAlly brings deep, specialized expertise in PBM and specialty pharmacy strategy that complements our broader payer capabilities. Together, we can deliver more integrated, data-driven solutions that help clients improve affordability, enhance clinical value, and optimize total cost of care in a rapidly evolving healthcare marketplace.”

Founded in 2022, PayerAlly has rapidly emerged as an independent, disruptive force in the pharmacy and medical drug cost management advisory space. The firm delivers integrated procurement services spanning PBM, specialty pharmacy, and infusion, and medical drug management, complemented by a differentiated suite of oversight capabilities, including auditing, vendor performance management, and data-driven clinical and pharmacy strategy. This suite of services enables clients to maximize value across their pharmacy spend.

PayerAlly leaders Will McHugh, Bill Guerci, and Kerri Tanner will continue to manage the business as PayerAlly, a division of HealthScape, maintaining its specialized focus while benefiting from expanded technological capabilities, scale, and continued investment.

“We built PayerAlly to deliver independent, high-impact consulting in a market that demands rigor and innovation, challenging the traditional siloed approach and helping clients address rising medication costs through an emphasis on total cost of care,” said McHugh. “Joining HealthScape enables us to accelerate that mission, combining our analytics and pharmacy expertise with their deep payer insight to develop more scalable, technology-enabled solutions that drive meaningful clinical, operational, and financial outcomes.”

Together, HealthScape and PayerAlly will offer a comprehensive suite of services spanning medical and pharmacy strategy, enabling clients to better manage high-cost drugs, optimize rebate and pricing strategies, and implement more effective PBM programs that balance affordability with innovation and access. The combination also creates new opportunities to develop advanced analytics, integrated data models, and technology-enabled tools to support next-generation, insight-driven payer strategies.

About PayerAlly

PayerAlly is a private, independent pharmacy consulting firm with offices in Orlando and New York City. They offer clients access to subject matter experts in PBM services, infusion services, medical drug management, specialty pharmacy, clinical pharmacy, PBM underwriting, and the broader pharmacy supply chain.

About HealthScape

Every payer strives for better. At HealthScape Advisors, we help them achieve it. We partner with payers to deliver better member experiences, build better provider partnerships, and realize better health outcomes. We work with health plans and payers, ancillary and specialty health organizations, and healthcare investors and innovators to accelerate strategic growth; advance care quality, accessibility, and affordability; optimize operations; communicate with purpose and unlock the power of data. Our expertise across thousands of projects means we understand the landscape like no one else. When payers want meaningful results, they turn to HealthScape Advisors—the specialists who’ll help them get to better. Learn more at healthscape.com.

About Chartis

The challenges facing US healthcare are longstanding and all too familiar. We are Chartis, and we believe in better. We work with more than 1,900 organizations annually to develop and activate transformative strategies, operating models, and organizational enterprises that make US healthcare more affordable, accessible, safe, and human. With more than 1,450 professionals, we help providers, payers, technology innovators, retail companies, and investors create and embrace solutions that tangibly and materially reshape healthcare for the better. Our family of brands—Chartis, Jarrard, Greeley, and HealthScape Advisors—is 100% focused on healthcare and each has a longstanding commitment to helping transform healthcare in big and small ways. Learn more.

Originally announced May 11th, 2026.

< + > Is Being a “Disruptor” Still a Good Thing? A Look at the 2026 Healthcare Disruptors List

In 2026, is it a good thing to be labeled a healthcare disruptor, or does that label just set you up for failure? For the past several years, Alan Shoebridge, AVP & Chief Communication Officer (National) at Providence, has been publishing a highly anticipated “Disruptors List,” tracking which organizations and trends are moving the needle in healthcare (and which are just hype).

In a recent sit-down with Healthcare IT Today, Shoebridge unveiled his 2026 list, breaking down why heavyweights like AARP and direct-to-consumer (DTC) primary care made the cut, why CVS got the boot, and why vaccine hesitancy is forcing its way back into the conversation.

Core Insight: Shoebridge believes that true disruption in healthcare isn’t about flashy tech; it’s about the very real policy, labor, and social shifts that affect how care is delivered and consumed.

The New Additions and the Danger of Hubris

When you look at the landscape, it’s easy to get distracted by companies claiming they will single-handedly fix the industry. Shoebridge takes a pragmatic approach, heavily discounting organizations with too much hubris. This is exactly why CVS fell off this year’s list. “We’re gonna revolutionize healthcare. We’re gonna fix it all. We’ve got it figured out,” Shoebridge noted of their past grandiosity, adding that ultimately “their vision was just not realized” and they remain “far away from that” goal.

Instead of tech panaceas, Shoebridge looks at raw influence. Take the AARP, a new addition for 2026. While not a flashy tech startup, they are “very visible advocates” leveraging massive political and financial sway on behalf of the baby boomer generation. Similarly, DTC primary care is generating buzz. Shoebridge remains cautiously optimistic about its durability, noting that without a robust referral network, “the margins on primary care are just really, really poor.”

Negative Disruption and the “Waiting Room”

Not all disruption is positive. Shoebridge brought vaccine hesitancy back onto his 2026 list, framing it as a dangerous barrier to access. “We’re seeing the negative effect,” he explained. “This is bad disruption… where you have people questioning scientific evidence.” This resistance leads to preventable outbreaks that tie up valuable hospital beds and strain an already exhausted workforce.

For those companies with potential but unproven staying power, like Best Buy Health and Salesforce, Shoebridge created a “waiting room” category. These are organizations doing interesting things, but as Shoebridge stated, “we’re going to keep them out here in the waiting room until we see if they’re really committed to doing something.”

Notable Healthcare Disruptors

New Disruptors: AARP, DTC Primary Care, Vaccine Hesitancy, Home Medical Testing, Maven Clinic

Disruptors Still on the List: Amazon, Epic, Mark Cuban, Microsoft, SEIU, Staffing Shortages

Disruptors Leaving the List: CVS

Waiting Room: Best Buy Health, Hinge Health, Optum/United Healthcare, Salesforce

The overall lesson here is simple: it’s easy to announce a disruption, but incredibly difficult to sustain one in an industry that still requires a massive amount of hands-on care.

See the full list here: https://www.linkedin.com/posts/shoebridge_healthcare-hospitals-disruption-activity-7447278247471656961-Zlfk

Connect with Alan Shoebridge on LinkedIn: https://www.linkedin.com/in/shoebridge/

< + > CIO Podcast – Episode 114: ACOs and Long-Term Care with Mike Camacho

For the 114th episode of the CIO podcast hosted by Healthcare IT Today, we are joined by Mike Camacho, CEO at Sound Long Term Care, to talk about ACOs and long-term care! We kick this episode off by discussing why ACOs have been a challenge for long-term care. Then, Camacho shares what his experience and results have been from being a part of an ACO. Next, we talk about how Camacho is leveraging data, care coordination, and technology in his efforts. We also talk about where Camacho is getting his data – is it from the provider or outside sources? Next, Camacho shares how he approached engaging providers in this effort. Then, we debate how important ACOs and value-based care efforts are to the future of long-term care, as well as what we think the keys are to doing it successfully. We then conclude this episode with Camacho passing along the best piece of advice he’s received in his career.

Here’s a look at the questions and topics we discuss in this episode:

• Why have ACOs been a challenge for long-term care?
• What’s been your experience and results from being part of an ACO?
• How are you leveraging data, care coordination, and technology in these efforts?
• Where are you getting the data? Is it from the provider or from other outside sources?
• How did you approach engaging providers in this effort?
• How important are ACOs and value-based care efforts to the future of long-term care? What will be the keys to doing it successfully?
• What’s the best piece of advice you’ve been given in your career?

Now, without further ado, we’re excited to share with you the next episode of the CIO Podcast by Healthcare IT Today.

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< + > Under Pressure: Why Pre-Bill Prevention is Now Non-Negotiable in Coding and Denial Management

The following is a guest article by Ritesh Ramesh, CEO at MDaudit

Healthcare organizations are operating in an environment defined by financial pressure, regulatory scrutiny, and rapid shifts in payer behavior. Leadership teams are expected to maintain financial stability, protect compliance, and support patient care with fewer resources and less margin for error.

These trends point to a clear reality: revenue risk is no longer isolated to downstream denials or post-payment reviews. It now runs through the entire revenue cycle, spanning charge capture, documentation, coding, billing, and payer response. Organizations relying on retrospective reviews and siloed oversight are increasingly exposed to lost revenue, audit risk, and operational strain.

Payer tactics continue to evolve and grow more aggressive. Policies are becoming more complex and less predictable. Enforcement is tightening as payers rapidly expand their use of artificial intelligence (AI) to automate reviews, spot anomalies, and trigger audits. Combine that with frequent unannounced policy updates and mid-cycle contract changes, and even fully compliant claims can be denied, including 14.4 million pre-approved claims that are rejected annually.

This puts HIM and coding teams directly in the crosshairs.

The mid-cycle — documentation, coding, and billing — is where revenue is captured or permanently lost. In fact, it has become the most critical point in revenue cycle management (RCM), one that can no longer be managed effectively with reactive tactics alone.

Reactive Denial Management Strategies are No Longer Sustainable

Denial management approaches that rely on reworking rejected claims have become increasingly unsustainable. Denial volumes, complexity, and costs are rising faster than healthcare organizations can absorb, in turn driving up administrative costs, straining staff, and eroding margins.

Findings from the most recent MDaudit Benchmark Report highlight the scale of the challenge confronting HIM and coding leaders:

• Average lag days for initial claims response by commercial payers increased across all care settings.
• Both the average denied amount and denial volume increased for Medicare Advantage (MA) plans.
• Average amount of RFI and medical necessity denials increased by 70% across all settings.
• Coding-related denials increased 26% across professional and hospital outpatient settings.
• Total amount at-risk and audit requests per customer rose by 30% for external payer audits.

The rising cost of reworking denials adds pressure, now averaging $25 per claim for practices and $181 for hospitals. With denial volumes climbing, these per-claim costs quickly reach millions of dollars annually. As a result, many provider organizations must prioritize which claims to rework, leading hospitals to lose an average of $5 million annually from unresolved denials — up to 5% of net patient revenue.

These trends demand a redefined approach to revenue integrity; one centered on solutions that unite revenue growth and risk mitigation into a single, disciplined framework.

How Technology is Redefining Revenue Integrity

When revenue integrity solutions are supported by connected technology, real-time data, and automated workflows, it shifts from a reactive function to a proactive, executive-level capability. Leaders gain early visibility into risk, clearer insight into performance trends, and the ability to act before issues escalate into financial loss or regulatory exposure.

The organizations that realize the strongest outcomes are those that break down silos between compliance, coding, revenue cycle, and clinical teams. They rely on data to drive objective decision-making, prioritize high-risk activity, and align teams around shared accountability. They are also increasing their use of risk-based audits, expanding pre-bill reviews, and leveraging technology to protect revenue with foresight rather than hindsight.

For healthcare revenue cycle leaders, revenue integrity is a financial performance strategy and not just a department-level operational concern. It directly impacts operating margins, forecasting accuracy, workforce planning, and capital decisions. Therefore, executives need revenue integrity solutions that provide:

• Clear visibility into financial risk. With total at-risk revenue from external payer audits increasing year over year, early detection of risk exposure is essential. A connected revenue integrity platform surfaces high-risk encounters, denial drivers, documentation gaps, and payer patterns before they scale.
• Proactive denial prevention. Reactive denial management is costly and inefficient. Pre-bill oversight and predictive monitoring allow organizations to correct issues before claims are submitted, protecting reimbursement and reducing administrative burden.
• Stronger audit readiness and accountability. Executive teams require centralized tracking, documentation, and response management to maintain control over exposure and defend reimbursement effectively.
• Data-driven decision-making across departments. When compliance, coding, billing operations, clinical documentation, and finance operate from siloed systems, leaders lack a unified view of performance. Technology-enabled revenue integrity solutions create shared accountability, measurable outcomes, and consistent reporting across teams.
• Sustainable financial performance. Revenue growth and risk mitigation are inseparable. Executives need balanced solutions that protect margin while reducing regulatory risk.

The Influence of Meaningful—Not Artificial—AI

No discussion of technology’s role in advancing revenue integrity is complete without addressing artificial intelligence. Health system CFOs face the same paradox as payers: AI is both the problem and the most powerful solution. The organizations that move fastest from theory to execution, delivering measurable AI-driven ROI, will define their financial resilience for years to come.

Successful AI integration facilitates true collaboration between people and technology, embedding augmented intelligence as a horizontal layer throughout the revenue cycle platform rather than layering it on as an afterthought. This creates a meaningful and responsible AI framework; a deliberate, outcomes-driven approach designed to deliver measurable ROI, not automation for its own sake or AI for novelty or competitive positioning.

For example, eValuator, part of MDaudit’s AI-powered platform that automates audit workflows and interrogates pre-bill charges across facilities, specialties, and multiple EHR and billing systems, delivered more than $500 million in annual ROI by doing the heavy analytical lifting and leveraging automation while keeping humans in the loop.

What AI should not do is:

• Make autonomous decisions that bypass clinical or compliance judgment.
• Add complexity in the name of innovation.
• Replace the expertise of the professionals using it.

AI should amplify and augment expertise across every encounter, claim, and audit. It should serve the specific purpose of helping top healthcare RCM professionals work faster, see further, and make better decisions.

Revenue Integrity Redefined: From Reactive to Confident Control

The strongest outcomes of redefining revenue integrity emerge when silos between compliance, coding, revenue cycle, and clinical teams are broken down, and objective data drives prioritization, accountability, and cross-functional alignment. Case studies across multiple health systems consistently demonstrate what that looks like in practice:

• Expanded audit coverage without proportional increases in staffing.
• Measurable improvements in coding accuracy, significant reductions in denials.
• Early identification of multimillion-dollar risk exposure.
• Increased executive visibility through defensible benchmarking.

Organizations that treat revenue integrity as a mission-critical capability, supported by continuous monitoring and intelligent automation, are better equipped to handle payer complexity, workforce shortages, and regulatory demands. They move from reacting to denials to anticipating risk, from defending audits to preventing them, and from fragmented oversight to confident control.

About Ritesh Ramesh

Ritesh Ramesh is CEO of MDaudit, an award-winning, AI-powered continuous risk-monitoring platform and a trusted revenue integrity partner to healthcare organizations nationwide. As CEO, Ramesh is focused on driving growth and profitability for MDaudit with a customer-centric vision, strong team culture, and platform innovation. He has spent his entire career — which spans more than 22 years with leading professional services organizations — at the intersection of data, analytics, and emerging technologies, transforming business models across various retail and consumer-focused industries, including healthcare.