< + > Healthcare IT History Doesn’t Repeat, but It Does Rhyme

We all know the common phrase that history repeats.  I get where it comes from, but I prefer to say that history rhymes.  It’s close to the same, but there are always nuanced differences with the next iteration that looks and feels very much the same.  I think we’re going through that right now with AI.  It’s something I’ve been talking a lot about in the healthcare AI keynotes I’ve been giving along with on podcasts and panels that I’m on.  Here’s the classic cycle we’re going through right now with healthcare AI.

Every healthcare CIO wants to have one AI platform, but right now they have no choice but to choose best of breed AI solutions.

If you’re a CIO, you know exactly what I mean.  You’ve been tasked with AI innovation by your board.  Ideally, you’d love to buy one AI platform that you implement at your organization and it satisfies all your needs.  Unfortunately, no platform exists (yet?).  If you want to leverage AI, you’re going to have to use the best of breed AI solutions out there and bet on which ones will become the AI platforms of the future.

As I mentioned, we’ve been through this before.  When we first implemented health IT in healthcare organizations, we had this exact same challenge.  Organizations had no choice but to implement a wide variety of best of breed solutions.  There was no all-in-one platform.  They implemented an accounting system, an EMR system (renaming it to EHR came later), a lab system, a pharmacy system, etc.  This worked fine, but implementing this many systems came with overhead.  Plus, once you wanted those systems to communicate with each other, the management became a nightmare.

What happened next is a preview of what’s to come with AI in healthcare.

Little by little the EHR vendors starting rolling out their own solutions that solved more and more of the software needs of a healthcare organization.  I can still remember the conversations that Epic Beaker wasn’t as good as the dedicated LIS (Lab Information Systems) solutions out there, but it wasn’t too awful.

You can imagine the conversations that happened next.  Why do we have 2 vendors?  Why don’t we just pay 1?  The integration will be better if it’s the same system.  We won’t have to worry about the finger pointing between vendors.  etc etc etc.  I’m sure this will bring back many memories for people that lived it.

Long story short, most healthcare organizations got rid of the niche solutions, which were actually better solutions, and went with the all-in-one EHR vendor so that they could have fewer vendors and a solution that was fully integrated.  All of this led to many organizations’ policies of EHR only or at least EHR first as they evaluate solutions.

The problem with healthcare AI is that it moved so fast that the EHR vendors couldn’t keep up.  The number of AI solutions in healthcare right now is mind boggling.  In fact, it’s probably the hardest IT challenge that healthcare organizations have faced in a while.  Where do they start and which are the best solutions to implement today.  The AI solutions are coming out so quickly that even with every EHR vendor announcing a roadmap of hundreds of AI applications in their system, there is still a ton of opportunity for healthcare AI solutions to do something the EHR isn’t doing.

Thus, we’ve entered the part of the cycle where healthcare CIOs have to decide to sit out or implement the best of breed healthcare AI solutions out there.  The problem with sitting out is that your organization will miss out on the benefits that AI could bring them today.  Plus, there’s a lot of learning that happens when you start using a new technology in your organization.  Those healthcare AI “reps” create a lot of value for an organization as it continues to evolve.  It’s hard to see where AI is headed and how it can benefit your organization watching from the sidelines.

Thus, every healthcare CIO and the associated AI governance committee is putting together processes and procedures to evaluate and implement AI solutions in their organization.  That’s a good thing because it’s going to drive a lot of value.  However, history teaches us that a few years from now, we’ll be sunsetting a number of these AI solutions and opting for the all-in-one AI platform.

Will there be one AI platform to rule them all?  Will the EHR be the one AI platform?

I can’t imagine anyone thinking the EHR won’t be one of the major AI platforms that healthcare organizations use.  However, it’s hard for me to imagine a scenario where the EHR is the only AI platform for healthcare organizations.  It’s probably in their best non-monopolistic interest to not be the only AI platform too.

I personally think that hospitals and health systems will have a half dozen different AI platforms that are based on very specific areas of their organization.  It’s not hard to imagine having an RCM AI platform that handles all of your revenue cycle management needs.  It seems obvious to me that there could be a radiology specific AI platform that does all your radiology AI.  I could imagine a whole back office AI platform for hospitals and health systems.  My point is that this time I think we’ll see consolidation of AI onto platforms, but I don’t think we’ll see one monolithic AI platform that covers every AI need of a hospital or health system.  On the independent ambulatory side, the EHR vendor may be the AI platform for that space, but we’ll see how that plays out as well.

What do you think?  How do you think the healthcare AI market will play out?  Do you see the same classic tech cycle playing out like it has before or will there be some unique nuances?  Let us know on social media.

< + > Optura Secures $17.5 Million Series A from Salesforce Ventures and Echo Health Ventures to Scale its ROAI Platform

Generates Over $120M in Value, 700% ROAI for In-Flight Initiatives, and Over 250 Use Cases for Multi-Million-Dollar Healthcare Organizations

Optura, the enterprise healthcare platform that delivers ROAI (Return on AI Investment), today announced a $17.5M Series A led by Salesforce Ventures, with participation from Echo Health Ventures and continued investment from Susa Ventures, Matrix Partners, and HC9 Ventures, bringing total funding to over $25M to date. The investment follows a momentum-driven seed round and validates what the market is already signaling: healthcare’s AI moment has shifted from proof-of-concept to performance. Optura’s platform is built for exactly that – to help organizations quickly assess AI business value and viability, prioritize AI investments, and provide clear visibility into an AI’s enterprise impact and return in real time.

“The hundreds of AI use cases coupled with the introduction of foundational models into healthcare markets, like Claude for Healthcare, are driving AI spend and increasing risk,” said Andy Fanning, Co-Founder and CEO at Optura. “It has also created a never-ending menu of point solutions, without an objective framework to help healthcare leaders measure the ROI on their AI investment decisions. We developed Optura to take out that guesswork and help healthcare organizations objectively measure results.”

Rapid AI Adoption Exposes Gap in the Ability to Measure Value

Healthcare doesn’t have an AI adoption problem. It has an AI results problem. The industry will spend over $18B on AI this year, 46% of all healthcare investment. Yet, often the AI isn’t a fit – 95% of enterprise GenAI pilots have produced no measurable value. As margins compress and economic headwinds intensify, the cost of chasing AI hype without accountability isn’t just strategic risk; it’s existential.

“Healthcare organizations are under growing pressure to move beyond AI experimentation and deliver measurable business outcomes,” said Katie Thiry, Managing Director at Salesforce Ventures. “Optura is helping customers bring greater rigor and visibility to AI investment decisions – identifying high-value use cases, measuring impact, and accelerating time to ROI. As enterprises look for more disciplined approaches to AI adoption, we believe Optura is well-positioned to help lead that shift.”

Since Optura’s founding last year, the company has gained significant traction with enterprise health plans and providers, which include Independence Blue Cross, Prime Therapeutics, and Ardent Health, among others. Today, more than $2B in AI initiatives are loaded into the platform, with $120M in tracked value at 700% ROAI on in-flight initiatives and over 250 new use cases identified. With the new funding, Optura will continue to invest in expanding AI capabilities, growing platform teams, and scaling LLM partnerships.

“Optura is solving one of our industry’s biggest challenges: helping healthcare organizations structure and capture real value from AI programs,” said Kurt Sheline, Partner at Echo Health Ventures. “Its platform delivers the combination of data insights, tools, and technology that we believe are consequential for moving into the next phase of AI transformation – accountability and orchestrated systems that deliver long-term value across the entire enterprise.”

Optura helps healthcare organizations identify and prioritize AI use cases quickly and with minimal risk, taking a disciplined approach to measurement, evaluation, and value creation. The ROAI platform:

• Systematically maps an organization’s existing data, regardless of fragmentation, into a unified knowledge layer so every decision is grounded in how that organization works
• Scores and ranks use cases against organizational priorities, cost, and readiness
• Translates the top priorities into AI agents, trained specifically for healthcare and built directly from workflows and SOPs
• Simulates the expected return so organizations can determine the projected value before deployment
• Deploys the AI agents across the enterprise, tracking outcomes, initiatives, and projected value in one unified, real-time dashboard to provide clear visibility into AI impact

“The question for health plans is no longer whether to invest in AI; it’s whether those investments are actually delivering better outcomes for members,” said Michael R. Vennera, Executive Vice President and Chief Strategy, Technology, and Operations Officer at Independence Blue Cross. “Optura answers that question for an organization like IBX, where every decision connects back to the people we serve; that kind of accountability and visibility isn’t optional; it’s how we ensure AI creates real value where it matters most.”

To learn more about Optura, visit: optura.ai.

About Optura

Optura is the enterprise healthcare platform for ROAI (Return on AI Investment). Industry-trained and organization-specific, Optura is the C-suite’s secret advantage for deciding where AI dollars actually pay off. It translates leadership priorities into operational AI agents built directly from existing workflows and SOPs, simulates the returns before a dollar is committed, and tracks live performance against the projection on a unified intelligence layer. ROAI is the discipline that replaces directionless AI pilots with measurable performance, setting the modern standard for how healthcare leaders justify, fund, and govern AI investment. More than $2B in healthcare AI initiatives run on Optura today, with $120M in tracked value at 700% ROAI on in-flight initiatives and over 250 new use cases identified. Optura is trusted by leading enterprise health plans and provider organizations.

Originally announced May 14th, 2026

< + > Revenue Cycle Management Moves to the Fore as Margin Pressures Intensify

The following is a guest article by Inger Sivanthi, Chief Executive Officer at Droidal

Thin Margins Have Less Room for Administrative Waste

There is a version of revenue cycle management that most healthcare leaders know well. Claims go out, denials come back, teams work through the queue, and the cycle repeats. For years, this was simply how things ran. Not ideal, but manageable. Margins that were already thin are getting thinner, staffing shortages are not improving at the pace anyone hoped, and payer contracts are not getting simpler. Revenue cycle teams are being asked to do more with fewer people and tighter timelines. The administrative load has not decreased; if anything, it has grown more complicated.

What makes this moment different from similar conversations five years ago is that the pressure is now showing up in the numbers in ways that are harder to absorb. Nearly four in ten hospitals operated at a financial loss in recent reporting periods. Physician groups are not insulated from this either. When reimbursement timelines stretch and denial rates stay elevated, the downstream effect on cash flow is not theoretical. It becomes a staffing decision, a capital decision, a question of whether to expand a service line or hold steady.

Most Rework Starts Before the Claim is Ever Built

For a long time, RCM improvement conversations centered on the back end. Work the denials harder. Hire more AR staff. Build a better appeals process. That thinking made sense when the alternative options were limited. But the back end is, by definition, a corrective exercise; you are already dealing with something that went wrong.

The more useful question is where in the workflow things actually break down. For most practices, it is earlier than people expect. Incomplete demographic captures at registration. Eligibility checks that happen too late or not at all. Prior authorization requests submitted without the documentation that a payer requires the first time. These are not catastrophic failures. They are small gaps that compound.

A single prior authorization delay does not break a practice. But when 30% of your prior auths require follow-up submissions, the math on staff hours and delayed reimbursement adds up quickly. The American Medical Association has consistently reported that prior authorization remains one of the top administrative burdens physicians cite, and that delays affect patient care in ways that go well beyond the financial.

Knowing Where Denials Come From is Not the Same as Stopping Them

There is also something worth noting about how these problems have historically been addressed. A lot of RCM investment over the past decade went into software that reported on what happened rather than software that influenced what was about to happen. Dashboards improved. Visibility into denial trends improved. But the actual intervention still required a human to look at the report and act on it.

That gap between insight and action is where a lot of efficiency gets lost. The more recent shift, one that is genuinely changing how RCM teams operate, is systems that sit inside the workflow rather than alongside it. Not a report that tells you claim type X has a high denial rate with payer Y, but something that flags the risk before the claim is submitted and suggests what needs to change. The difference is subtle in description but significant in practice.

Fixing the Front End Frees Up the People Who Actually Know the Work

None of this eliminates the need for skilled RCM professionals. That framing is one of the more frustrating misconceptions in how healthcare technology gets discussed. What it does change is what those professionals spend their time on. Less time keying in information that could be pulled automatically. Less time on denials that could have been prevented. More time on the cases that genuinely need clinical judgment, escalation, or a phone call.

For practice administrators and revenue cycle leaders thinking about where to direct attention right now, the front end of the cycle is probably underinvested relative to how much it affects downstream outcomes. Getting eligibility right before the visit. Confirming authorization requirements before the procedure. Capturing documentation accurately before the claim is built. These are not glamorous fixes, but they are where a significant portion of rework originates. Margins are not going to recover through denial appeals alone. The practices navigating this period well are the ones treating the revenue cycle as a connected workflow rather than a series of handoffs and finding the points where small improvements have the longest reach.

About Inger Sivanthi

Inger Sivanthi is the Chief Executive Officer at Droidal, focused on AI-led healthcare revenue cycle and operational automation. With deep expertise in large language models and applied AI, he has worked with healthcare organizations to drive measurable cost efficiencies through intelligent AI agents. His work emphasizes responsible and ethical AI adoption to improve healthcare and financial outcomes at scale.

< + > This Week’s Health IT Jobs – May 20, 2026

It can be very overwhelming scrolling through job board after job board in search of a position that fits your wants and needs. Let us take that stress away by finding a mix of great health IT jobs for you! We hope you enjoy this look at some of the health IT jobs we saw healthcare organizations trying to fill this week.

Here’s a quick look at some of the health IT jobs we found:

• Senior MEDITECH Revenue Cycle Consultant – Quoris
• Chief Technology Officer, Health – eSimplicity
• Senior Director – Data Systems – Cincinnati Children’s
• Electronic Medical Records Specialist – Novant Health
• Program Director- Infrastructure and IT Security – Quest Diagnostics
• Senior Health Data Analyst – Risk Adjustment – Johns Hopkins Hospital
• Director, Configuration – Versant Health
• Mental Health Compliance Specialist – McKinley
• Senior Director/Vice President – AI Transformation & Enablement – RevHealth
• ROI Medical Records Specialist – MRO
• Project Managing Consultant – MEDITECH Revenue Cycle – Quoris
• Patient Service Representative – Mercy
• Associate Director, Health IT – Arrowhead Pharmaceuticals
• Behavioral Health Associate 1 – Acadia Healthcare
• Assistant Director IT VMO Epic – Montefiore Health System
• Analyst I, Healthcare Data – Boston Children’s Hospital
• Associate Vice President – Methods4Insight, Data Foundry – Eli Lilly and Company
• Supervisor of IT – Tapestry (Epic) – Renown Health
• Director of Interoperability – IntellaTriage
• Associate Director, MEDITECH Revenue Cycle – Quoris

If none of these jobs fit your needs, be sure to check out our previous health IT job listings.

Do you have an open health IT position that you are looking to fill? Contact us here with a link to the open position and we’ll be happy to feature it in next week’s article at no charge!

*Note: These jobs are listed by Healthcare IT Today as a free service to the community. Healthcare IT Today does not endorse or vouch for the company or the job posting. We encourage anyone applying to these jobs to do their own due diligence.

< + > 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.