< + > Weekly Roundup – May 23, 2026

Welcome to our Healthcare IT Today Weekly Roundup. Each week, we’ll be providing a look back at the articles we posted and why they’re important to the healthcare IT community. We hope this gives you a chance to catch up on anything you may have missed during the week.

Healthcare IT History Doesn’t Repeat, But It Does Rhyme. The industry has a history of implementing best-of-breed clinical solutions, from EHRs to labs to pharmacy management, and having to clean up the mess that point solutions can leave. John Lynn said he sees the same thing happening with AI: Every CIO wants an AI platform, but the market only offers best-of-breed AI at the moment. Will a platform emerge? Read more…

How Physicians Are Using AI to Improve Care and Coding. At the recent Navina Ascend user conference, John sat down with three users that benefit from summaries of patients’ records and meeting Medicare Advantage documentation requirements, among other things. Read more… 

How Evolving Regulatory Requirements and Reimbursement Models Influence Health IT Strategy. We asked the Healthcare IT Today experts what the current environment means for providers and payers. Organizations need to emphasize accountability, collaboration, governance, transparency, and adaptability. Read more…

Interop and Data Standardization Challenges That Hinder Payer-Provider Collaboration. Improving data governance, accuracy, and usability help organizations addresses these concerns, according to the Healthcare IT Today community. It’s worth noting that often starts by breaking down data silos. Read more…

Curing Clinicians’ Recall Anxiety and Scheduling Friction. Colin Hung connected with Kate Steele at Axia Women’s Health, an eClinicalWorks customer that found success with patient-self-scheduling and AI scribing by showing physicians evidence of its benefits. Read more…

Is Being a Disruptor Still a Good Thing? Alan Shoebridge at Providence has published an annual list of healthcare disruptors for several years now. He joined Colin and indicated that policy, labor, and social shifts tend to be more disruptive than technology. Read more…

Leading Healthcare CIOs on LinkedIn. Meet the technology leaders at healthcare organizations sharing insights on AI, digital transformation, and the unique needs of rural and community-based organizations. Read more…

Life Sciences Today Podcast: Sanofi’s Rare Humanitarian Program. Bonnie Anderson at Sanofi talked to Danny Lieberman about the company’s 30-year effort to provide free enzyme replacement therapy in countries where no reimbursement pathway exists. More than 3,600 patients have benefitted to date. Read more…

CIO Podcast: ACOs and Long-Term Care. Mike Camacho at Sound Long Term Care joined John to discuss the role of data, care coordination, and technology in supporting value-based long-term care. Read more…

Why Most Health Systems Can’t Scale AI Past the Pilot. Eric Farr at BrainStorm said traditional app deployment processes don’t work for AI because models and capabilities evolve quickly. Readiness strategies for AI should be continuous, role-specific, easily audited, and tied to user behavior. Read more…

Workforce Readiness Is the Missing Link in Healthcare AI Adoption. Healthcare leaders underestimate the organizational change required to scale AI safely and effectively. Mechanisms that provide visibility, oversight, and concrete guardrails are necessary, noted Anupama Shashank at Kyndryl. Read more…

RCM Moves to the Fore as Margin Pressures Intensify. The front end of the revenue cycle is underinvested relative to how much it affects downstream outcomes, according to Inger Sivanthi at Droidal. Addressing this means reducing the gap between when insights are generated and when action can take place. Read more…

Healthcare AI Needs a Job Description. Dr. Scott R Schell at Cognizant outlined why AI needs to be evaluated like a component healthcare infrastructure and not a clinician, as that will help orgs better understand the role AI should play. Read more…

Pre-Bill Prevention Is Now Non-Negotiable in Coding and Denial Management. Revenue risk is no longer isolated to downstream denials or post-payment reviews, noted Ritesh Ramesh at MDaudit. Leaders need real visibility into risk to proactively prevent denials and strengthen financial footing. Read more…

This Week’s Health IT Jobs for May 20, 2026: Maryland-based consultancy eSimplicity is seeking a CTO for its Health division. Read more…

Bonus Features for May 17, 2026: Just 59% of healthcare orgs track the performance of their AI agents; meanwhile, 60% of nurses lack confidence in their org’s AI oversight. Read more…

Funding and M&A Activity:

• Health AI performance and ROI monitoring platform Optura announced a $17.5 million Series A funding round.
• IKS Health acquired ARAI Solutions to accelerate development of its AI stack and create its own small language model.
• Chartis company and payer advisory firm HealthScape Advisors acquired PayerAlly, an independent pharmacy consulting firm.
• Real-world data exchange vendor HealthVerity acquired Symphony Health, a commercial healthcare data business formerly part of ICON plc.

Thanks for reading and be sure to check out our latest Healthcare IT Today Weekly Roundups.

< + > IT is Easy – Fun Friday

Happy Friday everyone!  I hope you had an amazing week and are ready for a great weekend.

I’m not sure how funny this edition of Fun Friday is for people.  As a former techguy myself, this graphic below definitely hit home when I saw it: (Note: If the graphic is clipeed, you can see the full one here)

I’m still chewing on that last phrase “The Best IT Work is Invisible!”

It’s the truest statement in the world and yet it’s what makes the job so challenging.  Invisible work doesn’t get rewarded.  However, that’s exactly what you want from your IT department.  It’s even worse with healthcare security.  Ideally, you never know your security person’s name (a little exaggerated, but you get the point).  This is changing as the IT solutions make more of a clinical and financial impact on healthcare organizations.  However, even then you need to capture the data that shows the impact.  Otherwise, it will just be invisible.

What do you think of the graphic above?  Does it illustrate well much of the work IT does?

< + > Thirty Years of Free Therapy: Sanofi’s Rare Humanitarian Program – Life Sciences Today Podcast Episode 62

We’re excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today. My guest today is Bonnie Anderson, Head of Humanitarian Programs, Rare Diseases at Sanofi. For more than 30 years, Sanofi’s Rare Humanitarian Program has provided free enzyme replacement therapy to patients with lysosomal storage disorders — Gaucher, Fabry, Pompe, ASMD, MPS I and II — in countries where no reimbursement pathway exists. What started in 1991 with a single drug for a single disease has grown to cover six diseases across 100+ countries, with more than 3,600 patients treated and over 1,000 patients active today.

In this conversation we talk about what it actually takes to sustain patient access at this scale: how a physician in a country with no reimbursement initiates a request, how the program builds local diagnostic and treating capacity alongside the drug donation, and what changed in 2023 when Sanofi moved the review and approval workflow off spreadsheets and onto Bonterra’s grants management platform — cutting approval turnaround to under 36 hours. We also talk about what three decades of humanitarian access have taught Sanofi about running programs that outlast portfolio changes, M&A, and generational staff turnover.

Check out the main topics of discussion for this episode of the Life Sciences Today podcast:

• For someone who has never heard of the program, paint the picture — who is the patient, what disease do they have, and what would happen to them without this?
• The program started in 1991 with Genzyme for Gaucher disease. What was the original decision inside Genzyme that created it, and does that still hold today?
• Walk me through what happens from the moment a treating physician in a country like India or Egypt identifies a patient they think might qualify, through to the first infusion. Where does the physician meet the program?
• The program is case-by-case, country-by-country. What does “case-by-case” actually mean in practice — what are the criteria, and what kinds of requests don’t get approved?
• How does the program think about long-term commitment to a patient across decades of pharma portfolio changes and M&A?
• One of the things that stood out in the Orphanet paper is that the program builds local clinical infrastructure — advisory boards, training, diagnostics — not just drug logistics. Why was that decision made early, and what does it look like in a country today?
• Thirty years in, what does the program look like? Is it still there? Has it wound down? Has it evolved into something else as local capacity matured?
• Before 2023, the review and approval workflow ran on email, spreadsheets, and PDFs. What did a bad week look like back then, and what was the moment you knew the manual process couldn’t scale?
• You moved to Bonterra’s grants management platform in 2023. Approval turnaround is now reportedly under 36 hours. What does that delta mean for a patient and their physician?
• A grants management platform wasn’t originally built for medical access programs — it’s CSR software. Why was that the right shape for this workflow, versus a purpose-built managed access platform?
• What do you think about the cyber and privacy architecture of a program that crosses that many borders?
• What is the biggest anti-pattern in your industry of humanitarian access?

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If you work in Life Sciences IT, we’d love to hear where you agree and/or disagree with our takes on health IT innovation in life sciences. Feel free to share your thoughts and perspectives in the comments of this post, in the YouTube comments, or privately on our Contact Us page. Let us know what you think of the podcast and if you have any ideas for future episodes.

Thanks so much for listening!

< + > The Workforce Readiness Gap: Why Most Health Systems Can’t Scale AI Past the Pilot

The following is a guest article by Eric Farr, Principal and Executive Officer at BrainStorm Inc.

Health systems stuck in pilot purgatory are not waiting on better algorithms. They are waiting on a workforce that knows what to do with AI once it arrives.

At HIMSS26, a familiar pattern emerged. Health systems are no longer debating whether to adopt AI. They are debating why it is not scaling. Ambient documentation tools are live. Revenue cycle automation is running. Clinical decision support is deployed. Yet many organizations remain stuck in pilot phases, unable to move AI from controlled experiments into operational infrastructure.

The usual explanations—vendor immaturity, interoperability gaps, and budget constraints—do not fully explain it. Some of the best-resourced systems in the country are stuck alongside community hospitals with a fraction of the budget. The real bottleneck is not technology. It is people.

Most health systems deployed AI as a technology initiative and treated workforce readiness as a training checkbox. The systems making real progress treated readiness as infrastructure: continuous, role-specific, and tied to governance from the beginning.

Completion Rates Are Not Readiness

The standard approach to AI workforce preparation is familiar: buy a tool, build a training module, track completions, and report the number to leadership. It is the same model health systems have used for EHR rollouts, compliance requirements, and other enterprise technology initiatives.

For AI, that model breaks down. Capabilities change too quickly for point-in-time training to keep up. A module built in January can be outdated by March—not because the content was wrong, but because the tool has new capabilities, new risks, and new workflow implications.

More importantly, completion tells you who sat through the content. It does not tell you whether a radiologist is verifying AI-flagged findings before acting, whether a revenue cycle analyst is catching AI-generated coding errors before they become denials, or whether a clinician is using an approved AI tool instead of bypassing it for an unapproved consumer app.

That last scenario—shadow AI—is where the readiness gap becomes most dangerous. When staff do not feel confident or supported using approved tools, they find their own workarounds. In healthcare, those workarounds can involve patient data, and organizations often discover them only after something has gone wrong.

What Readiness as Infrastructure Looks Like

Organizations moving past pilots tend to share four traits.

First, readiness is continuous, not episodic. When a tool changes, staff get guidance in the flow of work—not months later in an annual refresher.

Second, readiness is role-specific, not generic. A nurse using ambient documentation faces different decisions than a coder using AI-assisted charge capture. Generic AI awareness training serves neither one well.

Third, readiness is measurable at the behavior level. Completion rates are input metrics. The better question is whether staff are using approved tools, following verification protocols, and escalating when outputs look wrong.

Fourth, readiness is auditable. Under regulatory or compliance scrutiny, organizations may need to show not just that they had a policy, but that workforce preparation translated into practice and was maintained over time. That requires evidence, not a slide deck.

The Community Hospital Problem

This issue is not limited to large academic medical centers. In some ways, it is even harder for smaller systems. A 200-bed community hospital may not have a dedicated AI governance team or a chief AI officer. It may depend on an EHR vendor’s roadmap and whatever training materials ship with the product.

But that hospital is still deploying AI into workflows that carry real regulatory and operational exposure. The compliance standard does not shrink just because the org chart does.

For smaller systems, readiness infrastructure has to be low-overhead and scalable. It has to adapt as tools change, reach people where they already work, and produce the behavioral evidence governance requires. Otherwise, organizations are left hoping a one-time training session was enough—and finding out only after a failure.

The Question That Separates Scaling from Stuck

If you want to know whether your organization is ready to move AI out of pilot, ask one question: Can you produce evidence—right now—that the people using AI in clinical and administrative workflows are demonstrably prepared, and that their preparation is being maintained as the technology changes?

If the answer is a training completion report, you are not ready. You have checked a box. The health systems scaling AI did not check a box. They built a system.

AI will continue to get faster, more capable, and more embedded in care delivery and operations. The workforce readiness gap will not close on its own. It closes when organizations decide that preparing people is as important as deploying the technology—and build the infrastructure to prove it.

About Eric Farr

Eric Farr is Principal and Executive Officer at BrainStorm Inc., which he co-founded in 2002. A Wharton MBA and 2019 EY Entrepreneur of the Year (Utah Region), he has spent more than two decades working with organizations on the gap between technology access and real workforce capability. BrainStorm helps enterprises build the human readiness infrastructure needed to capture durable value from AI and enterprise software.

< + > IKS Health Announces Acquisition of ARAI Solutions, an AI Management and Technology Company

Applied Research Expertise will Accelerate IKS Health’s Dynamic AI Transformation

IKS Health, a global leader in care enablement solutions supporting clinicians, staff, and patients at every step of the care journey, is proud to announce the acquisition of ARAI Solutions, a pioneering, innovation-driven provider in the field of artificial intelligence. With ARAI Solutions, IKS Health is accelerating the development of a full agentic AI technology stack with capabilities to create its own more efficient small language model.

IKS Health currently possesses a powerful four-layer healthcare AI stack with EMR integration, platform orchestration, trust/compliance, and AI applications. ARAI’s ontology adds a critical knowledge layer, making IKS Health the only full-stack player with proprietary domain intelligence at every level.

ARAI’s interconnected biomedical knowledge graphs, based on peer-reviewed research, feed a central reasoning engine, directly mappable to IKS Health platform outputs. Through a large language model grounding layer, several areas will see accelerated platform development and reduced dependency on third-party AI infrastructure, including:

• Autonomous coding
• Clinical decisions
• Denial prevention
• Prior authorization reasoning
• Precision medicine

“ARAI’s clinical knowledge infrastructure makes IKS Health AI operational models even more economical, reliable, auditable, and capable of reasoning,” said Sachin K. Gupta, Founder and Global CEO at IKS Health. “Uniting ARAI with IKS Health adds a valuable and complementary layer to our platform as we scale our powerful AI-driven and human-in-the-loop approach with evolving clinical and regulatory guidelines.”

By integrating its own clinical reasoning and medical ontologies, IKS Health strengthens its competitive edge and accelerates growth while delivering reliable, explainable results for regulators and healthcare organizations that foundation models cannot provide.

“It’s clear that IKS Health is poised to disrupt the health tech industry by creating AI-driven systems of action that have exponential value,” said Dr. Roland Haas, Co-Founder and CEO at ARAI. “ARAI is pleased to join forces with IKS Health as they extend into next-generation clinical and operational AI models.”

As clinical and regulatory knowledge evolves, research-backed methodologies combined with AI are shaping the future of healthcare. ARAI’s explainable AI approach converts black box AI into glass box AI and allows for predictions that are transparent, explainable, and traceable.

“For decades, we’ve constantly pushed the frontiers of innovation and set new AI industry benchmarks,” said Dr. Asoke Talukder, Co-Founder and CAIO at ARAI. “By using ARAI’s framework to fill the critical AI infrastructure knowledge layer, IKS Health will be the only full-stack player with proprietary domain intelligence at every tier.”

About IKS Health

IKS Health reduces the administrative, clinical, and operational burdens that slow healthcare down, giving clinicians and care teams the freedom to focus on delivering exceptional care. Through its Care Enablement platform, IKS Health integrates agentic AI workflows with human expertise to create smarter, more accurate operations, better outcomes, and financially sustainable growth across the care journey. Founded in 2006 and recognized by Black Book as the top provider of AI-driven RCM services, by KLAS for performance and client satisfaction, and by Google Cloud with a DORA Award for “Augmenting Human Expertise with AI,” IKS Health partners with the largest health systems, physician groups, and specialty practices across the United States. Learn more at ikshealth.com.

Originally announced May 13th, 2026

< + > 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