Friday, July 31, 2026

< + > How to Get 7 Figure Savings in Clinical Trials Without AI – Life Sciences Today Podcast Episode 72

We’re excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today. My guest today is Dr. Jurate Lusienne, Clinical Trial Optimization at Jura Lasas Consulting. Lusienne helps small biotechs run early clinical trials faster, cheaper, and with more control—without relying on big CROs or fancy AI. 

Drawing on experience across academia, CROs, and sponsor-side clinical ops in the US, Japan, UK, and Lithuania, she explains how founders can avoid expensive default decisions and design leaner, smarter trial strategies. We discuss how small teams can save 30–60% on Phase 1 and 2 trials, why sponsors remain responsible under GCP no matter who they outsource to, and the industry anti-pattern she sees most often: small biotechs choosing large CROs for a false sense of safety.

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

  • Tell me about your personal journey – how did you end up doing what you’re doing?
  • How do you create value for a biotech?
  • The decisions we make in life science companies today— whether it’s in the clinical journey or before that in the application or scientific side— tend to be ingrained in the minds of the Founder and CEO: ‘this is what I have to do.’ This brings them to a point where years down the line, they’ve lost their optionality. Do you agree with that, or is that something that you think is just highly exaggerated on social media?
  • How do you add value? What do you get people to do?
  • Let’s say I’m a small biotech and I got a quote from a big CRO. How much could I expect to save in terms of time and money if I worked with you?
  • What is your business model?
  • Why would a biotech want to work with a big CRO instead of working with you?
  • What are three things you want to do for your customers in the next twelve months?
  • What is the biggest anti-pattern in your industry today?
  • To what degree is it about learning for the biotech? When you outsource everything to a big CRO, it seems to me that the chances of a small biotech learning something new are kind of small. So maybe the secret sauce is that you’re actually helping these small biotechs learn a lot faster. Is that true?

Subscribe to Danny’s newsletter to get strategic patterns for life science leaders building a defensible business.

Be sure to subscribe to the Life Sciences Today Podcast on your favorite podcasting platform:

Along with the popular podcasting platforms above, you can Subscribe to Healthcare IT Today on YouTube.  Plus, all of the audio and video versions will be made available to stream on Healthcare IT Today. As a former pharma-tech founder who bootstrapped to exit, I now help TechBio and digital health CEOs grow revenue—by solving the tech, team, and go-to-market problems that stall your progress. If you want a warrior by your side, connect with me on LinkedIn.

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!



< + > Care Everywhere, Secure Endpoints: IT, Beyond IT, Beyond Hospital Walls

The following is a guest article by Joyal Bennison, Product Marketing Manager at ManageEngine

Every clinical device that touches patient data, whether it sits in a nursing station or in a clinician’s coat pocket, is now part of the attack surface. That reality is forcing healthcare IT to confront four fault lines at once: BYOD and shared devices, care that has moved beyond hospital walls, open source supply chain attacks, and a new class of Mythos and Frontier AI-based exploits that strike before a CVE even exists.

Start with the device in someone’s pocket. A large medical device manufacturer’s device management platform was compromised in March 2026, wiping roughly 200,000 devices and exfiltrating 50 terabytes of data. BYOD phones enrolled for clinical access were wiped alongside corporate machines. Shared, shift-based devices carry the same exposure: one compromised credential and a single admin console can put every connected device at risk.

Care has also moved past the hospital’s front door. Telehealth visits, remote patient monitoring, and home care documentation now happen on personal laptops, home routers, and tablets that IT never provisioned and rarely sees. A telehealth clinician’s home is effectively a clinical site with no managed network equipment. A home care worker’s tablet picks up whatever it encounters on a patient’s Wi-Fi and carries it back into the hospital network as a trusted device. Roughly half of health delivery organizations still lack the mobile threat defense coverage that matches their desktop security, a 30-point gap between where care happens and where security actually reaches.

The two newest frontiers sit further upstream, inside the software itself. Open source supply chain attacks now target the maintainers behind widely used npm and PyPI packages that power EHR integrations, telehealth apps, and patient-facing tools. A single stolen publish credential can push a poisoned package into thousands of pipelines within hours, and a point-in-time SBOM scan will not catch what was compromised after the last check. Meanwhile, Mythos and Frontier AI-based exploits are collapsing the exploit timeline itself. These models can discover and weaponize a vulnerability before it is ever assigned a CVE, leaving no patch to deploy and no scanner that can see it coming. In a sector where clinical downtime already limits how fast anyone can patch, a threat with no CVE at all is the hardest fault line to defend.

These four threads- BYOD exposure, care beyond hospital walls, open source supply chain attacks, and Mythos and Frontier AI-based exploits- run through Care everywhere, secure endpoints: IT, beyond IT, beyond hospital walls, a live webinar on August 11, 2026, running 60 minutes. Paddy Harrington, Senior Analyst at Forrester, and Romanus Raymond, Director of Technology at ManageEngine, unpack where the risk actually lives and what a layered response looks like in practice, closing with a CISO-ready dashboard demo.

Every clinical device is an endpoint, every endpoint is a target – learn how to close the gap. Register now for free to secure your spot.

About Joyal Bennison

Joyal Bennison is a Product Marketing Manager at ManageEngine, the enterprise IT management division of Zoho. Over eight years in the role, Joyal has built go-to-market strategy for B2B cybersecurity SaaS, spanning endpoint security, Zero Trust, and compliance, with a focus on BFSI and healthcare organizations. Joyal has spoken at more than a dozen industry events across North America, the UK, the Middle East, and India, including the HIMSS Global Conference, addressing CISOs and IT directors on endpoint security and zero-trust adoption.

ManageEngine is a proud sponsor of Healthcare Scene.



< + > Nexus Acquires Telemetrix RPM | Serelora Acquires ACTIN Care Groups’ Risk-Stratification Software

Check out today’s featured companies who have recently completed an M&A deal, and be sure to check out the full list of past healthcare IT M&A.


Nexus Acquires Telemetrix RPM, Extending the Clinical Care Operating System from the Hospital to the Home

The Combination Empowers Healthcare Organizations to Improve Outcomes, Increase Efficiency, and Bring Back the Joy of Nursing

Nexus (formerly Nexus Bedside) today announced that it has acquired Telemetrix RPM, a remote patient monitoring and chronic care management company that operates inside native Epic workflows and integrates with other electronic health records (EHRs). Together, the companies extend the Nexus operating system beyond the hospital, helping health systems keep patients connected to their care teams from the hospital to the home.

“Nexus is a movement to change clinical care for good, using technology not to replace human connection, but to make care more personal, more compassionate, and more human,” said Akram Boutros, MD, FACHE, Chief Executive Officer at Nexus and Telemetrix RPM. “Bringing Telemetrix under Nexus enables us to connect the hospital, home, and care team through one coordinated model, giving clinicians greater visibility, reducing gaps in care, and supporting patients across the entire care journey.”

Outcomes from the Nexus Bedside deployment at the University of Oklahoma (OU) Health were independently validated in a KLAS Emerging Insights Report published in April 2026. During the initial six months, OU Health reported zero falls with injury, a 67% reduction in nursing turnover, a 26% reduction in length of stay, and elimination of dual sign-off medication errors.

Telemetrix operates inside native Epic workflows under an Epic Consultant Access Agreement. This embedded EHR access optimizes Epic workflows, which is much broader than the limited standard API-based overlay model used by most virtual care companies. The new platform brings remote patient monitoring, chronic care management, cardiac AI, and clinical services into a single workflow, helping reduce disruption for care teams while making care safer, faster, and easier for patients. The platform also integrates with other EHRs.

“Under Nexus, we can provide a connected care model that follows the patient from the hospital to the home. By unifying inpatient care, remote monitoring, and chronic care management through one operating system, we can deliver more personalized care plans while embedding clinical intelligence, alerts, and remote clinical support directly into the EHR, without adding complexity for clinicians,” said Burley Wright, Chief Operating Officer at Nexus…

Full release here, originally announced July 28th, 2026.


Serelora Acquires ACTIN Care Groups’ Risk-Stratification Software

Serelora, Inc., a company building an agentic electronic health record (EHR), today announced it has acquired the clinical risk-stratification software of ACTIN Care Groups. The acquisition extends Serelora’s AI-native EHR beyond individual patient documentation into population-level analysis, giving clinicians and health systems the ability to identify, stratify, and act on patient risk across entire populations.

The acquired technology includes WellCheck, a 27-instrument preventive risk battery developed by ACTIN Care Groups to systematically assess patients across clinical, behavioral, and social risk factors. Within Serelora, these validated instruments become a native capability of the record itself, meaning risk scores can be generated, surfaced, and acted upon inside the same agentic system clinicians already use to document care.

Traditional EHRs were built to capture one encounter at a time. As an agentic EHR, Serelora is designed to do more than store what happened, reasoning over the record and taking work off clinicians’ plates. Adding risk stratification extends that intelligence from the individual chart to the whole population, allowing an organization to see not just who is sick today, but who is trending toward risk tomorrow.

“An EHR shouldn’t just record what already happened to a patient, it should help clinicians see what’s coming next, whether that’s for one patient or an entire population,” said Spencer Wozniak, Co-Founder and Chief Technology Officer at Serelora…

Full release here, originally announced July 13th, 2026.



Thursday, July 30, 2026

< + > Improving Operational Efficiency with Back Office Health IT Systems

As we try to combat healthcare burnout, improving operational efficiency is a big part of that. Like everything in healthcare, there are plenty of ways to approach an issue, so today we are going to narrow that down to back office health IT systems. We reached out to our incredible Healthcare IT Today Community to ask — how are back office health IT systems improving operational efficiency in areas such as finance, human resources, and supply chain management? The following are their answers.

Chris Luoma, Chief Strategy Officer at GHX
In the healthcare supply chain, we are seeing systems move beyond static reporting toward dynamic, decision-oriented models. This shift is helping to break down what I refer to as workflow debt: the fragmentation across systems, processes and trading partners that has long held healthcare operations back. By embedding intelligence directly into operational workflows, these platforms surface relevant signals and suggest next steps within the context of everyday decisions, turning disconnected data into coordinated action. The result is a more connected operational environment where supply chain functions are no longer reacting to yesterday’s problems, and instead they’re anticipating tomorrow’s.

Lou Fierens, Former Executive Vice President of Administrative Services at Trinity Health, and Advisor at AssistIQ
Some of the biggest supply chain efficiency gains in health systems are hiding in plain sight, but the industry is still focused on the wrong part of the problem. Procurement and inventory management tend to get most of the attention. But the harder, more foundational issue is data accuracy at the point of use, and the highest-cost items (implants, tissue, high-value disposables) are the hardest to keep accurate in the item master.

In the OR and procedural areas, documentation of what was actually consumed still relies heavily on manual capture. Intraoperative documentation error rates can be as high as 17%, rising to 38.4% when recording is delayed. And once that gap exists, every downstream system inherits it.

The lack of real-time recognition creates double and triple work across Clinical, Revenue Cycle, Accounts Payable, and Supply Chain. Each team ends up reconciling the same missing or inaccurate data from a different angle. The health systems making meaningful progress are the ones applying computer vision and AI to close the distance between what happens in the room and what the back office knows.

Denis Whelan, CEO at Documo
The real operational drag happens in what I call the “murky middle” of the back office. Nearly 70% of healthcare communications still rely on unstructured data—think paper, PDFs, and fax chaos. When your staff is bogged down by manual data entry and fragmented workflows, it creates tough bottlenecks. However, today we are seeing a massive shift.

Back-office Health IT is evolving from simple static repositories into AI-driven, automated workflow engines that drastically improve operational efficiency. For example, instead of a staff member spending 10-15 minutes per fax retyping information, AI extracts key data like patient name, date of birth, medical record number (MRN), and ZIP code and routes it directly to the correct workflow. Across hundreds of daily documents, this can save hundreds of staff hours each month, freeing teams to focus on higher-value tasks that directly impact both organizational health and patient care.

Kevin Erdal, President, Advisory Services at Nordic
Health IT behind the scenes is becoming a critical operational engine for healthcare organizations as they navigate workforce shortages, margin pressure, and rising patient expectations. Modern ERP, HR, and supply chain platforms are helping organizations standardize workflows, reduce manual processes, and improve visibility.

In finance, healthcare organizations are leveraging modern cloud-based ERP systems to automate core functions like accounts payable, procurement, budgeting, and financial reporting. This reduces administrative overhead while giving leaders more timely access to operational and financial insights that support faster decision-making.

In human resources, organizations are increasingly using integrated workforce management tools to streamline recruiting, onboarding, scheduling, payroll, and employee engagement. With ongoing staffing challenges, health systems are prioritizing technologies that help optimize labor resources, improve retention, and reduce the administrative burden of tasks like scheduling, training, or credentialing on managers and HR teams.

Ultimately, the biggest shift is that back-office systems are no longer viewed as isolated administrative tools. They are increasingly connected to broader transformation strategies focused on operational resilience, financial sustainability, and improved patient and workforce experiences.

Sarah O’Meara, Head of Product at LumApps
Patients take notice when the people helping them can’t give a straight answer about coverage, care plans, or what comes next. Our research found that when service interactions are unclear, only 16% of patients blame individual employees, while 44% blame the internal systems and tools behind them.

People want to trust that healthcare providers and staff have the tools they need to guide patients through questions that are often time-sensitive and stressful.

A connected AI employee hub gives staff one place to find accurate answers and approved resources. By digitizing processes and guidance across every team and shift, information remains consistent, and employees can find what they need fast when patients need it most.

Ajoy Ranga, Chief Digital Officer – Health Care at UST
The honest truth is that the healthcare back-office has trailed every other industry for decades, and the necessary catch-up is currently underway. On the finance side, hospitals and health plans are finally retiring on-premise general ledgers and moving to cloud ERPs such as Oracle Fusion and Workday Financials, which is letting close cycles drop from weeks to days and giving leaders the same near real-time visibility that retail and banking have had for years.

On the HR side, the wins are showing up in workforce scheduling, where the financial investment has been most significant. Mercy, the St. Louis-based health system, reported about 30 million dollars in savings in 2023 after deploying an AI-driven shift marketplace that moved its labor mix to roughly 69 percent core staff, 23 percent flexible and gig, and only 8 percent agency.

For supply chains, the work is invoice automation and contract intelligence layered on top of GPO purchasing. For example, Premier rolled its Remitra invoicing and payables platform into its Supply Chain Services segment in fiscal year 2025, which is one signal of where the industry is heading.

The pattern across all three sectors is thus a consistent push to replace human keystrokes on commodity transactions and reinvest those hours into work that actually requires judgment.

Ram Mohan Natarajan, Global Head of Business Transformation at Sagility
Healthcare organizations are increasingly moving beyond using technology simply to digitize administrative processes and are instead leveraging intelligent platforms to orchestrate work across functions. Modern back-office systems are creating greater visibility into financial performance, workforce utilization, procurement, and operational bottlenecks, enabling organizations to make faster and more informed decisions.

For example, AI-enabled finance platforms can identify payment variances, predict cash flow risks, and automate reconciliation activities. Any work done on the payment integrity space for payers in identifying overpayment will result in potential cash flows, and these need to be factored into the financial planning.

Similarly, human resources systems are helping organizations optimize workforce planning, improve recruitment efficiency, and better manage surges during enrollment season. Supply chain platforms are providing real-time inventory intelligence and predictive demand forecasting, helping reduce waste and improve resource allocation.

The greatest gains, however, come when these systems are connected to operational workflows rather than functioning as standalone applications. Healthcare organizations are increasingly focused on creating a unified operational view that allows leaders to move from reporting on performance to actively improving it.

Sharat Potharaju, Co-Founder and CEO at Uniqode
GS1 Digital Link-enabled QR codes are dramatically improving the efficiency of healthcare supply chain management. By encoding critical product identifiers, like GTINs, lot numbers, and expiration dates, into a single scannable code, these systems eliminate the fragmented data lookups that can slow down recall responses. When recalls are issued, dynamic QR codes can instantly redirect staff to updated safety information without reprinting packaging, reducing compliance risk and operational disruption across the entire supply chain.

Zack Tisch, Partner, Portfolio Services at Pivot Point Consulting
The biggest shift is that the ‘back office’ is no longer a reactive part of the business. Finance, HR, supply chain, and IT operations now directly affect access, staffing, margin, and ultimately patient care. Modern ERP, workforce, procurement, and analytics platforms are giving health systems a real-time operating picture instead of a month-end post-mortem. Healthcare cannot deliver 21st-century care on 1990s’ operational plumbing.

Greg Miller, Vice President, Marketing and Business Development at Carta Healthcare
Health systems have made real progress connecting back office, clinical, and patient-facing tools, and the next wave of integration work is opening up possibilities that weren’t realistic even a few years ago. Modern APIs and AI are doing more of the heavy lifting, and when you pair that technology with people who understand both the operational and clinical side, you get cleaner data, smoother handoffs, and reports leaders can actually trust. The organizations moving fastest are the ones that see integration as a chance to rethink how work gets done, not just a technical box to check.

Such 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.

How do you think back office health IT systems are improving efficiency in areas such as finance, human resources, and supply chain management? Let us know over on social media, we’d love to hear from all of you!



< + > If A Server Fails Today, How Long Until Your Patients Can Be Seen?

The following is a guest article by Michael Ohayon, GM Nexcess Managed Cloud at Nexcess

Go from “backups running” to a clinical recovery mandate

When a server fails at your practice, specialty clinic, or healthcare software company, the questions that follow are immediate and high-stakes. You need to know which systems are affected, where recoverable copies are stored, and most importantly when access will return. These questions require tested answers before the next hardware failure, cyberattack, or accidental deletion interrupts care.

In healthcare, a server outage transcends the IT department. Your front desk may lose appointment schedules, while clinicians find themselves unable to open patient records or review critical medication histories. Staff may be forced to fall back to paper workflows designed for short interruptions, not the uncertain windows associated with modern technical recovery. This is why the most vital question is,  “How long will it take to get the right systems back?”

A successful backup is not a successful recovery

Backups are often treated as evidence of preparedness. A dashboard indicates a job completed, and the organization moves on. However, a successful backup job doesn’t tell you how long a restore takes, nor does it confirm that the copy is usable for clinical applications.

This distinction matters because downtime spreads fast. A scheduling system or patient portal may not be the EHR, but its failure can still paralyze patient-facing work. A practical managed backup and storage strategy starts with recovery requirements, not storage capacity, which means defining two metrics up front:

  • Recovery Time Objective (RTO): The target duration for getting a system operational again.
  • Recovery Point Objective (RPO): The maximum amount of recent data the organization can afford to lose (e.g., losing four hours of clinical documentation vs. 24 hours).

Recovery priorities should follow the patient workflow

Not every system needs to return simultaneously. During an incident, the priority should be the apps that allow staff to identify patients, understand their clinical needs, and document treatment. Administrative reporting or development environments can wait.

That sequence has to be decided before a crisis, not negotiated in the middle of one. For example, you may decide that identity management and clinical access return first, with the patient portal following. Deciding in advance also exposes the dependencies that break recovery plans in practice: restoring an application server accomplishes nothing if the underlying database is still down.

Protection beyond the primary server

A backup stored only on the affected server is vulnerable to the same event that took the server down. Hardware failures, facility-level incidents, or ransomware can  compromise the original data and the local copy together. This reality was highlighted during the global cPanel security incident in April, forcing emergency patching and temporary service shutdowns across hosting environments to head off unauthorized root access.

Off-server and offsite protection should be the baseline, and the “3-2-1” principle remains the best guide: three copies of your data, on two forms of storage, with one copy offsite. For healthcare, that offsite copy also has to meet security and compliance obligations for encryption and access controls, not only exist. Small restores shouldn’t require big escalations

Not every recovery is a catastrophe. Often, the issue is a deleted document or an overwritten configuration file. If every minor restore requires a high-level support escalation, a small error turns into a four-hour interruption. Self-service recovery options for authorized IT staff lets routine restores  begin immediately and keeps specialized support free for genuine system-wide failures.

Nobody wants backups; what people want is the ability to restore. Getting there requires understanding the difference between standard backups and disaster recovery, which covers how infrastructure returns after a large-scale disruption.

Test the theoretical

Healthcare data grows relentlessly, and a backup strategy has to keep pace without collapsing under its own weight. Review storage against usage and retention requirements to avoid overprovisioning or hitting silent quotas that halt protection.

A recovery plan is theoretical until it is tested. Testing doesn’t require taking production systems offline; restoring selected files or validating a database copy in an isolated environment will answer the questions a dashboard can’t:

  1. Can we access the backup?
  2. Is the data complete and usable?
  3. How long does the process take?
  4. Who makes the call to fail over?

Ask the question in minutes, not hours

“Backups are running” is not an answer you can use. A resilient organization answers  in specifics: the scheduling system has a one-hour recovery target, the most recent copy is 30 minutes old, and the hosting team is executing a documented recovery runbook right now.

Server failures are inevitable, but extended uncertainty about patient care isn’t. Move from a backup mindset to a recovery mandate, and when the next outage begins, the path back to normal care is already mapped.

Michael Ohayon is with Nexcess, a managed hosting platform that provides HIPAA-aligned infrastructure and migration support for specialty practices, regional health centers, and healthcare SaaS platforms.



< + > Flourish Health Raises $46M | Procode Raises $10M

Check out today’s featured companies who have recently raised a round of funding, and be sure to check out the full list of past healthcare IT fundings.


Flourish Health Raises $46M to Scale Intensive Mental Health Care for Youth in Crisis

Funding Fuels National Expansion of Model that Delivers Proven Outcomes for High-Acuity Kids through Psychiatrist-Led Care Pods and an AI-Enabled Care Delivery Platform

Flourish Health, the mental health provider for young people with serious, complex needs that traditional care models cannot effectively support, announced $26 million in Series A funding led by B CapitalF-Prime, and Cherryrock Capital. Combined with $20 million in previously undisclosed funding, the investment brings Flourish Health’s total raised since inception to $46 million. The new capital will support the company’s national expansion, in partnership with the largest health plans in the country.

Flourish Health helps children and young adults overcome serious mental health challenges through a unique, in-home care model that’s delivered by psychiatrist-led teams. Studies with multiple major health plans have shown that Flourish Health delivers a 70-96% decrease in hospitalizations, a 69-90% decrease in residential treatment, and a sustained 80-95% decrease in higher levels of care post-discharge. These outcomes have led to expansions with nearly every major health plan in the country, including most major Medicaid plans, specialty foster care plans, and commercial insurers.

Flourish Health patients are supported by a dedicated Care Pod of four coordinated professionals: a child and adolescent psychiatrist, licensed therapist, patient guide, and family guide. The company’s AI workflow platform enables these teams and the company to scale effectively by resolving most of the administrative burden so human clinicians can focus on human patients. This model allows Flourish Health to serve families that the current system has been unable to effectively support. It is proven to deliver gold-standard outcomes for the three clinical cases where it specializes. This includes kids with externalizing behaviors (including ODD, IED, DMDD, and conduct disorder); multiple suicide attempts or suicide-related hospitalizations; and complex needs and family situations that force other clinics to turn them away.

“I lost a young patient to suicide working in the pediatric ER. He was getting bullied at school and his mom felt lost trying to advocate for him. The next day, I cared for another teenager in crisis who was in foster care and extremely distressed about having to change homes again. I knew she needed more than another waitlist or referral, and there had to be a better way to help these kids and their families,” said Natalia Birgisson, MD, Co-Founder and CEO at Flourish Health…

Full release here, originally announced July 27th, 2026.


Procode Raises $10M to Bring Its AI-powered Medical Billing Services to Every Surgical Specialty

Led by Health Velocity Capital, the Round Funds Additional Medical Billing Acquisitions and Follows Procode’s First Peer-Reviewed Study Showing its AI Outperforms Human Coders and General-Purpose LLMs in Complex Surgical Coding

Procode Inc., an AI-powered medical billing, or revenue cycle management (RCM) company, serving private practice surgeons, today announced a $10 million Series A led by Health Velocity Capital. The funding follows Procode’s acquisition of The Auctus Group — the leading RCM company for plastic surgeons and dermatologists — and the publication of its first peer-reviewed research in Plastic & Reconstructive Surgery (PRS) Global Open, the official open-access journal of the American Society of Plastic Surgeons. Procode will use this new capital to expand its AI-powered RCM platform through additional acquisitions and accelerate growth into all surgical specialties and ambulatory surgical center (ASC) billing.

Three Companies, One Platform

The $10 million will fund two additional acquisitions, to be announced in the coming months. Procode intends to follow the same playbook it used with The Auctus Group: acquire the best surgical billing companies and layer in Procode’s AI to drive better outcomes for surgeons:

  • Procode: AI coding for surgery that automates the coding workflow from charge capture to submission, reduces coding-related denials, and fully captures reimbursements
  • Procollect: An operating system for billing teams with native reporting, AI support, and automation that reduces days in AR and accelerates reimbursements
  • Auctus Provider App: An app that puts real-time financial data at Procode’s clients’ fingertips

“We’re on track to double The Auctus Group’s revenue and quintuple its EBITDA margin,” said Jeff Cripe, CEO at Procode…

Full release here, originally announced July 28th, 2026.



Wednesday, July 29, 2026

< + > Human in the Loop Does Not Mean Safe: The Hidden Risks of Agentic AI in Healthcare

The healthcare industry treats artificial intelligence like just another software rollout. We write policies, form committees, and assume standard guardrails will keep operations safe. But agentic AI behaves entirely differently than previous digital health tools.

At the recent eHealth26 conference in Halifax, Nova Scotia, I sat down with Julia Zarb. She is the force behind Blue x Blue, a company building AI infrastructure with governance incorporated. She warned that applying the old IT playbook to this new medium creates massive unseen risks.

Core Insight: Human Oversight is a Liability Trap Slapping a “human-in-the-loop” label onto an AI tool does not guarantee patient safety, it simply shifts the legal and operational liability onto overwhelmed clinicians.

The Human-in-the-Loop Fallacy

Executives love hearing that a human will review AI outputs before finalizing a clinical decision. In reality, these reviewers lack the time and system explainability to catch microscopic errors under high stress.

“What we’re doing is transferring the liability that comes with that review,” Zarb noted. “We don’t have what’s called explainability to go back in and see how that got to it, who knew what, when, and where, and why?”

The Threat of Invisible Errors

AI models fail in tiny, logical steps that easily bypass human reviewers.

“What makes agentic AI good is also its internal flaw,” Zarb explained. “The way that it processes, it carries forward little micro fissures and flaws.”

Standard IT guardrails fail because AI naturally jumps boundaries. When multiple AI agents interact, a single logical error propagates until it results in a much larger failure.

“If 100 agents speak to 100 other agents and those mistakes are brought forward, these aren’t mistakes that are just errors, these are mistakes that make sense [to Ai at the time] because of the way the AI is built.”

Questions Healthcare IT Leaders are Asking

Why is the “human-in-the-loop” approach risky for healthcare AI? Relying solely on human oversight transfers immense liability to clinical staff. Fast-paced environments and high stress prevent humans from properly auditing AI outputs. Furthermore, clinicians lack the technical explainability tools required to understand exactly how the AI arrived at its conclusions.

How do AI errors differ from standard digital health software bugs? Traditional software bugs are often clear and trackable. AI generates “micro fissures” or small gap-fillers that look perfectly logical to the system. These tiny errors bypass standard guardrails and multiply rapidly when different AI agents share data across an organization.

Learn more about Blue x Blue at https://www.bluexblue.com/

Listen and subscribe to the Healthcare IT Today Interviews Podcast to hear all the latest insights from experts in healthcare IT.

And for an exclusive look at our top stories, subscribe to our newsletter and YouTube.

Tell us what you think. Contact us here or on Twitter at @hcitoday. And if you’re interested in advertising with us, check out our various advertising packages and request our Media Kit.



< + > Applying AI to Healthcare Integration and Interoperability

One of the coolest AI tools I’ve seen recently was the healthcare integration agent, Rhapsody Axon.  It’s an AI integration agent embedded directly in the Rhapsody and Corepoint platforms, built to speed up the work integration teams do every day. It also lines up with my theme that AI is going to be able to be applied to almost every aspect of healthcare.  In fact, that’s what makes Axon so cool.  Rhapsody applied AI to a topic that they understand deeply, and they know is a challenge for the interoperability and integration experts out there.

Here’s what I keep coming back to: as more AI agents show up across healthcare, they’ll all eventually need to move data across systems. Axon is an early example of an agent that doesn’t just answer questions about integration, it can actually help build the connections (not just wait for a human to do it).

This is why I was really excited to check out their webinar called “What’s Next in AI for Healthcare Interoperability? See Rhapsody Axon in Action.”  Click the link to watch the full webinar or check out the highlights below that we shared on social media during the event.

What do you think about applying AI to healthcare integration and interoperability?  What other novel approaches to AI in healthcare have you seen?  Let us know on social media.

How does Rhapsody Axon reduce the grind of integration work?

I’ve heard this from a number of integration experts in the past.  Many of them spend more time looking at the documentation than they do actually coding.  Not to mention time spent troubleshooting an integration that has broken because some interface has changed.  The time spent on these actions are real and required.  Axon’s AI helps remove some of this friction.

Is Rhapsody Axon embedded or a standalone tool?

I think every CIO I talk to is afraid of AI sprawl.  In other words, they’re afraid of having hundreds of different AI applications that they have to support.  Thus, it’s a welcome sight that Axon is embedded in Corepoint and Rhapsody.

How does Axon understand your integration environment?

I’m also impressed that Axon not only understands the standards and integration documentation, but it also understands your Corepoint and Rhapsody set up.  This type of personalization to your environment is so powerful and helpful when building new integrations or troubleshooting existing ones.

How does Axon help onboard new integration staff?

Of course, I also love the idea of Axon quickly upskilling a new person at your organization rather than having the new person continually bothering your other integration staff.  A new employee can ask Axon all the questions they want without annoying them.

How does Rhapsody Axon handle data security and privacy?

These are the types of questions I see a lot of CIOs asking of their AI vendors when it comes to AI security and privacy.  I love that Rhapsody just shared these details up front.

I think we’re probably going to have this conversation about technology replacing humans forever.  Clearly humans have shown themselves to be pretty resilient.  It’s great to see Axon helping those humans be more efficient.  As I often say, AI is going to replace all those time consuming things that humans don’t want to do.  That seems to be what Axon is doing for integration experts.

Can Rhapsody Axon build integrations automatically?

I was a little surprised that searching through standards documentation wasn’t on the list of things that most integration experts spend time on.  Although, maybe that’s part of the two that they listed.

No doubt these two tasks are important and accelerated by AI.  As their customer said on the webinar, if you don’t like Axon after you see how it can automatically create the integration routes for you, then he doesn’t know what to tell you. These agents can consume interface specs and create integrations for review. I’d add that if you don’t see the value in agentically created integration routes, you’re probably not an integration engineer.

AI programming engines have become all the rage.  Programmers have really come around to using them.  The problem is they don’t work for this kind of custom coding that’s needed for healthcare integrations.  That really is what makes Axon unique, but still in line with what’s happening with coding in other languages.

I imagine that an integration engineer’s least favorite task is upgrading their integration from one HL7 standard to the new one, or migrating an old HL7v2 interface over to FHIR. That’s a bunch of work to essentially make sure it keeps doing the same thing.  That’s not fun work.  In other words, it’s perfect work for an AI agent.

How are teams using Axon to review integration documents?

This example from Jennifer of how they’re using Axon was really brilliant.  No one likes reviewing documents for a new integration.  Axon can do that quickly and highlight the areas that really matter so you can quickly have the right conversation with the vendors you’re looking to integrate with.  That’s powerful.

I’ve felt Jennifer’s pain a lot with the Healthcare IT Today website.  I know just enough to be dangerous and things that would be easy and quick for an expert take me far too long.  AI has really helped me solve these more technical issues quickly.  Sounds like she’s doing the same in her job with Axon.

Having Axon run reports for her is fascinating too.  I love the concept that she ran the reports manually for so long that she can really know if the AI is doing well or not.  The fact that she trusts the AI now is saying a lot.

Those are a few of the highlights from the webinar.  You can watch the full webinar including a demo of Axon or check out the Rhapsody Axon page to see it in action for yourself. What do you think of this use case for AI in healthcare?



< + > This Week’s Health IT Jobs – July 29, 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:

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.



Tuesday, July 28, 2026

< + > Lessons from CHIMA: Coding for Clinical Care Instead of Reimbursement

The Health Information Day at eHealth26 in Halifax was an unexpected treat. The event hosted by CHIMA felt entirely different than previous revenue cycle and health information management (HIM) conferences. The biggest surprise was the lack of discussion on payer friction and claim denials.

Core Insight from CHIMA26

The conversations in Halifax proved that health information management is moving out of the back office. Without the constant battle of fighting insurance companies for reimbursement, Canadian professionals are focusing increasingly on data quality and governance. They are actively evolving into the essential data stewards needed to make future AI projects successful.

Coding for Care Instead of Cash

US hospitals code every minor patient condition to justify costs and ensure payment. In Canada, the single-payer model changes the rules entirely. Jodi McMullin, founder of ScoJo Consulting, holds certifications in both countries and explained the stark contrast.

“Because it’s a funding portfolio, we don’t look at those smaller issues,” McMullin explained. Because primary care in Canada is a capitated system, physicians are not as concerned about reimbursement accuracy. As long as the patient is treated and the information recorded into the EHR, everyone just moves on.

Expanding the Profession

The health information role is expanding rapidly across Canada. Mahmoud Suliman, CEO of CHIMA, pointed out that the profession often struggles to explain its true value to the public. He wants to change that narrative.

“There are a good number [of our members] that are coders, but we’re finding there are people part of clinical documentation improvement, release of information,” Suleiman explained.

These professionals ensure patient data remains secure, accurate, and accessible for clinicians and policymakers making critical decisions for the health system.

The Bottom Line from CHIMA’s Health Information Day

Health information management is no longer just a back-office administrative task. These professionals are strategic assets for data governance. Organizations looking to establish a strong and accurate data foundation, need look no further than their HIM department for a pool of capable professionals ready for the challenge.

Questions Healthcare IT Leaders are Asking

Why is clinical coding volume lower in Canada than the US? The United States operates on a multi-payer system that forces providers to document every condition to secure reimbursement. Canada uses a single-payer system that focuses strictly on the primary reason for the patient visit. This results in fewer codes and far less administrative friction.

How do health information professionals impact AI strategy? Artificial intelligence models require accurate, clean data to produce safe clinical outputs. Health information professionals act as data stewards who enforce governance and maintain privacy compliance. Their oversight guarantees that the data feeding these models is trustworthy.

Learn more about CHIMA at https://www.echima.ca/

Learn more about ScoJo Consulting at https://scojocoding.com/

Listen and subscribe to the Healthcare IT Today Interviews Podcast to hear all the latest insights from experts in healthcare IT.

And for an exclusive look at our top stories, subscribe to our newsletter and YouTube.

Tell us what you think. Contact us here or on Twitter at @hcitoday. And if you’re interested in advertising with us, check out our various advertising packages and request our Media Kit.



< + > The Future of Value-Based Care Will Require Actuarially Engineered AI

The following is a guest article by Brian Overstreet, President & CEO at Arbital Health

At a recent Summit on the Future of Value-Based Care (VBC) and Risk Contracting, more than 150 payer executives, provider leaders, policy influencers, and digital health innovators gathered to discuss the operational realities of managing risk. While the attendees represented organizations deeply committed to value-based care, their conversations revealed concern that the industry’s ambition for aligning financial returns to better patient outcomes is advancing faster than the infrastructure that supports that goal.

Three key themes emerged from the discussions at the event:

  1. Leaders are confident that VBC is working but are still having difficulty showing direct financial impact
  2. The first step in making value-based care succeed is actuarially scoring the value of clinical interventions so that the economics are clear
  3. AI’s value, cost, and long-term impact on VBC is still relatively unknown

Showing Direct Financial Impact

Despite momentum in the evolution of VBC, many organizations still struggle to measure results, particularly within a one-year contract cycle. Delayed performance data, misaligned measures, and slow operational processes make it hard to show financial impact on demand. Forecasting and risk adjustment are still not moving fast enough to support the industry’s ambitions.

Too many organizations are still relying on information cycles built for fee-for-service: lagged claims, quarterly refreshes, and retrospective reporting. In most risk contracts between payers and providers, that approach is no longer good enough.

The organizations gaining ground are not waiting for a settlement to understand performance. They are using actuarial AI to track performance trends against contract dynamics in real time, surface cost drivers earlier, and identify interventions before negative trends become financial results.

The First Step: Actuarially Scoring Clinical Interventions

Participants at the Summit described a practical formula for success in specialty value-based models:

  1. Actuarially score the value of clinical interventions, so the economics are clear
  2. Create meaningful provider incentives and engagement
  3. Deliver high-quality clinical care that improves patient outcomes
  4. Design benefits that reduce friction and drive patient participation

The first step—scoring interventions using actuarial logic—proved particularly important as it sets the stage for financial success, without which the rest becomes unsustainable. Too often, programs are launched with clinical enthusiasm but limited financial clarity and early measurability. Only actuaries can help set value against the counterfactual effects of VBC, i.e., the positive outcomes that didn’t happen (deaths avoided, etc.), versus those that did happen. Without actuarial modeling of potential outcomes, even well-intentioned and successfully executed interventions can struggle to demonstrate value within short contract windows.

The Debate Around AI

Healthcare organizations are investing heavily in AI, yet many executives are now asking if AI actually reduces costs, or if it is simply adding another layer of expense. Infrastructure investments, integration challenges, staffing requirements, and model maintenance can quickly accumulate for organizations trying to build AI solutions internally. And, for organizations already managing thin margins, the financial return on AI investments remains under scrutiny.

That skepticism is shaping how leaders evaluate new technologies. Tools that promise predictive insight are increasingly expected to deliver both operational efficiency and measurable financial impact.

A Shift Toward Actuarial AI

Most healthcare AI tools are built by data scientists trained to optimize predictions. What’s different about Actuarial AI is that it is built by actuarial engineers and trained on actuarial-grade logic — credibility theory, loss ratio modeling, risk adjustment mechanics, trend analysis, reserving principles, and regulatory guardrails – all tied to individual contract terms and measures. It does not “black box” outputs, but surfaces its assumptions, model logic, data sources, and calculations to be fully transparent and auditable.

That distinction matters because in risk contracting, trust is currency. If finance leaders cannot audit the model, they will not rely on it. At the Summit, participants discussed the shift from standard reporting to accountable analytics, that is, systems that show their math.

The Strategic Implications

VBC will continue expanding across Medicare Advantage, ACO REACH/LEAD Model, Medicaid managed care, and commercial risk arrangements, and the attendant complexity will increase. Organizations that pair actuarial expertise with actuarially engineered AI will price better, see performance trends faster, and provide actionable insight into bending the risk curve so that organizations can manage risk more confidently.

Industry leaders are optimistic about what comes next for VBC, both from a policy standpoint and in how it operates day-to-day, but they recognize that success will depend on more than expanding risk contracts.

Organizations seeking to move ahead will use AI to drive immediate efficiency and faster decision-making, while requiring transparency and auditability. The next phase of VBC will be defined not just by risk contracts, but by the intelligence infrastructure that supports them.

About Brian Overstreet

Brian Overstreet is the Co-Founder, President, and CEO at Arbital Health. Brian has over twenty years of experience working with data and analytics SaaS companies in the healthcare market.



< + > Corner Health Announces $32.5 Million in Seed and Series A Financing | TytoCare Names Adam Pellegrini as CEO and Closes $25M+ Growth Round

Check out today’s featured companies who have recently raised a round of funding, and be sure to check out the full list of past healthcare IT fundings.


Corner Health Announces $32.5 Million in Seed and Series A Financing to Help Independent Clinicians Rebuild Primary Care

Corner Health, a company designed to rebuild the healthcare system by empowering Nurse Practitioners (NPs) to start and scale their own local primary care practices, announced today it has raised $32.5 million in Seed and Series A financing. Their recent Series A was led by Oak HC/FT, with participation from existing investors First Round Capital and Zigg Capital. The investment follows a period of rapid growth for Corner Health, which has established a network of more than 70 provider-owned primary care practices. Corner Health is the fastest-growing primary care network in Arizona and Washington and has enabled 35,000+ patient visits in the past year.

The funding comes at a pivotal moment for healthcare. Despite unprecedented investment in AI, more than 100 million Americans still lack access to a primary care provider, clinicians are burning out at record rates, and patient visits continue to get shorter. Corner Health is taking the opposite approach: using AI to create more time for the provider-patient relationship, not less.

“I grew up watching my mom run her own private practice, and I saw firsthand how powerful it is when a clinician owns the relationship with their patient,” said Lava Sunder, Co-Founder and CEO at Corner Health. “The tragedy is that the economics of healthcare have made that model increasingly difficult to sustain. Healthcare has spent decades pushing clinicians into larger and larger systems, often at the expense of time with patients. Via Corner Health’s network, the most common word that appears in patient reviews is ‘listen.’ When providers have more time, patients feel heard.”

The backbone of Corner Health’s model is Cora, an AI-native practice operating system designed to automate the full spectrum of brick-and-mortar primary care operations, including scheduling, patient communication, billing, lab orders, referrals, and prior authorizations. Built for scale and in-person clinical environments, Cora enables 90% of Corner Health clinics to operate with no additional staff, dramatically reducing overhead and giving NPs more time to focus on patient care.

“The dominant use of AI in healthcare today is helping clinicians move faster and see more patients,” said Anne Gifford, Co-Founder and COO of Corner Health…

Full release here, originally announced July 13th, 2026.


TytoCare Names Adam Pellegrini as CEO and Closes $25M+ Growth Round to Scale AI-First Clinical Enablement Platform

Company Repositions as an AI-First Clinical Enablement Platform, Bringing FDA-Cleared AI-Powered Insights into Virtual Primary Care to Support Cardiopulmonary and Oncology Care

TytoCare, pioneer of remote physical examination technology featuring FDA-cleared medical devices, today announced two major developments: the appointment of Adam Pellegrini as Chief Executive Officer, and the closing of a $25 million-plus growth round led by Insight Partners along with HOOP, OliveTree, OrbiMed, Qumra Capital, Qualcomm Ventures, and others. The company is expanding its platform to deliver clinical-grade remote care for patients with chronic and complex disease, positioning TytoCare to redefine how virtual primary care is delivered across high-acuity chronic disease populations.

Pellegrini brings more than two decades of experience at the intersection of digital health, consumer health technology, and large-scale clinical programs. His appointment comes as TytoCare moves to embed its remote examination platform and FDA-cleared AI-powered SaMD (software as a medical device) algorithms directly into integrated care pathways for remote cardiopulmonary monitoring, oncology support, and complex chronic disease management, disease areas where the gap between in-person clinical rigor and virtual care delivery has remained wide.

TytoCare’s platform combines a handheld examination platform featuring FDA-cleared medical devices — capable of capturing clinical-grade heart, lung, ear, skin, throat, and abdomen data — with a suite of FDA-cleared AI-powered SaMD algorithms that enable clinicians to conduct comprehensive remote physical exams with diagnostic confidence previously achievable only in person. The company’s expanded clinical enablement strategy is set to deepen integrations with leading health systems, payers, and employer health programs, with particular focus on congestive heart failure (CHF), COPD, post-surgical recovery, and oncology treatment monitoring.

“The convergence of a clinically validated exam device, AI-powered diagnostic algorithms, and the urgent demand from health systems for real clinical intelligence at the point of virtual care is an extraordinary and rare combination,” said Adam Pellegrini, Chief Executive Officer. “TytoCare has built the foundational infrastructure for the next generation of intelligent remote care, and I could not be more energized to lead this next chapter.”

The oversubscribed financing round, led by Insight Partners with participation from existing strategic investors, reflects institutional conviction in TytoCare’s market position and the accelerating commercial demand for AI-enabled remote diagnostics…

Full release here, originally announced July 15th, 2026.



Monday, July 27, 2026

< + > The Hidden Risk of Rushed Technology: Patrick Lo on Establishing AI Governance

The healthcare industry is rushing to adopt artificial intelligence tools, but many organizations are moving too fast without proper safeguards. Deploying automated systems without a clear plan for risk management can expose a health system to major compliance liabilities and operational failures.

Healthcare IT Today sat down with Patrick Lo, CEO of Privacy Horizon, at the eHealth26 conference to discuss why healthcare organizations must establish solid AI governance before deploying new digital health solutions.

Core Insight: Trust Before Intelligence

Healthcare organizations must establish comprehensive AI governance, including cross-functional risk management frameworks and clear lines of accountability, before deploying any automated clinical or administrative tools.

The Necessity of Pre-Deployment Controls

Healthcare leaders rush into vendor contracts without evaluating the long-term operational impacts on their staff. Patrick warns that organizations must prepare their compliance infrastructure well before any software integration goes live. “You just cannot just deploy a solution and hope for the best without thinking about what should prepare and put in place before you adopt a solution,” Lo explained.

Establishing Committee Accountability

Effective governance means bringing different stakeholders to the table to validate the operational purpose and risk profile of a new tool. Committees must assign clear ownership to handle system errors and manage vulnerabilities before technology enters the clinical environment.

“Before you deploy anything that’s AI, you should have an AI governance, which means including how do you make decision to deploy an AI? Why do you need to deploy an AI? Who should be accountable?” Lo noted.

Questions Healthcare IT Leaders are Asking

Why is AI governance required before a technology deployment? AI governance establishes critical rules for decision-making and risk management prior to software integration. Without this foundation, healthcare facilities risk implementing tools that compromise patient privacy or lack clear lines of staff accountability when automated errors occur.

What primary questions should an AI governance committee address? A cross-functional committee must explicitly define the clinical or administrative problem the technology is intended to solve. Leaders must also determine who is operationally accountable for the data inputs, system outputs, and ongoing risk mitigation frameworks.

Learn more about Privacy Horizon at https://www.privacyhorizon.com/

Listen and subscribe to the Healthcare IT Today Interviews Podcast to hear all the latest insights from experts in healthcare IT.

And for an exclusive look at our top stories, subscribe to our newsletter and YouTube.

Tell us what you think. Contact us here or on Twitter at @hcitoday. And if you’re interested in advertising with us, check out our various advertising packages and request our Media Kit.



< + > Healthcare AI Advances with Precision and Data Control

The following is a guest article by Paul Speciale, Chief Technology Evangelist and CMO at Scality

Artificial intelligence adoption is accelerating across nearly every industry, but healthcare and life sciences organizations are approaching the technology differently than many of their peers. While sectors such as financial services and manufacturing are rapidly expanding the use of generative AI and edge-based systems, healthcare organizations are prioritizing precision, governance, and data control as they scale AI into production.

That measured approach reflects the realities of the healthcare environment. Clinical and operational AI systems must deliver reliable outcomes, operate within strict regulatory frameworks, and protect highly sensitive patient data. As a result, healthcare organizations are favoring proven and explainable AI models while building infrastructure strategies designed to support long-term operational resilience.

New research from Freeform Dynamics, based on a survey of 504 enterprises actively running private AI environments, highlights how healthcare organizations are balancing innovation with risk management as AI adoption matures. 

Healthcare Favors Established AI Approaches

The survey reveals that healthcare and life sciences organizations continue to invest heavily in established AI technologies. Traditional machine learning leads adoption at 52%, while computer vision and image processing workloads account for 50% of deployments. Fine-tuned or customized AI models are also gaining traction at 44%. 

These numbers reflect the practical realities of healthcare AI. Machine learning models have long been used to support operational analytics, patient risk scoring, and predictive workflows. Meanwhile, computer vision is already deeply embedded in diagnostic imaging, pathology analysis, and radiology applications.

The report specifically notes that healthcare organizations show “notably lower LLM adoption,” likely due to concerns about “variability of output, hallucinations and regulatory constraints.” That caution is reflected in the survey data, with only 31% of healthcare organizations reporting adoption of RAG-enhanced foundation LLMs, well below financial services at 67% and manufacturing at 57%. 

This gap does not suggest healthcare is falling behind in AI adoption. Instead, it highlights a more selective and risk-aware deployment strategy. Healthcare organizations are focusing AI investments on areas where outcomes can be validated, monitored, and governed effectively.

AI in Healthcare is Expanding Beyond Pilots

Although healthcare organizations are moving cautiously with newer AI approaches, the survey makes clear that AI adoption overall is broadening rapidly across enterprises.

Among all respondents:

  • 68% are active with at least three different AI genres 
  • 29% are active with at least five AI categories 
  • 54% report having an overall AI strategy 
  • 49% say AI initiatives are generally well funded 

Healthcare organizations are participating in this broader evolution, particularly as AI use cases expand beyond isolated pilot projects into operational environments.

The report emphasizes that enterprises increasingly view AI as a strategic initiative tied to competitive differentiation, operational efficiency, and customer expectations. In healthcare, this can translate into faster diagnostics, improved clinical workflows, better resource utilization, and more personalized patient experiences.

At the same time, healthcare organizations face a uniquely high burden of responsibility around data governance, explainability, and compliance. This creates additional pressure to ensure AI systems are tightly aligned with organizational controls and infrastructure policies.

Private AI Gains Momentum in Healthcare

One of the strongest themes emerging from the research is the growing importance of private AI infrastructure, also referred to as sovereign AI.

Across all industries surveyed, 81% of organizations say private AI infrastructure they control is critical to their success. The report attributes this trend to concerns around sovereignty, compliance, data proximity, performance, and long-term cost management. 

These priorities are especially relevant in healthcare environments, where organizations must maintain strict oversight of patient records, imaging data, genomic datasets, and other highly regulated information.

Rather than relying entirely on public cloud AI services, many healthcare organizations are adopting hybrid or private AI architectures that allow them to keep data closer to clinical systems and internal governance controls.

The report notes that private AI can also reduce latency and improve throughput by keeping AI applications close to the data they rely on. For healthcare organizations handling large diagnostic images, real-time monitoring systems, or longitudinal patient records, these performance advantages can be significant.

Data Infrastructure is Becoming the Real AI Battleground

While public discussion around AI often centers on GPUs and compute power, the Freeform Dynamics research suggests enterprises increasingly recognize storage and data infrastructure as equally critical. The study found that:

  • 57% of organizations prioritize storage performance to avoid AI bottlenecks 
  • 54% prioritize compute and GPU availability 
  • 52% focus on network bandwidth limitations 

In addition, 86% of respondents recognize that different stages of the AI pipeline require different storage approaches. 

For healthcare organizations, this is particularly important because AI workloads often span multiple data-intensive stages, from data preparation and cleansing to model training and runtime inference. These environments must also support long-term data storage and retention requirements while maintaining compliance, governance, and audit management across the entire AI lifecycle.

Healthcare environments also generate enormous volumes of unstructured data, including imaging files, clinical notes, research datasets, and telemetry streams from connected devices. Managing these diverse datasets efficiently requires infrastructure that can scale while maintaining strong security and resilience. 

The report further found that 91% of organizations running private AI environments rely meaningfully on object storage, with 44% using it extensively. Object storage is increasingly becoming foundational for AI pipelines because it supports scalability, lifecycle management, and the handling of massive data repositories.

Security and Resilience Remain Central Concerns

Healthcare’s cautious AI adoption strategy is also shaped by cybersecurity and operational resilience requirements. The survey found that enterprises place cybersecurity, operational resilience, regulatory compliance, and sovereignty among the most important factors influencing AI storage decisions. 

Respondents also identified concerns around data leakage and unauthorized access, ransomware attacks targeting AI pipeline data, data corruption and integrity issues, and the ability to recover systems and data quickly following an incident. These concerns are amplified in healthcare, where AI failures or compromised data can directly affect patient outcomes and increase regulatory and compliance exposure.

Healthcare organizations must also navigate stringent data privacy and governance requirements, including HIPAA regulations in the United States and GDPR requirements across Europe. As AI initiatives expand across clinical, operational, and research environments, organizations are under increasing pressure to ensure sensitive patient and healthcare data remains secure, auditable, and properly governed throughout the AI lifecycle.

As a result, healthcare organizations are increasingly recognizing that AI infrastructure decisions cannot focus solely on performance. Security, recoverability, and governance must be integrated across the entire AI lifecycle. 

Healthcare’s AI Future will be Data-Centric

The research suggests healthcare organizations are not resisting AI adoption. Instead, they are building toward a more deliberate and sustainable model for operational AI.

Compared with industries such as manufacturing and financial services, healthcare may appear more conservative in adopting large language models and distributed AI systems. However, healthcare’s emphasis on explainability, governance, and trusted data pipelines may ultimately position the sector for more sustainable long-term AI deployment.

The report concludes that organizations with more AI experience tend to adopt more strategic infrastructure planning approaches, prioritize versatile platforms over siloed point solutions, and define storage requirements earlier in the deployment lifecycle. 

For healthcare organizations, these lessons are especially relevant. As AI adoption expands across diagnostics, operations, research, and patient engagement, success will increasingly depend on the ability to operationalize AI securely and at scale. The next phase of healthcare AI will likely be defined less by headline-grabbing models and more by the underlying data infrastructure that enables trusted, resilient, and compliant AI systems.

About Paul Speciale

Paul Speciale is a data storage and cloud industry veteran with over 20 years of experience with small and large companies. Paul is currently the Chief Technology Evangelist and CMO for Scality, leading the team across activities ranging from building awareness to content development and lead generation, as well as being a spokesperson for the company.



< + > CARPL.ai Raises $10M Led by IFC | Pearl Health Raises $110 Million

Check out today’s featured companies who have recently raised a round of funding, and be sure to check out the full list of past healthcare IT fundings.


CARPL.ai Raises $10M Led by IFC to Accelerate Healthcare AI Adoption Globally

CARPL.ai, the world’s largest radiology AI marketplace and enterprise platform, today announced a Series A round of USD $10 million led by International Finance Corporation (IFC), the private sector arm of the World Bank Group, with participation from Stellaris Venture Partners and other investors. The funding will accelerate CARPL’s mission to help healthcare providers adopt AI in a meaningful and safe manner.

CARPL is trusted by the world’s largest and most prestigious healthcare organizations, including four of the world’s top five private radiology groups. It is also being used by many governments, including Brazil, India, Singapore, Spain and the UAE.

Medical imaging generates more than 80% of healthcare data, as imaging demand continues to outpace radiologist capacity. Although more than 1,000 FDA-cleared radiology AI applications are available today, adoption remains fragmented. Healthcare organizations are forced to evaluate, integrate, and monitor dozens of standalone AI products independently, creating significant operational and IT complexity. In short, while the AI applications exist, the governance of these applications does not.

Through a single integration with CARPL, healthcare providers gain access to the world’s largest radiology AI marketplace with more than 300 AI applications from over 100 partners. In addition to this, the CARPL platform also enables its customers to build, test, deploy, and monitor AI across clinical workflows while remaining vendor-neutral and infrastructure-agnostic.

The investment will support continued product innovation, expansion of CARPL’s global partner ecosystem, and commercial growth across North America, LATAM, Europe, Asia-Pacific, and emerging markets. The partnership also reflects a shared focus on advancing digital health innovation through CARPL’s model of building high-skill engineering, product development, and AI capabilities in India for deployment to healthcare providers around the world.

“Our mission is to ensure that clinicians can confidently utilize the absolute best AI technology available for their patients. Over the past few years, we have built…

Full release here, originally announced July 23rd, 2026.


Pearl Health Raises $110 Million to Expand Its AI Platform Helping Providers Deliver Better Outcomes at Lower Cost for Medicare Patients

The Company Reached Profitability in 2025 and is Projected to Generate $500 Million in Gross Healthcare System Savings While Tripling Its Patient Base from 2024 Through the End of 2026

Pearl Health, a healthcare technology company helping manage risk and deliver better care to Medicare patients, today announced a $110 million capital raise, comprised of equity investment led by Andreessen Horowitz with participation from Viking Global Investors, AlleyCorp, Ulysses Capital, and a debt facility led by Trinity Capital. The new capital will expand Pearl’s AI platform, turning clinical intelligence into measurable outcomes, and accelerate its growth across enterprise health system and payer partnerships, its expansion into Medicare Advantage, and new risk offerings.

More than 70 million people today rely on Medicare, with costs exceeding $1 trillion and climbing. Across healthcare, reimbursement is increasingly tied to outcomes rather than utilization, creating powerful incentives for providers to prevent avoidable illness, intervene earlier, and manage patient populations. As healthcare shifts from reactive treatment to preventative care, demand is accelerating for technologies that enable providers to succeed in this new model.

“Pearl was founded on a simple belief: healthcare should reward keeping people healthy, not just treating them when they are sick,” said Michael Kopko, Co-Founder and CEO at Pearl Health. “Unnecessary costs and poor outcomes persist in US healthcare because most providers lack the capabilities to shift to outcomes-based care alone. With this financing, we are investing in accelerated innovation and growth to expand our impact across the healthcare system.”

“Pearl has demonstrated that managing risk across large patient populations across many different settings of care can improve patient outcomes, generate meaningful savings, and support a sustainable business model at scale,” said Vineeta Agarwala, MD, General Partner at Andreessen Horowitz. “Pearl’s ability to enable providers to participate in value-based payment programs successfully – and to do so through technology, rather than clinical workforce expansion – is a testament to both the vision and execution of the Pearl team.”

“We believe Pearl Health is changing how providers participate in value-based care, simplifying the data and daily workflow so they can spend more of their time and attention on their patients,” said Phil Gager, Senior Managing Director, Tech Lending at Trinity Capital…

Full release here, originally announced July 8th, 2026.



< + > Evogene – Can a Platform Company Become a Real Business? – Life Sciences Today Podcast Episode 74

We’re excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today. My guest today is Gabi Tarcic, Chie...