Ben says that 60% of health care payments already have some form of value-based modifier built into it, and credits this with reducing costs in the U.S. from an expected 6 trillion dollars to 5 trillion.

Rural organizations are often exceptionally strong at primary care delivery, but Sachdeva points out a towering challenge to value-based care for rural health: some 50% to 65% of costs are incurred in other facilities outside the ones used by patients for primary care. Technology solutions such as cleaner, well-integrated data and better coordination are required to make value-based care work.

What Sachdeva found is that the most impactful value based care strategy is when it’s an extension of the care team while still keeping the provider relationship central. Using Garage-generated prioritization lists in the background allows care teams to focus on the right patients without disrupting the provider-patient relationship.

The reality is that what works in urban markets often does not work in rural communities due to unique challenges around access, affordability, transportation, awareness, and social determinants.  On the positive side, Ben says, rural providers are very committed and have a “strong fabric” in their communities and are very resilient. They are not focused on maximizing the number of encounters. Furthermore, according to Sachdeva, it may be easier to implement new workflows at a small facility where there’s easier coordination and the staff trusts their leadership.

Both interviewees say that the $50 billion recently earmarked for improving rural health care in the Rural Health Transformation Program (RHTP) comes at a critical moment as communities prepare for the impact of the upcoming Medicaid reductions. Ben and Sachdeva suggested that this funding creates a great opportunity to use these funds to modernize infrastructure, virtualize care delivery, automate workflows with AI, train the workforce, and better position rural communities for long-term sustainability.

Ben says that the principle is to “focus on the right patients at the right time.” Value-based care means more screening and early disease management.  Sachdeva also emphasized that these investments impact the entire continuum of care and that payer partners must actively be part of these efforts.  Great payer partners are the key to being successful in value-based care.

This requires integrated data that is turned into “actionable insights.” Payer and provider data must be better integrated, perhaps requiring support at the state level.  Providers are operating with lean teams and need “one version of the truth” to focus on the patients that matter most within VBC contracts.

On top of a strong data infrastructure, AI can look through millions of patient records to identify revenue opportunities. Sachdeva points out that wearable devices can produce more accurate interventions by learning each patient’s personal baseline, instead of depending on “just what the textbook says is normal.”

Getting “rural-relevant” contracts is also important. AI can help reduce the manual labor of dealing with many contracts at many payers.  One of the largest barriers to successful VBC participation in rural markets is having enough scale and having payer relationships structured appropriately for rural organizations.  Cibolo and The Garage partnered together to help rural health organizations really address these obstacles.  Through this collaboration, the BlazeLink Contract Compliance Agent was born.

In our interview, Pranam noted that contracts can now be consumed by the agent in approximately 0.25 seconds on average and analyzed against millions of records to identify revenue opportunities. Given rural facilities often operate on extremely narrow margins, even recovering a fraction of the typical 3–6% of compromised revenue can materially impact sustainability.

Sachdeva shared that AI is becoming a necessity, not a replacement for providers. The “people” aspect of care cannot be removed. These technology and AI tools though can reduce the operational burden rural facilities face around contract compliance and understanding reimbursement methodologies.

Learn more about what’s happening in rural health to address the challenges, making rural health’s value based care efforts successful, and how technology can help in these efforts in this interview with The Garage and Cibolo Health.

Learn more about The Garage: https://www.thegaragein.com/

Learn more about Cibolo Health: https://cibolohealth.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 Garage is a proud sponsor of Healthcare Scene.

The following is a guest article by Colin Lawlor, CEO at Sleep.ai

Most digital mental health products slot sleep into the “bedtime content” bucket, creating meditation tracks, breathing exercises, or wind-down playlists. The premise is that sleep sits downstream from mood: calm someone down, and sleep arrives. But the evidence points the other direction, too.

A Journal of Affective Disorders meta-analysis reported that people who were not depressed but did have insomnia faced about double the risk of later developing depression. Affects this significant don’t show up often in psychiatric epidemiology, and this one has stayed consistent through years of replication.

A Signal That Keeps Getting Stronger

The relationship runs both ways, and it runs deep. Poor sleep predicts future mental health symptoms. Mental health symptoms disrupt future sleep. The mechanisms are physiological, not only psychological. Chronic sleep loss changes how the brain regulates emotion, how the body tolerates stress, and how the experiences of the day get consolidated during the night. Those changes turn into measurable risk for depression and anxiety.

This isn’t hanging on a single headline study. A meta-analysis of more than 172,000 people arrived at essentially the same takeaway and a 2024 umbrella review pooling 29 systematic reviews did too. An APA meta-analysis of 154 sleep-loss studies found a clear dose-response pattern: less sleep, worse mood, and higher anxiety, step by step along the curve. For anyone delivering or building mental health care, sleep is one of the most reliable indicators of how someone is doing day to day. Yet in many digital mental health products, it’s functionally invisible.

Why Mental Health Products Haven’t Incorporated Sleep

The teams building in mental health know the research, but have faced structural obstacles in implementation.

First comes the fact that sleep is hard, particularly the data. Patients are using apps, watches, rings, and even mattresses to track – but figuring out how to use the differing (often conflicting) data from each of these devices can be really challenging. Then you have accuracy issues – many of these devices have been trained on populations with no sleep issues, and they can be way off the mark when it comes to populations suffering from sleep issues.

Secondly, the engagement ceiling. In clinical trials of depression and anxiety apps, about a third of users are gone by follow-up, and real-world drop-off is often worse. One panel-based analysis found that only around 10% of users were still active a week after download. Conversation, content, and coaching can help significantly improve symptoms for those who stick with them, but that group shrinks quickly. If digital mental health is going to make a meaningful jump forward, it has to come from the product working smarter, not from trying to squeeze more usage out of people. That lever has already been pulled.

The third issue is design. Apps built around chat, lessons, and coaching haven’t had an easy path for bringing in an objective physiological input. Sleep tracking, for a long time, meant hardware that many members wouldn’t wear. Product teams responded in a predictable way: they treated sleep as nearby wellness material and kept it outside the core workflow that drives engagement and care.

What Integration Looks Like Now

Things are beginning to improve. A 2025 meta-analysis in the Journal of Clinical Sleep Medicine reported that current consumer sleep trackers have reached accuracy levels that clinicians and researchers can work with. Smartphone-based measurement, using motion, ambient audio, and on-device AI, has been improving alongside it. Measuring sleep through the phone someone already owns is now something you can actually ship.

Some digital mental health platforms are already building around this shift. Meru Health weaves heart rate variability biofeedback into its CBT-based depression and anxiety program, using a cardiac signal as an objective anchor alongside therapist conversation. HearMe is working sleep intelligence into a peer support experience. Different physiological inputs, same basic move: let objective measurement shape how the product responds to the user.

Payers are moving as well. German statutory insurers, covering about 25 million members, have begun reimbursing digital sleep coaching programs as preventive care, launching the first reimbursable digital sleep intervention at that scale. When payers start paying for sleep as prevention, it’s a sign the evidence has pushed sleep out of the “wellness extra” category.

Where the Bar Should be Set

As more teams try to integrate sleep, it helps to be clear on what counts as real integration versus a new feature label.

The first question is whether the sleep signal is genuinely objective. Mood journals and sleep diaries don’t line up well with measured physiological state. If a system depends on users remembering, estimating, and logging how they slept, it’s still self-report, just dressed up.

Next is validation against a clinical reference. Sleep tracking quality differs drastically from one device to the next. If sleep data is going to inform a mental health product, it should be benchmarked against polysomnography or a comparable standard, with peer-reviewed comparison data in the open. The distance between “we track sleep” and “we track sleep in a way a clinician would recognize” is larger than most marketing copy admits.

Then comes the practical test: does sleep change what the product does, or did it just earn a tab in the app? If sleep produces a dashboard but never alters coaching, prompting, or clinical escalation, it’s still adjacent content. Integration becomes real when the product behaves differently after a week of poor sleep than it does after a week of good sleep.

Digital mental health’s first decade produced meaningful science and real symptom improvement. The next stage needs physiological grounding, and sleep is the most replicated, workable signal to build around. The research has been settled for a long time. What wasn’t available was a credible way to measure sleep at the scale these products operate at. That gap is closing now. 

The practical move for anyone building in this space is to look closely at where sleep sits in the mental health experience they’re responsible for. If it’s limited to a meditation track, ask whether that still fits what the evidence says. If it’s missing entirely, ask what’s blocking it. And if sleep data is already present, the real question is simple: when the signal says the user slept poorly, does anything the product actually does change? That’s the bar worth holding the whole category to.

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

• Director of Information Technology – Trace Medical
• Data Specialist I – Nursing Administration – CHRISTUS Health
• Senior Director Systems Engineering – WellSpan Health
• Director of Network & Technology Resilience – TransMedics, Inc.
• Medical Biller – Epic
• Health Information Tech II – Health Information Services – Health First
• Vice President of IT – Avance Care
• Credentialing Coordinator – Revenue Cycle – UPMC
• US Oncology Practice Technology Director – McKesson
• Senior Director, IT PMO – Labcorp
• Patient Financial Representative – Patient Financial Services – CHRISTUS Health
• Technology Director, Billing and Enrollment – Medica
• Program Administrator GME – HCA Healthcare
• Patient Care Specialist – Community Events – Sentara Health
• Director, Data Systems and Clinical Programming – Alumis
• Health Information Specialist – Evara Health
• Senior Director of IT Operations – Vivie
• Patient Experience Representative II – MultiCare Health System
• Director, Configuration – Versant Health
• Associate Director, IT End User Services – BioSpace

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.

The initiative came about after a few doctors heard of ambient scribes and wanted to try it. The health center started by offering the tool to these doctors, but then realized it had a great opportunity to reach out and introduce it to other doctors experiencing burn-out. A demo video of the tool in action was enough to persuade many to use it. Family Health Center EHR trainers came into the practice to work side-by-side with doctors, training them on Sunoh.ai. And the doctors are happy with the change with many of them using the scribe for more than 90% of their notes.

The departments currently using the scribe are primary care and behavioral health. The latter department has unique needs for recording interviews which are longer and more in depth.  This required a special effort from the team at Sunoh.ai to make it work effectively. Cox hopes to have their women’s health and pediatrics clinicians using the scribe soon.

According to Cox, one of the most important traits of Sunoh.ai is it’s ability to handle Spanish.  Spanish is spoken by most of the 41% of patients who are non-native English speakers and is also used by some doctors during the visit.  This allows a patient to speak in the language their most comfortable while still capturing the information for the clinical note in English.

Cox also noted that Sunoh.ai can focus on what’s important in the visit. Casual chat about non-essential subjects is left out of the note, but important points are documented and sometimes in more detail than the doctors were doing before Sunoh.ai.

She hopes that Sunoh.ai in the future will help with more of the discrete data that is needed, as Family Health Centers is a federally qualified health center. She also says it could offer more support with pulling in templates for structured data.

Watch our interview with Family Health Centers to learn more about their experience with the AI medical scribe, Sunoh.ai.

Learn more about Family Health Centers: https://www.fhclouisville.org/

Learn more about eCW: https://www.eclinicalworks.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.

 eCW is a proud sponsor of Healthcare Scene.

Every revenue cycle leader knows denial rates are increasing industry-wide, in part due to AI systems implemented by Payers but also increasingly complex CMS guidelines. Most of them also know that AI coding tools are supposed to help with that. 

The tools are getting better. Documentation review is faster. Code suggestions are more accurate. Audit flags catch things human coders miss. 

And denials are still happening on claims that were coded correctly. 

That is not a coincidence. It is a structural problem that coding tools were not built to solve. 

What Coding Tools Actually Do 

Most AI coding tools operate on a specific problem. They read clinical documentation and return code suggestions. They flag gaps between what the provider documented and what the codes require. Some layers in compliance checks against known audit patterns. 

That is genuinely useful. A coder working with a well-trained AI model produces more accurate codes faster than a coder working without one. 

But accurate codes and a payable claim are not the same thing. The gap between them is where most of the preventable denial volume in high-complexity specialties actually lives.

Where the Real Problem Is 

Take a wound care encounter. The provider performs sharp selective debridement, applies a skin substitute, and uses chemical cauterization on a granulation tissue complication. The coder reads the note and assigns 97597, 15271, and 17250. All three codes are correct based on what was documented. 

The claim will be denied. 

CPT 17250 is bundled with 97597 under CCI edits. Billing them together on the same wound during the same encounter is a bundling violation. The codes were right. The claim construction was wrong. 

That is one example from one specialty. Multiply it across modifier requirements, payer-specific coverage conditions, LCD and NCD medical necessity rules, surface area measurement logic, HCPCS product code accuracy, and authorization matching, and you start to understand the actual surface area of the problem. 

None of that is a coding problem. All of it determines whether the claim pays. 

Why the Tools Stop Short

AI coding tools are built around a specific workflow. Clinical note goes in. Code suggestion comes out. The model is trained on documentation patterns, coding guidelines, and audit logic. 

What it is not trained on is the relationship between codes after they are assigned. Whether two correct codes can coexist on the same claim for the same encounter. Whether the modifier required by one payer differs from what another accepts. Whether the diagnosis attached to a procedure maps to an active LCD in the MAC jurisdiction where the claim will be adjudicated. 

That logic does not live in the documentation. It lives in payer policy, coverage determinations, and claim-level edit libraries that exist entirely outside the coding workflow. 

A tool that reads notes and returns codes has no mechanism to enforce any of it. 

What Has to Happen Before Submission 

The point in the revenue cycle where payability is actually determined is not coding. It is claim construction. That is the moment where everything upstream gets assembled into a structured transaction and validated against the rules that govern adjudication. 

That validation has to check bundling edits against the specific procedures performed. It has to match modifiers to payer requirements, not just general CPT guidelines. It has to verify that the diagnosis codes attached to each procedure appear on the covered diagnosis list in the relevant LCD. It has to confirm that any product billed by HCPCS code is attached at the right unit of measure. 

In wound care, pain management, DME billing, and other high-complexity specialties, a single encounter can involve six to eight codes with interdependencies between all of them. The relationships between those codes determine the outcome in adjudication. Not any single code in isolation. 

The Structural Gap This Creates

Healthcare providers are investing heavily in AI coding. The category is legitimate, the tools are improving, and the ROI case on documentation efficiency is real. 

What is not being addressed is the layer downstream from coding, where most of the preventable denial volume originates. The claim construction layer. The payer intelligence layer. The place where a correctly coded claim gets assembled into a transaction that either survives adjudication or generates a denial work queue. We unpack this layer in more detail here. 

That gap is not visible in a coding accuracy report. It shows up in first-pass resolution rates, denial reason codes, and the volume of rework that billing teams absorb on claims that came out of the coding workflow clean. 

The industry is measuring the wrong thing and concluding the problem is solved. 

What the Next Generation of Tools Has To Do 

Solving this requires more than better coding models. It requires claim-level intelligence that knows what the payer will do with a specific combination of codes before the claim is submitted. 

That means specialty-specific logic built from inside the revenue cycle, not abstracted from it. Payer-aware validation that enforces coverage policy at the claim level, not just coding accuracy at the code level. Catching the bundling violation, the missing modifier, and the LCD mismatch before submission rather than managing the denial after the fact. 

The providers who figure this out first will not just reduce denials. They will eliminate the category of rework that no amount of denial management can fully recover from. 

The next generation of revenue cycle tools will not be measured by how well they manage denials. They will be measured by how few denials ever exist. 

Revenue integrity is not a post-bill problem. It never was. 

About Jenakan Dev

Dr. Jenakan Dev is the Co-Founder and CEO at Grelin Health, a pre-bill revenue intelligence platform built for physician groups, MSOs, and specialty practices in high-denial verticals. He is a practicing physician and a computer science engineer, a combination that shaped Grelin’s approach to solving revenue integrity from inside the clinical workflow rather than downstream of it.

Knit Health, a health tech company spun out of the University of California, Berkeley, developing collective clinical intelligence, today launched out of stealth with $11.6 million in seed funding. The round was co-led by Uncork Capital and Frist Cressey Ventures, and the pre-seed round was led by Moxxie Ventures, with participation from Coalition Operators. The funding will accelerate deeper development and deployment of Knit Health’s Large Clinical Behavior Model (LCBM) across health systems, helping optimize clinical operations and elevate patient care.

Much of AI in healthcare today relies on large language models primarily trained on text or published literature to support clinical operations and decision-making. This approach overlooks collective clinical intelligence: the patterns of real-world decision-making and coordination embedded in referral habits, scheduling practices, and the informal ways clinicians navigate complex institutions. This knowledge is what ultimately drives better outcomes, ensuring patients are routed to the right care, at the right time, with the right information.

Knit Health is addressing this gap as it builds its LCBM trained on Truveta EMR Data from over 130 million patients across 30 U.S. health systems. Using deep reinforcement learning, causal inference, and behavioral cloning, the model learns directly from clinician decision-making patterns and applies that insight across operational and care delivery workflows – enabling providers to deliver faster, more informed, patient-specific care pathways.

“Much of what matters most in medicine isn’t written in textbooks, it’s learned through experience with time and navigating the healthcare system,” said Jonathan Kolstad, Co-Founder and CEO at Knit Health. “Across millions of patient journeys, clinicians develop patterns for what to do next and when. Knit learns from those real-world decisions, transforming collective clinical experience into intelligence that improves how the system works.”

Knit Health’s approach differs from traditional systems in several key ways:

• Building to a Behavioral World Model: Knit’s LCBM learns from observed sequences of clinical decisions, enabling it to reflect how care actually unfolds in practice rather than producing probabilistic text
• Health System-Specific: Knit fine-tunes to each health system’s practice patterns, capacity constraints, and referral dynamics, allowing it to integrate seamlessly into existing operations and deliver measurable impact from the outset
• Infrastructure Layer to Drive Health Systems: Knit sits beneath every routing decision, every discharge prediction, every care team allocation, every referral, and eventually every clinical workflow that touches a patient; this drives intelligence across the systems

“Knit Health is creating a new approach to AI. Unlike traditional models, it learns and evolves from real human behavior and can be applied across complex systems,” said Tripp Jones, General Partner at Uncork Capital. “This approach redefines how intelligence is captured and scaled, opening entirely new possibilities for AI-driven innovation in healthcare.”

“The hardest challenge in healthcare isn’t knowing what good care looks like; it’s delivering it consistently for every patient,” said Navid Farzad, Managing Partner at Frist Cressey Ventures. “Knit Health’s model embeds the best clinical intelligence directly into the workflow, helping clinicians make better decisions faster and more consistently. At scale, this will improve patient outcomes and transform clinical operations across health systems.”

Knit Health is built with full HIPAA compliance – rigorous governance, bias testing, and continuous monitoring to ensure guidance that patients and providers can trust. The company is partnering with health systems to deploy its initial models for triage, patient flow, and quality improvement. Through these collaborations, Knit aims to become the irreplaceable infrastructure layer that every clinical decision runs on.

Learn more about Knit Health here.

About Knit Health

Founded in 2025, Knit Health is building AI that captures collective clinical intelligence to become the infrastructure for better healthcare. Knit Health’s founders are a team of University of California, Berkeley researchers and academics and together, they bring deep expertise across behavioral economics, causal inference, generative AI, and healthcare. Knit Health was born from their shared vision to harness the capabilities of generative AI in a way that reflects the human behavior and collective intelligence that defines medicine.

Originally announced May 12th, 2026

Here’s a preview of the topics and questions we discuss in this episode:

• How is the healthcare website changing in 2026?
• Where is AI changing how patients seek and get care?
• What marketing message surprised you at the Swaay.Health LIVE conference?
• What are the key takeaways from Swaay.Health LIVE that Health IT leaders need to know?

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

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Telehealth has expanded access to care and fundamentally changed how healthcare organizations deliver services, with projections suggesting telehealth may account for up to 30% of U.S medical visits in 2026. Behind every telehealth appointment is a broad workforce that includes coders, billing specialists, insurance professionals, and contractors – most of whom work remotely, hybrid, or even offshore. This increasingly blended workforce reflects real changes in how care and support services are delivered. As more people rely on telehealth services, there’s a growing demand for professionals enabling virtual care – as well as solutions that ensure data remains secure across these distributed workforces.

However, traditional security model approaches – such as shipping corporate-managed laptops to onboard this distributed workforce, or virtual desktops which rely on remote hosting, are too slow and complex to support the speed and scale needed. This current mismatch is fueling the need for a more flexible, device-agnostic security model that will enforce compliance without restricting how and where work gets done.

Ensuring HIPAA Compliance Across a Distributed Workforce 

With 73% of healthcare leaders expecting half of their workforce to be hybrid in 2026, maintaining HIPAA compliance across diverse devices and environments will be critical for protecting patient data. It’s well known that HIPAA requires organizations to implement safeguards that include strong policies and controls that prevent the unauthorized access, use, or disclosure of electronic protected health information (ePHI). As a default, these responsibilities extend to anyone who accesses this data, including contractors, vendors, offshore workers, and other business associates.

For healthcare organizations, enforcing consistent security policies can be challenging with remote contractors using personal or third-party devices that companies don’t fully control or manage. For an industry severely understaffed, maintaining a high level of control and consistent protection for ePHI is difficult. Without strong access controls and monitoring, the risk of unauthorized access or disclosure increases. 

Legacy security for these scenarios was built around the endpoint. IT leaders would lock down the device, control the hardware, and manage everything installed on it, with the assumption being that protecting the machine protected the data on it. But that model doesn’t hold up when your workforce is spread across geographies, on personal laptops, contractor machines, and home offices.

Ensuring HIPAA compliance in a distributed environment requires a reframe: protecting the work itself. Encrypting and isolating work applications and ePHI directly on the endpoint, regardless of what device is being used to access them.

Enabling Secure, Device-Agnostic Access for Telehealth Workforces

To fully embrace this next phase of telehealth, healthcare organizations need workforce models that are more flexible by design, and the security infrastructure to match. 

Allowing employees and contractors to use their own devices can remove much of that friction. It makes the onboarding process a lot faster and gives organizations more flexibility in how they want to build and scale their workforce. 

But device flexibility only works if compliance travels with it. HIPAA doesn’t change based on who owns the laptop, so the obligation to control access to ePHI applies regardless of whether someone is using a corporate-issued machine or their own. That’s the challenge that a device-agnostic model solves: giving people the freedom to work on any device, while ensuring the organization never loses control of the data.

Building a Secure, Flexible Future for Telehealth Workforces

The practical shift starts at the endpoint. Rather than restricting access by locking down entire devices, IT teams can isolate ePHI and healthcare applications inside a protected workspace directly on the user’s machine, fully separated from personal activity. 

This allows healthcare organizations to onboard remote workers and contractors in minutes instead of days, without shipping laptops or spinning up complex virtual desktops. Offboarding is just as fast, since access can be revoked instantly, with no equipment to retrieve. For an industry dealing with persistent staffing pressure and high contractor turnover, that agility matters.

As telehealth continues to reshape care delivery, the organizations best positioned to scale are those that stop treating device ownership as their security strategy. HIPAA compliance doesn’t require that IT teams control the hardware; it just requires that they keep sensitive data secure, regardless of what device it’s on. A model that isolates and protects ePHI at the endpoint, regardless of what device it sits on, gives healthcare organizations the flexibility to grow their distributed workforce without compromising the trust patients place in them.

About David Matalon

David Matalon is the Founder and CEO at Venn, the leader in flexible workforce enablement. As a 5-time founder, David has experience running companies focused on securing and delivering applications for distributed workforces. He led businesses that worked on virtual desktop (VDI) technologies and security/compliance for remote teams. Venn is the innovator behind Blue Border, a purpose-built technology that creates a secure, IT-controlled work environment on a user’s own PC or Mac—separating work and personal activity without locking down the device or relying on virtual desktops. He earned his undergraduate degree from NYU’s Stern School of Business and holds a master’s degree from Columbia University. You can follow him on LinkedIn here.

Roche announced today that it has entered into a definitive merger agreement to acquire PathAI, a US-based company in digital pathology and AI-powered technology for pathology laboratories and the biopharma industry. This acquisition builds on the successful partnership between Roche and PathAI, established in 2021 and scaled up in 2024 to include the development of AI-enabled companion diagnostic algorithms. Subject to the closing of the transaction, which is expected in the second half of the year, the acquired entity will become part of the Diagnostics division.

This acquisition strengthens Roche’s position in Digital Pathology, which is transforming extensive manual workflows into fully automated, AI-driven processes and insights. Digital pathology enables the creation of high-resolution digital images from physical tissue on slides, allowing pathologists to use AI tools to facilitate diagnostic workflows and provide patients with faster results.

“Digital pathology has the potential to improve precision diagnosis of cancer and enable physicians to offer better-tailored treatment regimens,” said Matt Sause, CEO at Roche Diagnostics. “Bringing PathAI into Roche Diagnostics will allow us to combine their best-in-class digital pathology tools with our leading oncology diagnosis platforms to deliver better insights for physicians and potentially better outcomes for patients worldwide.”

Andy Beck, Co-Founder and CEO at PathAI, adds, “Joining forces with Roche marks a new era for PathAI, enabling us to realise our mission of improving patient outcomes through AI-powered pathology at unprecedented scale and speed. Roche’s global infrastructure and expertise will bring our digital diagnostics technology to patients worldwide.”

PathAI’s AISight IMS software interface is efficient and user-friendly, seamlessly integrating advanced analysis and workflow capabilities within the digital pathology laboratory. In the rapidly growing pathology market, Roche intends to scale this solution globally.

In addition, the expanded capabilities strengthen Roche’s competitiveness in precision medicine by enhancing its biopharma services. PathAI’s strength in AI-driven solutions, including clinical trial support and translational research, will complement Roche’s deep expertise in companion diagnostics. Combining these capabilities will foster the discovery of new biomarkers, potential drug targets, and novel diagnostic tools, increasing the value Roche can bring to biopharma companies.

About Roche

Roche is a healthcare company uniquely placed to prevent, stop, and cure diseases by uniting leading science and technology across diagnostics, medicines, and digital solutions.

Roche was founded in Basel, Switzerland, in 1896 and today is a leading provider of transformative medicines and diagnostics for millions of people in over 150 countries around the world. It is dedicated to tackling healthcare challenges that place the greatest strain on patients, families, communities, and healthcare systems. Across its Diagnostics and Pharmaceutical divisions, Roche focuses on areas including oncology, neurology, cardiovascular and metabolic diseases, ophthalmology, infectious diseases, and immunology with the aim of providing real and positive change for patients, the people they love, and the professionals who care for them.

Genentech in the United States is a fully owned subsidiary in the Roche Group. Roche is the majority shareholder in Chugai Pharmaceutical, a major innovator in the Japanese therapeutic antibody market.

For more information, please visit roche.com.

Originally announced May 6th, 2026

Studies

• While 94% of healthcare organizations said AI agents are “indispensable” to operations, according to a Hyro report, just 59% have standard processes and frameworks to track their success. In addition, Hyro’s AI agents have been integrated with Five9‘s Intelligent Cloud Contact Center.
• A survey from Harmony Healthcare IT found 62% of healthcare technology professionals said legacy data archiving challenges impact patient care; 54% said they can affect care coordination.
• Nearly 3 in 4 (74%) of compromised healthcare devices contain credentials for EHR systems, according to a report from Flare.
• When it comes to managing prior authorizations, 81% of staff spend 5 hours or more each week on fax management, according to a Retarus report.
• More than 1 in 4 Americans (27%) said engagement with social media or AI tools encouraged them to schedule or consider a cancer screening, according to a survey from Mesothelioma.com.

Partnerships

• Pathology reporting technology from mTuitive is now integrated with Fujifilm‘s Synapse Pathology. In addition, Fujifilm launched EB-710US Ultrasound Bronchoscope for looking at a patient’s lungs.
• Clinical research engine Briya is now integrated with InterSystems.
• SandboxAQ integrated its Large Quantitative Models with Claude from Anthropic.
• Smart ring maker Oura is partnering with ResMed, a sleep technology company.
• Cybersecurity vendor IntraSystems is partnering with Goliath Technologies, an EHR optimization company, to form the Strategic Partner Consortium for Healthcare.
• Demand-side platform DeepIntent is integrating OptimizeRx‘s EHR network into its healthcare advertising platform.

Products

• Canvas Medical launched Canvas Studio, a no-code interface for custom workflows within its EHR platform.
• ChiroTouch launched CT Pay, a payment solution embedded in its EHR and practice management software for chiropractors.
• Definitive Healthcare announced product enhancements that include HubSpot and Salesforce integration.
• Flatiron Health launched Flatiron Telescope, a platform for finding patients, assessing study feasibility, and generating oncology insights.
• HOPPR introduced HOPPR EB 2D Mammo Narrative Model to translate mammography images into narrative language describing imaging characteristics.
• Labcorp launched MyLabcorp, a mobile app that brings together lab results, clinical guideline-based content, and AI features.
• Parexel introduced ParexelAI, a suite of services supporting the clinical development lifecycle.
• RedSail Technologies launched RxMarket, a purchasing marketplace for independent pharmacies.
• Price transparency platform Turquoise launched OON Dashboard, a free tool for comparing contracting rates to qualified payment amounts or independent dispute resolution rates.
• Verato is introducing Verato Identity Network, a shared identity infrastructure across healthcare platforms.
• Wolters Kluwer Health launched Lippincott CoursePoint+ Faculty Assistant and expanded Nursing Tutor Usage Insights.

Implementations

• Prime Healthcare selected Collette Health to support virtual sitting and nursing.
• Tribal healthcare operations consultancy Face Rock expanded its RCM collaboration with Inovalon.
• Moffitt Cancer Center expanded its Reimagine Care deployment for virtual oncology care.
• Los Angeles-based home health agency Unison Health Services expanded its adoption of WellSky SkySense AI.

Company News

• 1Kosmos received Epic Toolbox designation for Identity Verification for MyChart.
• BioHeartland launched as a collaboration among the Central Indiana Corporate Partnership and Central Indiana Regional Development Authority.
• ConcertAI expanded its CancerLinQ network, adding University of Rochester Medicine Wilmot Cancer Institute and UCHealth/University of Colorado Anschutz Cancer Center.
• Global Near Field Communication standards body NFC Forum launched a Healthcare Special Interest Group.
• PointClickCare is making Chart Advisor available to senior living communities by the end of this year.

People

• Senior care technology provider LivTech appointed Samantha McAlister as Chief Customer Officer.
• Care coordination technology vendor Watershed Health named Malissa Binkley as Chief Operations Officer and Monica Rivera as Chief Growth Officer.

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