Category: Digital Health

2026 Healthcare Forecast Cloudy, But AI Rays Could Poke Through

Posted on by Jack Troy

Venrock’s 10th annual survey of healthcare insiders reveals they’re a pessimistic bunch lately, harboring cynicism about recent policy developments and the future of health tech IPOs, though views on AI were more of a mixed bag. Let’s break down the results. 

But first, a bit about the survey. More than 200 leaders from all corners of healthcare shared their thoughts with Venrock. Some areas were better represented than others.

  • Respondents skewed toward the private sector (28%), investing (20%), life sciences or pharma (16%), professional services (8%), and academia (7%). 

Venrock loaded up the questionnaire with AI inquiries. Big picture: Insiders are becoming more comfortable with the tech, but remain mindful of its downsides. 

  • Trust in AI grew for 73% and fell for 2% (unclear who hurt them). 
  • Just 9% view AI as the most overrated trend in healthcare.
  • HIPAA breaches (24%), harmful hallucinations (19%), and overspending on healthcare-specific platforms (28%) ranked highest among possible AI pitfalls. 
  • Most expect AI to create an costly arms race between payers and providers (63%) as each side rolls out bots specifically designed to argue with other bots.

Here’s another fun one: M&A targets. There’s no consensus on who will get snapped up next, but the industry seems confident it will be a big name in AI-powered services. 

  • OpenEvidence (21%), Komodo Health (18%), Abridge (14%), and Sword Health (11%) were the top answers out of 10 companies, but only after none of the above (46%).

So the hottest firms are going public? Nope — that’s one thing people can agree on. 

  • Only 3% think health tech IPOs will be back in style this year, with the rest split roughly down the middle between 2027 and 2028 or beyond. 
  • For those keeping score, 42% of last year’s respondents predicted a health tech firm would go public in the first half of 2026. Tumbleweeds…

In fairness, it’s hard to predict the future, especially with $1.15T in Medicaid cuts looming over everyone’s heads. 

  • Will they harm rural hospitals? Empty state coffers? Ruin MCOs? Strain blue-state safety net hospitals? Most checked all of the above (65%). 

The Takeaway

Some of the smartest folks in healthcare think we’re heading toward a world of payer-provider bot wars, sluggish health tech IPOs, and brutal fallout from Medicaid cuts. Here’s to hoping Venrock’s survey missed the mark. 

Rock Health H1 2026: Durable Roots, Shifting Routes

Posted on by Jason Barry

Rock Health just dropped its H1 digital health funding overview, and the halftime report shows a market that knows where it’s going, even if the way it gets there keeps changing.

Here’s H1 2026 by the numbers:

  • Digital health startups raised $7.4B across 244 rounds (up $1B from H1 2025).
  • Median round size climbed from $12M to $14M, the highest since 2022.
  • 19 companies raised 20 mega-rounds, accounting for 45% of all capital invested.

That last stat is starting to look familiar. Capital concentration was the headline of the Q1 report, and the trend hasn’t let up. 

  • Just over 8% of rounds absorbed nearly half the capital, and some companies aren’t even waiting a full year between nine-figure checks.
  • Garner Health’s $100M Series E landed three months after its Series D, and Aidoc grabbed its second $150M in under a year.

Mental health is still the belle of the ball. It was the top-funded clinical indication for the seventh straight year, led by Talkiatry ($210M) and Grow Therapy ($150M).

  • Weight management took the silver on the back of the insatiable appetite for GLP-1s.
  • Both categories share a secret weapon: 64% of their H1 raisers go direct-to-consumer, versus 29% of digital health overall.

The IPO drought is creeping back. After seven exits last year, 2026 hasn’t produced a single digital health IPO, and Oura’s S-1 is the only one on file.

  • This year’s exit action is all M&A. H1 saw 115 acquisitions, including 71 in Q2 alone (the busiest quarter since 2021), with revenue cycle management consolidating fastest.

Where are all the moats? The H1 report’s big question was what actually counts as a durable advantage now that “we have AI” no longer moves the needle.

Rock Health landed on four answers and one graphic: 

  • founders with real domain expertise (sharpens product and buyer relationships)
  • platforms scaling to own more workflows (and more context to coordinate tasks)
  • hands-on delivery (forward-deployed engineers are officially a healthcare job)
  • network effects (every new partnership builds on the last so competitors can’t catch up)

The Takeaway

AI made digital health products easier to build than ever, which means the products themselves are no longer the moat. Investors are looking for the same fundamental roots – teams, trust, and traction – but the routes to find them are shifting faster than ever.

Behavioral Health Moves From Access to Accountability

Posted on by Jason Barry

The pandemic-era land grab in digital behavioral health is behind us, and a beautiful report from 7wire Ventures makes the case that the next chapter will be written by the platforms that can prove people actually get better.

Access was just the start of the story. Hundreds of behavioral health companies launched on the premise that technology could improve access at scale, and the capital followed. Digital mental health raised $4.9B in 2021 alone.

  • Despite the investment surge, the access problem is still very real. Nearly 1 in 4 U.S. adults experienced a mental health condition in 2024, yet almost half received no treatment.
  • The problem is that utilization growth outpaced meaningful clinical outcomes, and population-level ROI remained tough to verify.

The market is adjusting accordingly. Payors and self-insured employers are now conditioning contracts on measurement-based care – PHQ-9s, functional improvement data, and documented reductions in downstream medical costs – while commercial models shift from PMPM setups toward case rates and performance guarantees.

  • First-generation platforms that scaled enrollment without outcomes infrastructure are struggling to keep pace, and behavioral health M&A jumped 42% in 2025 as a result.
  • Recent tie-ups like Spring Health and Alma plus UHS’ $835M acquisition of Talkspace suggest the fragmented point-solution era is winding down in favor of integrated platforms.

AI is the primary lever. The provider shortage isn’t budging (HRSA projects a 43k psychiatrist shortfall by 2038), so the most scalable near-term play is technology that extends existing clinicians rather than trying to bypass them.

  • The report draws a sharp line between supervised AI (where a licensed clinician stays accountable for diagnosis and escalation) and autonomous agents, with the supervised camp winning payor confidence and reimbursement traction.

One prediction worth flagging: 7wire expects outcomes data to become the primary basis for payor and employer contract decisions, turning measurement-based care from a differentiator into table stakes that determines who even makes the RFP shortlist.

The Takeaway

Behavioral health went from digital health’s most funded clinical indication to a market where enrollment numbers no longer impress anyone. Access got the industry to the starting line, but the platforms that survive the accountability era will be the ones that can show their work – to payors, to employers, and ideally to patients.

Cadence Lands $100M to Scale Chronic Care

Posted on by Jason Barry

One of the hottest names in chronic care just got nine-figures hotter after Cadence hauled in $100M of Series C funding to scale up with AI agents.

Welcome to the unicorn club. The round vaulted Cadence’s valuation to $1.2B as it looks to extend the reach of its Clinical Intelligence platform to more patients managing chronic conditions like hypertension, diabetes, and heart failure.

  • The platform is purpose-built to deliver continuous support between visits, with regular vital monitoring and AI agents that translate the data into timely medication adjustments and personalized lifestyle coaching. 
  • Cadence integrates directly with its partners’ EHRs and clinical workflows, then equips every patient with connected devices that allow its system – supervised by physicians – to flag high-risk patients and predict adverse events before they happen.

Cadence lets its numbers do the talking. It’s easy to see why.

  • 70% relative improvement in blood pressure control (JACC).
  • 27% fewer hospital admissions (Mayo Clinic Proceedings).
  • 230% increase in heart failure patients on GDMT (JCF).
  • $1,300 annual cost reduction per patient (Circulation).

Now it’s time to scale. Cadence is already treating 100k patients, and this investment will support the infrastructure “to treat millions.”

  • The current partner roster includes over 20 major health systems, including new additions like Texas Health Resources and Duke Health, which liked working with Cadence so much that it participated in the round.
  • The word on the street is annualized revenue is pacing $140M for FY 2026, more than double the $62M it brought in last year.

Last but not least. Scaling from thousands of patients to millions of patients is no small task, and Cadence is working on deploying new AI agents to help carry the load.

  • A new prescription hypertension management agent is reportedly en route, which “pre-authorizes” medication recommendations to steer patients toward their blood pressure goals, with new prescriptions and dose changes made autonomously by the AI.
  • That falls squarely in medical device territory, and Cadence is actively working with regulators to bring it to patients – including an application to the FDA’s TEMPO pilot that will let certain devices for chronic care on the market before they’re officially approved.

The Takeaway

Chronic disease is the single largest driver of healthcare spending in America, and a significant amount of that cost is avoidable. Cadence is building the tools to avoid it, and another $100M should only help the cause.

Easing the Data Burden in Diabetes Care

Posted on by Partner Content

By Mark Clements, M.D., Ph.D. and Trisha Martinez , RN, BSN, MBA, Glooko

Health systems are not short on diabetes data. They are short on the time, workflows, and signal clarity needed to act on it.

That distinction matters. Continuous glucose monitoring (CGM), connected devices, remote uploads, insulin delivery data, and inpatient glucose trends are generating more information than ever before.

In Glooko’s latest Annual Diabetes Report, more than 60 billion CGM readings flowed through the platform in 2025, supported by more than 1 million active patients, 30,500 clinicians, 9,000 clinics, and a global footprint across 1,082 geographic locations.

  • The scale is no longer the story, but what health systems do with that scale is.

For leaders focused on digital transformation, the opportunity is to shift diabetes management from retrospective review to prioritized action.

  • Traditional measures such as Time in Range (TIR), Glucose Management Indicator (GMI), Time Below Range (TBR), and Time Above Range (TAR) remain essential, but they can obscure when risk occurs, how severe it is, and which patients need attention first.
  • Two patients may look similar by familiar metrics, yet one may face recurring overnight lows while another carries persistent daytime hyperglycemia.

The report’s overnight hypoglycemia analysis illustrates why this matters. Glooko’s model surfaced patients who appeared near target by common measures but had substantially higher overnight hypoglycemia exposure.

  • In validation across 586,549 patient weeks, the highest-risk group showed a 2.79x lift in identifying observed overnight hypoglycemia compared with baseline selection.
  • This is where digital transformation becomes clinical transformation: turning connected data into prioritized lists, cohort-level visibility, and workflows that help teams intervene between visits.

The same safety lens extends into the hospital. EndoTool provides an inpatient view of glycemic management, supporting individualized insulin dosing during complex episodes such as Diabetic Ketoacidosis (DKA) and Hyperosmolar Hyperglycemic State (HHS), renal impairment, steroid exposure, and changing nutrition status.

The Takeaway

The next chapter of diabetes care will not be defined by more dashboards. It will be defined by connected intelligence that helps health systems identify risk earlier, focus clinical attention, reduce cognitive burden, and support safer decisions across the hospital, clinic, and home.

Learn how Glooko is partnering with organizations like yours to transform diabetes care.

Medicare’s None the WISeR

Posted on by Jason Barry

Washington state just delivered an unfortunate crash course on U.S. health policy after the model aimed at “Wasteful and Inappropriate Service Reduction” led straight to higher costs and fewer treatments for seniors.

Does Medicare need prior authorizations? CMS designed WISeR to find an answer by testing whether bringing AI-driven prior auths (which are already widespread among private payors in Medicare Advantage) to traditional Medicare could cut down on wasteful spending.

  • The six-year pilot kicked off in six states on January 1st (AZ, NJ, OK, OH, TX, WA), targeting a list of 13 “low value” services with a high potential for fraud or waste – most notably orthopedic pain management procedures and skin substitutes.

Washington is already tapping out. Less than five months in, Senator Maria Cantwell (D-Wash.) had enough data to publish her new report on the “clear risks of AI in Medicare.”

  • Drawing on a Washington State Hospital Association survey of 16 hospitals, the report found that procedures previously approved within days are now taking 4 to 8 weeks. 
  • CMS’ own WISeR standards call for responses to providers within 1 day for urgent care and 3 days for routine care, both of which are now clocking in at 15 to 20 days.

You get what you pay for. WISeR compensates third-party administrators for each claim they deny, under the assumption that these denials account for the reduction in wasteful spending.

  • That obviously creates some adverse incentives, which the report eloquently framed up by saying the model “incentivizes WISeR contractors to weaponize AI-driven medical determinations not for the sake of efficiency… but to maximize profitability.”
  • As a result, Washington hospitals have had to add staff and increase hours to manage the surge in prior auths – not a great formula for lowering the cost of care.

The report went straight to the top. At a Senate hearing last week, Senator Cantwell made her case directly to HHS Secretary RFK Jr., who said “that kind of delay is unacceptable.”

  • He went on to say that prior auths are there to prevent the government from being “ripped off” by unethical providers and only applies to 5% of services in Medicare.
  • That might be accurate, but it doesn’t mean they aren’t high-volume services. A separate KFF analysis found that 86% of the 1.1M Medicare beneficiaries that used at least one of the services on WISeR’s list in 2024 received a pain management service.

The Takeaway

Reducing waste in Medicare is a worthy goal, but so far it looks like the best way to make it happen probably isn’t by adding prior auths to the program that many seniors specifically chose to avoid them.

PHTI: AI Reality Opposite of Expectations

Posted on by Jason Barry

AI promised less friction and lower administrative costs, but a new report from the Peterson Health Technology Institute suggests that it might actually be delivering the exact opposite.

The report stems from a stakeholder workshop that PHTI held to uncover AI’s impact on two of healthcare’s most hotly debated administrative processes: prior authorization and medical coding.

The main finding highlights an obvious predicament. Speeding up flawed processes doesn’t make them any less expensive. PHTI didn’t pin the blame on either side of the AI arms race.

  • It found that payors are (mostly) using AI responsibly. They’re accelerating PA reviews and auto-approving more clean cases, while simultaneously improving code validation and risk adjustment – although the DOJ would probably disagree.
  • Providers are also using their AI superpowers for good. They’re automating the PA workflows driving burnout and streamlining the coding processes that take clinician time away from patients.

That almost sounds like it should create some efficiency. The problem is that it’s the system that’s broken, and AI doesn’t fix the underlying issues.

  • The report pointed out how AI tools for providers caused an uptick in billing intensity, which payors naturally responded to with across-the-board downcoding and other reimbursement reductions.
  • AI might also reduce the cost for individual orgs to execute or appeal prior auths, but it won’t impact costs for the overall system if nothing gets passed on to patients.
  • PHTI believes this makes reimbursement policy the strongest lever that can realistically be pulled to slash system-level spending.

Follow the incentives. Or in this case, the lack thereof. 

  • On paper, payors and providers should be competing for a finite pool of patients in an arena that rewards better products with smaller price tags. If AI cuts costs, providers would be able to bill less and payors could lower premiums.

Efficiency doesn’t translate to deflation. Payors or providers are rational market actors, and if AI can streamline a process that lets them hold onto more of their revenue, then that’s exactly what they’ll do.

The Takeaway

Bots arguing with bots might be faster than humans arguing with humans, but PHTI doesn’t see that eliminating friction from the overall system if nobody has any incentivize to make it happen. 

The Rise of the Generalist-Specialist

Posted on by Jason Barry

Healthcare’s tidy hierarchy of specialties was formed by cognitive necessity. The corpus of medical knowledge is too massive for a single person to master and the clinical workforce was organized around it, but a new article in Health Affairs says it might be time for a redesign if AI removes that constraint.

AI is scaling specialist-level knowledge. Leading models are coasting through Board exams and polishing their clinical capabilities, which the authors argue will quickly scale specialist-level knowledge to the point where most specialty care can be delivered by PCPs.

  • They coined the term “generalist-specialists” for a new category of doctors that transcends narrow specialty definitions.

Clinical expertise is increasingly democratized. The authors see a future where AI-augmented clinicians can manage the full constellation of patients’ chronic conditions within disease-based domains rather than organ-specific specialties. They give a few examples:

  • Cardiometabolic Diseases – combines cardiology, endocrinology, and nephrology
  • Infectious & Inflammatory – rheumatology, infectious disease, & gastroenterology
  • Primary Care: spans OB/GYN, internal medicine, and pediatrics.

That could have some major benefits. Instead of shuffling a diabetic patient between an endocrinologist, cardiologist, and nephrologist, a generalist-specialist could manage the full cardiometabolic picture.

  • That means fewer handoffs, faster diagnoses, and lower co-pays. It would also unlock a ton of specialty capacity for the patients that need it most.
  • Consolidating care under fewer clinicians would also be a tailwind for value-based care, although it would likely increase utilization in a fee-for-service world by converting deferred, fragmented, or incomplete care into a cohesive billable treatment.

AI isn’t the only barrier to making that happen. Everything from med schools and malpractice standards to credentialing and referral systems would need to be completely overhauled.

  • The generalist-specialist vision also assumes that specialists will be on board with either becoming quasi-PCPs or upskilling to ultra-complex care. Definitely not a given.
  • Patient safety concerns also go without saying, but the AI will probably be pretty decent by the time we have cardio-endocrin-nephrologists putting together the care plans.

The Takeaway

AI could easily bring specialist knowledge to generalist fingertips, but if overworked PCPs are going to start also being OB/GYNs it will take more than a fancy LLM to get there.

Why AI Vendors Struggle to Compete With EHRs

Posted on by Jason Barry

Anyone who has ever tried selling AI into health systems will tell you that it’s tough to compete with EHRs, but a new article in JAMA makes the case that it’s actually gotten too tough – and it might be time for regulators to step in.

Most markets reward the best products. The healthcare industry has a funny way of preventing that from happening, and EHR vendor dominance is a textbook example.

  • EHRs hold advantages across infrastructure, workflow integration, procurement, and pricing that make it difficult for third-party tools to gain a foothold.
  • A 2025 Health Affairs study backed that up by showing that 79% of U.S. hospitals use AI models from their EHR vendor, compared to just 59% that use AI from third-party developers.
  • A Bain report drove the point home. Two-thirds of Epic customers said they’d pick a “good enough” Epic option over a better competing product.

These EHR advantages are a natural feature of the market. That said, it’s up to regulators to decide whether the status quo is serving patients and the overall healthcare system. The JAMA authors argue that it doesn’t, and offer three areas where targeted policy could level the playing field.

Infrastructure – Integrating AI tools into clinical workflows requires real-time data access and the ability to survive EHR upgrades intact, both of which are dramatically easier for EHR vendors – particularly as data fields get added or removed.

  • Potential Policy – Mandate broader API adoption so third parties can access EHR data on equal footing, and use existing EHR certification and interoperability frameworks to do it.

Workflow and Usability – The authors specifically flag EHR vendors’ edge in understanding the trade-offs of allocating limited screen real estate to new AI tools, something that’s harder for third parties to gauge from the outside looking in.

  • Potential Policy – Require EHR vendors to offer more robust developer sandboxes – similar to Apple’s iOS developer environment – so third parties can build and test without operating at a structural disadvantage.

Procurement and Pricing – Long-standing health system relationships give EHR vendors a streamlined path through procurement, as well as the leverage to “use pricing structures that incentivize adoption.”

  • Potential Policy – Although this is the hardest area for a policy fix, the authors suggest that improving transparency around AI performance could at least help health systems make more informed decisions regardless of where a tool comes from.

The Takeaway

EHRs are in a powerful position, and companies in powerful positions have a long track record of making life harder for their competition. Healthcare is too important of an industry to not have the best products rise to the top, and this article offers some sound strategies to make sure that stays possible.

Rock Health Q1: Capital Continues Concentrating

Posted on by Jason Barry

Spring is finally here, and Rock Health’s Q1 funding recap shows that the investing landscape is definitely looking greener than last year.

Digital health startups raised $4B on the dot. That’s a whole billion higher than Q1 2025, although the gains were far from evenly distributed.

Here’s Q1 2026 by the numbers:

  • Digital health funding totaled $4B across 110 rounds (vs. $3B and 122 rounds last year).
  • Average round size climbed to $36.7M (highest since Q4 2021).
  • Rock Health counted 12 mega-rounds over $100M.

That last bullet defined the quarter. A dozen companies accounted for 59% of all capital deployed in Q1, one of the highest concentrations Rock Health has ever seen.

  • Round sizes have consistently increased every quarter since 2024, and there haven’t been this many nine-figure checks in a quarter since the pandemic peak in 2022. 
  • Whoop landed $575M at a $10B valuation, Verily raised $300M as it steps out from under Alphabet’s umbrella, and OpenEvidence’s fundraising blitz added another $250M.

The check sizes only tell half the story. One of the reasons why startups are raising bigger late-stage rounds is because they’re waiting longer to go public. 

  • Hinge and Omada broke the ice, but all it took was a little “geopolitical uncertainty” to spook investors and close the IPO window right behind them.
  • If the rest of 2026 pans out like the first quarter, we’d see close to 50 mega-rounds, almost double last year’s count.

AI is now the operating environment. The tech has become so ubiquitous that Rock Health said it will no longer be using “AI-enabled startups” as a distinct category in its funding reports.

  • The broader market remains bullish on the value of AI, but if everyone has it then it stops being a differentiator.
  • The AI startups successfully raising are the ones moving earliest into complex use cases, like Doctronic’s prescribing pilot in Utah or Qualified Health’s governance platform for health systems.

The Takeaway

Q1 mostly brought more of the same. Investors are active but selective, and the chasm between the Davids and Goliaths isn’t getting any smaller. AI is helping startups move faster than ever, but the rest of the year should help clarify whose momentum is actually durable.

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