PHTI: AI Reality Opposite of Expectations

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

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

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

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.

Inside the World’s First One-Man AI Unicorn

We officially have our first one-man, billion-dollar AI startup, and it’s in healthcare… for better or worse.

It was hard to be online last week without stumbling across the New York Times profile on “How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company.”

The article tells a shimmering tale of AI entrepreneurship. The 41-year old founder took just two months, $20k, and a dozen AI tools to get his startup Medvi off the ground.

  • Medvi plays at the intersection of two trends that seem almost engineered to mint new billionaires: agentic AI and GLP-1s.
  • The online telehealth provider offers GLP-1s for weight loss, with an army of AI agents handling everything from website copy and design to ad images and customer service.
  • It had 300 customers in its first month, generated $401M in its first full year (2025), and revenue is now on track to hit $1.8B after the founder doubled the headcount by hiring his brother.

Medvi is basically dropshipping healthcare. They’re in the business of acquiring customers, not delivering care. The entire clinical infrastructure runs through turnkey partners.

  • CareValidate manages the physician licensing and prescriptions.
  • OpenLoop handles the pharmacy fulfillment and shipping.

Here’s what NYT forgot to mention. The article notes that Medvi’s founder was “nervous to talk publicly” about the company and hasn’t exactly been waving his momentum around.

Some solid sleuthing from digital health’s finest offers a few hints as to why that might be:

  • Medvi (or partners with a conveniently long leash) spun up 800+ fake doctor Facebook accounts to aggressively advertise their meds. Not great.
  • They’re named in a lawsuit alleging a nationwide scheme to manufacture and promote a fraudulent, unapproved oral tirzepatide pill. Also not great, allegedly.
  • Their onboarding accepted a user with a February 31st birthday, then told them they had a 94% chance of hitting their goal weight of 200lbs starting from 7’11” and 350lbs. Yikes.

Sam Altman called it. The OpenAI CEO was spot on with his prediction that AI would quickly let solo founders generate billions by eliminating the inefficiency seen at larger orgs. Unfortunately in healthcare, much of that “inefficiency” is in place to protect patients.

The Takeaway

Is it impressive that one founder and a little grayhat marketing can now do billions in revenue? Yes. Should we be dropshipping healthcare through predatory ad funnels? Probably not. 

Scribes Show Modest Impact at Major Academics

Ambient scribes are back in the spotlight after a new study in JAMA confirmed that they move the needle on productivity metrics, but the jury’s still out on whether that’s the best yardstick for success.

This was a big one. The study examined the impact of AI scribe use on over 1,800 clinicians at five major academic medical centers from 2023 to 2025.

  • The academics: MGB, YNHH, UCSD, UCSF, UC Davis 
  • The scribes: Abridge, Ambience, Microsoft DAX Copilot

Here’s what they found. Clinicians who used AI scribes:

  • Saved 16 minutes of documentation time per eight hours of patient care 
  • Saved 13 minutes of EHR time 
  • Could see one additional patient every two weeks
  • Saw no significant impact on EHR timeoutside of working hours

Usage patterns helped color in the story. While 1,800 AI scribe adopters is one of the largest samples out there, the 6,770 control clinicians were also offered scribes and opted not to use them.

  • The biggest gains went to the biggest users. Clinicians who used the AI scribe for over 50% of visits experienced twice the reduction in EHR time and 3x the reduction in documentation time, yet only 32% of adopters fell into this bucket.

What’s counted? What matters? This isn’t the first study we’ve covered that scores AI scribes based on metrics that researchers can easily measure (EHR time, visits), which isn’t necessarily the same as the metrics that matter most to patients or clinicians.

  • Although this study solidifies that scribes can cut documentation time, the question now is if that time gets reinvested in ways that improve care and outcomes for patients.
  • The results also confirm that the mechanism of action for scribes reducing burnout isn’t through time savings, but it’s still unclear whether it’s from having a couple more moments to take a deep breath throughout the day or from reallocating the extra minutes to things that feel valuable.

The Takeaway

This study offers the most definitive real-world data yet that AI scribes have a modest impact on productivity metrics, but it also confirms that cleaner notes aren’t the only key to improving healthcare experiences.

Qualified Raises $125M to Build AI Infrastructure

In an era of isolated AI pilots, Qualified Health is building the infrastructure to connect the dots.

AI is the star of enterprise transformation. Health systems are looking to deploy and scale AI across their entire organization, and Qualified just raised $125M of Series B funding to make sure every new agent fits into a cohesive constellation.

The core platform has four distinct layers:

  • A data foundation that turns the EHR and external sources into an AI-ready bedrock.
  • A layer that lets hospitals build and deploy AI tools without always starting from scratch.
  • A layer that turns those tools into AI apps and agents deployed directly into workflows.
  • A layer that keeps governance, monitoring, and evaluation at the center of everything.

Qualified doesn’t leave AI to chance. It embeds forward-deployed product leaders alongside health system teams to identify high-priority needs, deploy solutions quickly, and iterate based on actual feedback in the trenches.

That has a couple of major benefits:

  • AI solutions are purpose-built for specific operational problems rather than mass market appeal.  
  • The tight feedback loop allows Qualified to iterate faster than it would be able to with a traditional implementation cycle, which shortens the timescale needed to improve its deployments and demonstrate a measurable impact.

The proof is in the pudding. At the University of Texas Medical Branch, Qualified reportedly generated a $15M measurable run-rate impact within the first six months.

  • That’s an eye-popping number to get on record, and it apparently stemmed from “a real willingness to dive deep” alongside UTMB clinical teams to deploy multiple assistants and automated workflows.
  • Qualified already supports systems representing about 7% of U.S. hospital revenue, and the next chapter is about deepening those partnerships and scaling responsibly.
  • Big ambition also means big competition, and Qualified will be up against everyone from Innovaccer to Epic if it wants to become healthcare’s AI platform of choice.

The Takeaway

Hospitals aren’t looking to AI for incremental improvement. They’re looking to AI to transform how they deliver care, and Qualified just landed another $125M to be the infrastructure that makes that possible.

Google AMIE Shines in First Real-World Study

The gap between benchmark scores and real-world performance has been the theme of the year in AI research, so Google was right on cue with its first prospective clinical trial for AMIE using actual patients. 

Meet the Articulate Medical Intelligence Explorer. AMIE is Google’s flagship “medical AI researcher,” and it teamed up with Beth Israel Deaconess Medical Center to gauge performance in real clinical workflows.

  • 100 patients completed an AMIE interaction before their primary care visit, with AMIE taking medical histories and equipping patients with potential diagnoses to discuss with their PCP.
  • PCPs received the transcript, summary, and AMIE’s management plan prior to the visit. All interactions were monitored live by physicians trained to intervene if safety criteria weren’t met.

AMIE got a gold star. Not only were there zero safety stops across all 100 interactions, patients reported that their attitudes toward AI significantly improved after chatting with AMIE.

  • AMIE’s differential included the correct final diagnosis in 90% of cases (per chart review 8 weeks post-encounter), with 75% top-3 accuracy.
  • PCPs using AMIE reported increased visit preparedness in 75% of cases, as well as potential behavior change in nearly 60%.
  • The quality of AMIE’s differential diagnosis and management plan appropriateness was similar to PCPs, although PCPs won on management plan practicality and cost-effectiveness.

Other findings were less obvious. PCPs had the chart, the physical exam, and the pre-visit transcript, yet AMIE still matched them on differential quality and management safety without taking a single peak at the EHR.

  • That speaks to the ceiling (or lack there-of) for structured AI history-taking, and shows that AI is gearing up to improve patient care in more ways than just making predictions.
  • The fact that PCPs reported better visit preparedness and potential behavior change in over half of cases also highlights how AI can augment – not just replace – clinical reasoning.

The Takeaway

The distance between the bench and bedside is getting shorter, and Google’s AMIE results suggest that conversational AI in primary care is closer to reality than most people might think.

How to Build Patient Trust in Medical AI

AI might move at the speed of trust, but new research in JAMA Network Open shows that trust only moves at the speed of accuracy.

The study had a solid setup. To determine the factors currently driving patient trust in AI, researchers presented 3,000 U.S. adults with a pair of hypothetical AI-assisted visits for a moderate-risk rash. 

  • Each visit had six randomized attributes, such as whether or not a doctor was present, how well the AI performs relative to human clinicians, and various AI governance mechanisms.

AI performance came out on top by a wide margin. Respondents cared more about how well the AI performs than FDA approval, governance, and even having a doctor in the room.

  • The biggest difference came from AI performing better than a specialist, which increased the likelihood of choosing that visit by 32.5%.
  • AI performing at the same level as a specialist boosted visit preference by 24.8%, slightly more than having AI that performs as well as a general practitioner (19.1%).
  • Having an actual doctor present surprisingly only swayed visit preference by 18.4%.

Governance factors also moved the needle. They just didn’t move it much.

  • FDA approval for the AI increased visit preference by a modest 11.1%.
  • Mayo Clinic AI certifications apparently carry just as much weight – also coming in at 11.1%.
  • Local hospital certifications for the AI only gave visits a 7.8% lift.

AI data quality was important. It just wasn’t as convincing as AI performance. 

  • AI that had nationally representative training data boosted visit preference by 11.9%, but it was interesting to see that disclosing bias in the training data had no effect versus not providing any data details.

The written explanations told the same story. Respondents cited AI performance and clinician involvement as the primary reasons for their choices, with many of them expressing comfort with AI as a tool – but not as a standalone decision-maker.

The Takeaway

Widespread AI adoption requires patient trust, and this study did a great job highlighting the specific areas that should be prioritized for building it.

Microsoft Dragon Copilot Gets AI Upgrades

Microsoft might have had the biggest presence at the biggest health IT conference, and it made sure all the lights in Las Vegas were on Dragon Copilot. 

Unify. Simplify. Scale. Microsoft’s theme at HIMSS was all about making Dragon Copilot a one-stop-shop for information within clinical workflows. It debuted several new capabilities at the show:

  • Integrated medical content from trusted sources
  • Partner-powered AI apps and agents
  • Proactive ICD‑10 specificity suggestions
  • Expanded role-based experiences for physicians, nurses, and radiologists

Partnering is quicker than building. Rather than developing every Dragon Copilot capability in-house, Microsoft has been leaning on outside partners to round out the platform.

  • Dragon Copilot’s clinical evidence feature is a prime example. It brings medical content and other relevant contextual information in-workflow, all curated through new partnerships with Wolters Kluwer, Elsevier, and other vetted sources.

Microsoft Marketplace fills the gaps. It allows users to add AI partner apps directly into their Dragon Copilot workflows. Picture a modular side panel with insights from folks like: 

  • Regard – surfaces comorbidities and relevant diagnoses 
  • Canary Speech – analyzes voice biomarkers for mental health conditions
  • Humata Health – automates prior authorization processes for clinicians 
  • Atropos – generates personalized real-world evidence 
  • Optum – identifies potential coverage issues and supports claims processing 

All roads lead to scribes. When Microsoft first acquired Nuance for $20M back in 2022, it was its second largest acquisition ever behind LinkedIn, and the core offerings were radiology report automation, dictation, and transcription (with humans still pulling a ton of weight).

  • The product formerly known as Dragon Ambient eXperience is now the backbone of Dragon Copilot, and it’s been adding features at a breakneck pace.
  • Microsoft is looking to make Dragon Copilot everything, everywhere, all at once, and so far new partnerships have been the key to making that happen.

The Takeaway

As every digital health company rushes to add scribing to their platform, the OG scribe is rushing to add everything else. Now it just needs to maintain a unified user eXperience.

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