Month: April 2025

The Healthcare AI Adoption Index

Posted on by Jason Barry

Bessemer Venture Partners’ market reports are always some of the best in the business, but its recent Healthcare AI Adoption Index might just be its finest work yet.

The Healthcare AI Adoption Index is based on survey data from 400+ execs across Payors, Providers, and Pharma – breaking down how buyers are approaching GenAI applications, what jobs-to-be-done they’re prioritizing, and where their projects sit on the adoption curve.

Here’s a look at what they found:

  • AI is high on the agenda across the board, with AI budgets outpacing IT spend in each of the three segments. Over half (54%) are seeing ROI within the first 12 months.
  • Only a third of AI pilots end up reaching production, held back by everything from security and data readiness to integration costs and limited in-house expertise.
  • Despite all the trendsetters we cover on a weekly basis, only 15% of active AI projects are being driven by startups. The rest are being built internally or led by the usual suspects like major EHRs and Big Tech.
  • That said, 48% of executives say they prefer working with startups over incumbents, and Bessemer encourages founders to co-develop solutions with their customers and lean in on partnerships that provide access to distribution, proprietary datasets, and credibility.

The highlight of the report was Bessemer’s analysis of the 59 jobs-to-be-done as potential use cases for AI. 

  • Of the 22 jobs-to-be-done for Payors (claims, network, member, pricing), 19 jobs for Pharma (preclinical, clinical, marketing, sales), and 18 jobs for Providers (care delivery, RCM) – 45% are still in the ideation or proof of concept phase.
  • Providers are ahead in POC experimentation, while most Payor and Pharma use cases remain in the ideation phase. Here’s a beautiful look at where different use cases stand.

Bessemer topped off its analysis with the debut of its AI Dx Index, which factors in market size, urgency, and current adoption to help startups map and prioritize AI use cases. One of the best graphics so far this year.

The Takeaway

Healthcare’s AI-powered paradigm shift is kicking into overdrive, and Bessemer just delivered one of the most comprehensive views of where the puck is going that we’ve seen to date.

Peterson Center on Healthcare Takes Aim at Remote Patient Monitoring

Posted on by Jason Barry

The Peterson Center on Healthcare is back with another report that doesn’t mince words when it comes to remote patient monitoring: “RPM reimbursement in Medicare is misaligned with the clinical value of these tools.”

The report synthesized Peterson’s past evaluations of diabetes, hypertension, and musculoskeletal solutions, which encompassed the trio of conditions associated with the highest spending and utilization for remote monitoring services.

Although only ~1% of Medicare patients are in RPM programs, utilization has grown exponentially since the start of the pandemic.

  • There’s been a tenfold increase in Medicare patients using RPM services from 2019 to 2023 (44k to over 451k patients). 
  • Medicare expenditures for RPM skyrocketed from $6.8M to $194.5M over the same time period.

There’s also been a steady rise in long-term monitoring, even though research suggests most of RPM’s benefits accrue within the first few months.

  • The average RPM episode duration was just 1.7 months in 2019, jumping to 5.2 months by 2023. Over 22% of RPM episodes now last over nine months.
  • There’s currently no limit on how long providers can use RPM for a specific patient, meaning they can be reimbursed “on a monthly basis in perpetuity for anyone with a diagnosed chronic condition, even if they are already well-managed.”

PCH offered three recommendations to policymakers to combat these issues:

  • Base coverage and reimbursement on proven clinical value for different conditions. 
  • End indefinite billing practices for “forever codes” that encourage waste. 
  • Improve data collection to enable evidence-based decision making about the value and use of remote monitoring. 

The Takeaway

As providers adopt new technologies that extend care beyond the walls of the hospital, aligning incentives with high-value care sounds like an obvious approach to reimbursement, but PCH’s report makes it clear that current policies need an overhaul if they’re going to maximize the effectiveness of RPM programs.

hellocare.ai Growth Round Fuels Smart Hospital Transformation

Posted on by Jason Barry

The shift from point solutions to platform approaches has been one of the defining trends of the past few years, and hellocare.ai is positioning itself to capitalize on the transition with $47M of growth funding from prominent health system backers.

hellocare.ai’s unified virtual care platform transforms any hospital room into a connected care environment, enabling a wide range of use cases seamlessly embedded into existing EHRs, infrastructure, and care delivery models. That includes:

  • Virtual Nursing
  • Virtual Sitting
  • Virtual Consultation
  • Ambient Documentation
  • Hospital-at-Home

By keeping its full technology stack in-house, hellocare.ai aims to differentiate itself through speed, customization, and continuous innovation.

  • Everything from the hardware and software to the EHR-integration engine is baked into the platform to make it as enterprise-ready as possible for smart hospitals.
  • By pairing the platform with a flexible hybrid clinical care team model, hellocare.ai enables health systems to quickly scale AI-enabled hybrid care across their entire organization.

Over 70 health systems are already using hellocare.ai to consolidate various telehealth solutions – many of which were adopted out of pandemic-driven necessity – into a unified experience for both patients and clinicians.

  • AdventHealth not only participated in the round, but also announced that it’s rolling out hellocare.ai across more than 50 hospitals and 13k patient rooms as part of an enterprise-wide implementation.
  • Both Bon Secours Mercy Health and UCHealth also came on as investors and are actively deploying the platform across their systems. Votes of confidence don’t get much stronger than that.

The Takeaway

Health systems have been vocal about needing a unified virtual care platform to simplify care delivery and increase patient engagement – and that’s exactly what hellocare.ai designed its platform to do. Meeting those needs while demonstrating a measurable ROI is another challenge entirely, but hellocare.ai’s investor roster is a clear sign that some major players believe in its roadmap.

K Health’s AI Clinical Recommendations Rival Doctors in Real-World Setting

Posted on by Jason Barry

Real-world comparisons of AI recommendations and doctors’ clinical decisions have been few and far between, but a new study in the Annals of Internal Medicine gave us a great look at how performance stacks up with actual patients.

The early verdict? AI came out on top, but that doesn’t mean doctors should pack their bags quite yet.

Researchers from Cedars-Sinai and Tel Aviv University compared recommendations made by K Health’s AI Physician Mode to the final decisions made by physicians for 461 virtual urgent care visits. Here’s what they found:

  • In 68% of cases, the AI and physician recommendations were rated as equal
  • AI rated better on 21% of cases, versus just 11% for physicians 
  • AI recommendations were rated “optimal” in 77% of cases, versus 67% for physicians

Although AI takes the cake with the top line numbers, unpacking the data reveals some not-too-surprising strengths and weaknesses. AI was primarily rated better when physicians:

  • Missed important lab tests (22.8%)
  • Didn’t follow clinical guidelines (16.3%)
  • Failed to refer patients to specialists or the ED if needed (15.2%)
  • Overlooked risk factors and red flags (4.4%)

Physicians beat out AI when the human elements of care delivery came into play, such as adapting to new information or making nuanced decisions. Physicians were rated better when:

  • AI made unnecessary ED referrals (8.0%)
  • There was evolving or inconsistent information during consultations (6.2%)
  • They made necessary referrals that the AI missed (5.9%)
  • They correctly adjusted diagnoses based on visual examinations (4.4%)

While the study focused on the exact types of common conditions that AI excels at diagnosing (respiratory, urinary, vaginal, eye, and dental), it’s still impressive to see the outperformance in the messy trenches of a real clinical setting – a far cry from the static medical exams that have been the go-to for similar evaluations. 

The Takeaway

For AI to truly transform healthcare, it’ll need to do a lot more than automate administrative work and back office operations. This study demonstrates AI’s potential to enhance decision-making in actual medical practice, and points toward a future where delivering high-quality patient care becomes genuinely scalable.

Rock Health Q1 2025 Funding Recap, Late-Stage is Back

Posted on by Jason Barry

In a first quarter packed with uncertainty and policy shifts, digital health didn’t skip a beat.

Rock Health’s Q1 Digital Health Market Update counted $3B in venture funding across 122 rounds (up from $2.7B in Q1 2024), and it sounds like there’s finally some optimism in the air again.

Early-stage startups dominated the deal count, with Seed, Series A, and Series B raises comprising 83% of labeled rounds in Q1 (in line with 86% last year).

  • Those included some extra-large investments like Achira’s $33M Seed, Open Evidence’s $75M Series A, and Hippocratic AI’s $141M Series B.

The bigger story was the triumphant return of late-stage mega-rounds, headlined by Innovaccer’s $275M Series F and Abridge’s $250M Series D.

  • While Q1 only clocked five raises that were Series D or later, this cohort lifted the quarter’s median Series D+ round size to $105M – almost double the $55M median Series D+ size seen in 2024.

Success in this climate requires “leapfrogging.” Rock health devoted a large section of the report to its four strategies for leapfrogging over market shifts using their unique circumstances.

  • Tapestry Weaving – using M&A to integrate new features and offerings into your capability mix. Of the 46 M&A deals tracked in Q1, 67% involved digital health startups acquiring other digital health startups, up from 53% across 2024.
  • Modular Tech Stacks – designing flexible infrastructure that reduces dependencies and allows for new integrations. Lumeris’ newly introduced Tom AI platform is a perfect example, leveraging capabilities of 60+ LLMs depending on use case.
  • Platforms and Channel Partners – building platforms that can plug in channel partners and key experiences. Q1 was brimming with good examples, including Eli Lilly bringing GLP-1 partners onto Lilly Direct and Teladoc expanding its Connected Care Program.
  • Engaging Disruptors – embracing solutions that challenge the status quo. Rock Health highlights Labcorp’s participation in Teal Health’s $10M raise, which proactively aligned it with an in-home cervical cancer screening startup that’s disrupting traditional pap tests.

The Takeaway

Following a year of valuation corrections and down-rounds, digital health VCs are showing signs of life, but we’ll have to wait until Rock Health’s next report to see if the momentum can stand up to a trade war.

Thatch Raises Series B to Dislodge Health Coverage From Employment 

Posted on by Jason Barry

Help is on the way for employers grappling with rising healthcare costs after Thatch locked in $40M of Series B funding to help tailor benefits to the needs of employees.

Thatch is dislodging health coverage from employment by providing individual coverage health reimbursement arrangements (ICHRA) that let employees choose their own benefits.

  • By blending fin-tech and health-tech tools, Thatch gives employers a way to “abstract away the complexity” of the ICHRA law that passed in 2020, which enabled them to provide a monthly budget to employees for selecting their own health benefits.
  • The Thatch platform streamlines budget setting, plan selection, and lowers costs through pooled purchasing power. If employees spend less than their budget, they receive a Thatch debit card to use for things like prescriptions, copays, and therapy.

Thatch CEO Chris Ellis shared on LinkedIn that one of the reasons why health coverage feels so broken is because it wasn’t designed for humans, it was designed for HR departments. 

  • Ellis has a different vision, and it’s being met with open arms: “Imagine choosing your health plan like you choose your car. Imagine keeping it when you switch jobs.”
  • Although Thatch hasn’t revealed any official revenue numbers, it’s reportedly onboarded over a thousand companies in the last 18 months – including big names like Jersey Mike’s and Dave’s Hot Chicken – and grown revenue by 8X year-over-year.

The Series B funding will allow Thatch to “double down” on its vision to make health benefits  accessible for every American through deeper integrations with carriers and payroll systems.

  • That includes an API service that allows partners to embed ICHRA directly within their own product, which already has QuickBooks signed on as a marquee client.
  • Thatch is also bringing on Gary Daniels, the former CEO for UnitedHealthcare’s Pacific Northwest division, as its new Chief Growth Officer to help make it all happen.

The Takeaway

Most current health benefits solutions were designed for a workforce that stayed with a single company for most of their careers, and have had a tough time keeping up with today’s dynamic labor market. Thatch is among a new pack of startups building the infrastructure for a modern experience, and it seems like both employers and employees have a lot to look forward to.

Layer Health Takes AI to Chart Review With Series A

Posted on by Jason Barry

The healthcare AI momentum isn’t showing any signs of letting up, and chart review automation startup Layer Health just added another $21M to the segment’s quickly growing venture total.

Layer’s AI platform leverages LLMs trained on longitudinal patient data to automate data abstraction for the medical chart reviews that underpin a wide range of clinical and administrative workflows, including: 

  • Quality Reporting & Clinical Registries – extracting data from clinical registries and quality measurement programs to improve accuracy and ensure compliance
  • Hospital Operations & Revenue Cycle – enhancing clinical documentation integrity and coding processes to optimize reimbursement and reduce denials
  • Clinical Decision-Making & Patient Care – providing physicians with real-time insights that synthesize a patient’s full medical history to support personalized treatments
  • Clinical Research & Real-World Data – accelerating patient cohort identification for research studies and improving real-world evidence generation 

Chart review has been a longstanding challenge for most health systems, which can spend upwards of $6M per hospital on personnel costs for care quality data reporting.

  • At the same time, this data represents an invaluable resource for unifying care data with clinical and financial outcomes, enabling treatment decisions to be mapped to their real-world impact.

Flare Capital Partners’ investment memo for Layer laid out how the clinical registries that map chart information to direct outcomes have historically been hamstrung by their unstructured source data.

  • Abstracting this data into a usable format is a time-consuming manual process, and most technological fixes have usually only involved automating small parts of it.
  • By combining the reasoning ability of large language models with the cost-efficiency of small language models, Flare believes that Layer can capture a major slice of the multi-billion dollar care quality reporting market (plus another chunk of the life sciences sector’s growing appetite for real-world data).

The Takeaway

Surfacing insights from medical charts requires peeling back countless layers of structured and unstructured data, which makes it particularly well-suited for both AI solutions and ambitious startups like Layer that are bringing them to market.

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