Tag: ai

Tennr Raises $101M Series C for AI-Powered Referrals 

Posted on by Jason Barry

Tennr just raised $101M of Series C funding to have AI help solve one of healthcare’s most timeless challenges: fax machines. 

Tennr got its start in 2021 improving the patient intake and documentation review process, but has quickly expanded its capabilities to make sure that patients don’t get lost in a “black hole” during the referral process.

  • More than one-third of Americans receive a medical referral each year, half of which aren’t completed due to miscommunication, misdirected referrals, or missing information.
  • Tennr’s orchestration platform and proprietary language models automate these workflows to help providers convert more patients, cut denials, and deliver care without growing their teams.

The secret sauce is Tennr’s specialized language models (RaeLM), optimized to understand the nuanced data in medical determinations and evaluate it against strict payor criteria.

  • Tennr integrates with over 50 types of e-faxes, phone lines, emails, and portals to collect patient information, then leverages RaeLM to structure the data into usable information that can be shared with EHRs and pharmacy management systems.
  • The thesis was that if Tennr could read the documents and structure the data, it would be in a good spot to bolt-on more services – such as its eligibility benefits product, patient communication solution, and referral management suite. 

The fresh funds will fuel the launch of the Tennr Network, designed to equip referring providers, receiving providers, and patients with real-time visibility into the referral status.

  • Referring providers can see the status of every patient they’ve sent out, eliminating phone tag and guesswork.
  • Receiving providers can track the status of every referral, see which need more documentation, and identify which sources are driving the most conversions.
  • Patients can see when their referral was accepted, when it’s scheduled, and what to expect to pay – matching the “transparency we take for granted in food delivery or e-commerce.”

The Takeaway

Faxes are here to stay, and Tennr has $101M to make sure that they’re actually serving the practices using them. The plan isn’t to give healthcare a new AI tool, but to use AI to help the industry get more out of the tools it already has.

Nabla Series C Brings Agentic AI to the Heart of Healthcare

Posted on by Jason Barry

Healthcare’s “zero-screen future” is looking closer than ever after Nabla locked in $70M of Series C funding to put AI agents to work restoring the human connections at the heart of the industry.

Ambient AI is hot, but Agentic AI is hotter. The round follows a wave of adoption across U.S. health systems as Nabla sets its sights on growing beyond its roots in ambient documentation.

  • Nabla’s AI assistant is now used by 85k clinicians across 130+ healthcare orgs, ranging from FQHCs and rural hospitals to academic medical centers and national providers.
  • In the past six months, Nabla’s grown its Live ARR by over 5X (meaning its annual recurring revenue from clients that have already gone-live, not “contracted” revenue, and definitely not annual run rate).

The key to Nabla’s success has been its ability to deliver highly personalized AI experiences without compromising on reliability, and the fresh funds will help build out an Adaptive Agentic Platform that brings that same ethos to new use cases:

  • Proactive Coding Agent – a real-time coding assistant that flags billing issues and surfaces compliance nudges.
  • Context-Aware Agent – strengthens existing support for patient summaries and pre-charting with direct EHR commands and the ability to initiate orders.
  • Custom Care Setting Agent – adds new capabilities for nurses and inpatient teams to bring tailored support to frontline workers.

The Agent Era has arrived. The days when every ambient scribe demo got a standing ovation are long gone, and health systems are looking for AI that can automate increasingly complex workflows (or better yet, generate revenue that justifies its cost).

  • Nabla clearly got the memo, but so did competitors like Abridge, Ambience, and Suki – all of which are actively working to add coding and other new features to their platforms.
  • The race is on, and the entire healthcare industry – patients and providers alike – should be better off because of it.

The Takeaway

Nabla’s been making quick progress toward some ambitious goals, and agentic AI just raised the goalpost even higher. In the words of CEO Alex LeBrun, “Our mission: bring agentic AI to clinicians in a safe, compliant, and ethical framework. The journey?‍ 1% done.”

Ellipsis Health Closes Series A, Unveils Sage AI Care Manager

Posted on by Jason Barry

Care management gaps are becoming a pressing problem as the provider shortage deepens, and Ellipsis Health just raised $45M of Series A funding to show why empathy is the answer. 

Ellipsis put Sage front and center in the announcement, unveiling its emotionally intelligent AI care manager that expands staffing capacity through proactive patient engagement.

  • Sage’s Empathy Engine leverages Ellipsis’ patented vocal biomarker tech and training from millions of real clinical conversations to support the patients driving a majority of healthcare costs: those with complex physical, behavioral, and social needs.
  • Unlike traditional AI agents, Sage adjusts its tone and approach based on a patient’s emotional state, exactly how a human care manager would when conducting health risk assessments, post-discharge follow-ups, or care transitions.

The Series A investor roster suggests that Ellipsis has some strong tailwinds working for it.

  • The round was led by Salesforce (workflow integration through Salesforce Health Cloud), Khosla Ventures (tech validation from an AI fund and early investor in OpenAI), and CVS Health Ventures (plenty of Aetna members to reach meaningful scale).
  • Those investors will only help Ellipsis land and expand across more enterprises as it continues building evidence that Sage moves the needle with patient outcomes.

Competition is heating up. Companies like Hippocratic and Innovaccer have been rolling out fleets of AI agents that can handle routine tasks like appointment scheduling, but Ellipsis is setting out to prove that empathy is a difference maker in actual case management.

  • It’s the little things between visits that can decide whether a patient gets their hypertension under control, like walking them through lab results or making sure they take their medication.
  • It’s also the same little things that slip through the cracks when you have a staff shortage or burned out clinicians, and a little empathy could go a long way toward filling these gaps and driving the behavior change that leads to better outcomes.

The Takeaway

Ellipsis envisions a future for healthcare where AI can “extend human capabilities while preserving the empathy and clinical judgment that defines great care.” Sage is the tool that Ellipsis built to carve that future, and it now has $45M to put it in the hands of more providers.

OpenEvidence Partners With JAMA Ahead of Next Raise

Posted on by Jason Barry

“The fastest-growing platform for doctors in history” continues to step on the gas, and OpenEvidence is reportedly on the verge of notching a $3B valuation after inking a deal to bring JAMA Network journals to its AI medical search engine.

The multi-year content agreement will make full-text articles from the American Medical Association’s JAMA, JAMA Network Open, and 11 specialty journals available directly within the OpenEvidence platform.

  • OpenEvidence’s medical search engine helps clinicians make decisions at the point of care, turning natural language queries into structured answers with detailed citations.
  • The model was purpose-built for healthcare using training data from strategic partners like the New England Journal of Medicine, which joined the platform through a similar deal earlier this year.

The Disney+ content strategy has arrived in healthcare. OpenEvidence compares its approach to streaming services that drive subscriptions through exclusive movies.

  • If a physician wants information from top journals to support decision making, they’ll either have to get it straight from the source or use OpenEvidence, just like how anyone who wants to stream Moana needs to go to Disney+.
  • The kicker is that OpenEvidence is available at no cost to verified physicians, and advertising generates all of the revenue. 

The blueprint is working like a charm. OpenEvidence has over 350k doctors using its platform plus another 50k joining each month, and it’s apparently close to raising $100M at a $3B valuation just a few months after closing its $75M Series A.

  • It’s rare to find hockey stick growth in digital health, and OpenEvidence is a good reminder that many areas of healthcare change slowly… then all at once.
  • It also isn’t too surprising to hear that VC’s like Google Ventures and Kleiner Perkins are lining up to fund a company with a similar ad-supported business model to Doximity – one of the only successful healthcare IPOs since the start of the pandemic.

The Takeaway

Content is king, and OpenEvidence is locking in partnerships to make sure its platform is wearing the crown. The results have been speaking for themselves, but healthcare’s genAI streaming wars are just getting started.

More Reasoning, More Hallucinations for LLMs

Posted on by Jason Barry

Better reasoning apparently doesn’t prevent LLMs from spewing out false facts.  

Independent testing from AI firm Vectara showed that the latest advanced reasoning models from OpenAI and DeepSeek hallucinate even more than previous models.

  • OpenAI’s o3 reasoning model scored a 6.8% hallucination rate on Vectara’s test, which asks the AI to summarize various news articles.
  • DeepSeek’s R1 fared even worse with a 14.3% hallucination rate, an especially poor performance considering that its older non-reasoning DeepSeek-V2.5 model clocked in at 2.4%.
  • On OpenAI’s more difficult SimpleQA tests, o3 and o4-mini hallucinated between 51-79% of the time, versus just 37% for its GPT-4.5 non-reasoning model.

OpenAI positions o3 as its most powerful model because it’s a “reasoning” model that takes more time to “think” and work out its answers step-by-step.

  • This process produces better answers for many use cases, but these reasoning models can also hallucinate at each step of their “thinking,” giving them even more chances for incorrect responses.

The Takeaway

Even though the general purpose models studied weren’t fine-tuned for healthcare, the results raise concerns about their safety in clinical settings – especially given how many physicians report using them in day-to-day practice.

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AI Can Help Doctors Change Their Minds

Posted on by Jason Barry

A recent study out of Stanford explored whether doctors would revise their medical decisions in light of new AI-generated information, finding that docs are more than willing to change their minds despite being just as vulnerable to cognitive biases as the rest of us.

Here’s the set up published in Nature Communications Medicine: 

  • 50 physicians were randomized to watch a short video of either a white male or black female patient describing their chest pain with an identical script.
  • The physicians made triage, diagnosis, and treatment decisions using any non-AI resource.
  • The physicians were then given access to GPT-4 (which they were told was an AI system that had not yet been validated) and allowed to change their decisions.

The initial scores left plenty of room for improvement.

  • The docs achieved just 47% accuracy in the white male patient group.
  • The docs achieved a slightly better 63% accuracy in the black female patient group.

The physicians were surprisingly willing to change their minds based on the AI advice.

  • Accuracy scores with AI improved from 47% to 65% in the white male group.
  • Accuracy scores with AI improved from 63% to 80% in the black female group.

Not only were the physicians open to modifying their decisions with AI input, but doing so made them more accurate without introducing or exacerbating demographic biases.

  • Both groups showed nearly identical magnitudes of improvement (18%), suggesting that AI can augment physician decision-making while maintaining equitable care.
  • It’s worth noting that the docs used AI as more than a search engine, asking it to bring in new evidence, compare treatments, and even challenge their own beliefs [Table].

The Takeaway

Although having the doctors go first means that AI didn’t save them any time in this study – and actually increased time per patient – it showed that flipping the paradigm from “doctors checking AI’s work” to “AI helping doctors check their own work” has the potential to improve clinical decisions without amplifying biases.

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.

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.

PHTI Delivers Mixed Reviews on Ambient Scribes

Posted on by Jason Barry

The Peterson Health Technology Institute’s latest technology review is here, and it had a decidedly mixed report card for the ambient AI scribes sweeping across the industry. 

PHTI’s total count of ambient scribe vendors stands at over 60, but the bulk of its report focuses on the early experiences and lessons learned from the top 10 scribes across leading health systems.

According to PHTI’s conversations with health system execs, the primary driver of ambient scribe adoption has been addressing clinician burnout – and AI’s promise is clear on that front.

  • Mass General Brigham reported a 40% reduction in burnout during a six-week pilot.
  • MultiCare reported a 63% reduction in burnout and a 64% improvement in work-life balance.
  • Another study from the Permanente Medical Group found that 81% of patients felt their physician spent less time looking at their computer when using an ambient scribe.

Despite these drastic improvements, PHTI concludes that the financial returns and efficiency of ambient scribes remain unclear.

  • On one hand, enhanced documentation quality “could lead to higher reimbursements, potentially offsetting expenses.”
  • On the other hand, the cumulative costs “may be greater than any savings achieved through improved efficiency, reduced administrative burden, or reduced clinician attrition.”

It’s a bold conclusion considering the cost of losing a single provider, let alone the downstream effects of having a burned out workforce. 

PHTI’s advice to health systems? Define the outcomes you’re looking for and then measure ambient AI’s performance and financial impacts against those goals. Bit of a no-brainer, but sound advice nonetheless. 

The Takeaway

Ambient scribes are seeing the fastest adoption of any recent healthcare technology that wasn’t accompanied by a regulatory mandate, and that’s mostly because of magic that’s hard to capture in a spreadsheet. That said, health systems will eventually need to justify these solutions beyond their impact on the clinical experience, and PHTI’s report brings a solid framework and standardized methodologies for bridging that gap.

AI Misses the Mark on Detecting Critical Conditions

Posted on by Jason Barry

Most health systems have already begun turning to AI to predict if patient health conditions will deteriorate, but a new study in Nature Communications Medicine suggests that current models aren’t cut out for the task. 

Virginia Tech researchers looked at several popular machine learning models cited in medical literature for predicting patient deterioration, then fed them datasets about the health of patients in ICUs or with cancer.

  • They then created test cases for the models to predict potential health issues and risk scores in the event that patient metrics were changed from the initial dataset.

AI missed the mark. For in-hospital mortality prediction, the models tested using the synthesized cases failed to recognize a staggering 66% of relevant patient injuries.

  • In some instances, the models failed to generate adequate mortality risk scores for every single test case.
  • That’s clearly not great news, especially considering that algorithms that can’t recognize critical patient conditions obviously can’t alert doctors when urgent action is needed.

The study authors point out that it’s extremely important for technology being used in patient care decisions to incorporate medical knowledge, and that “purely data-driven training alone is not sufficient.”

  • Not only did the study unearth “alarming deficiencies” in models being used for in-hospital mortality predictions, but it also turned up similar concerns with models predicting the prognosis of breast and lung cancer over five-year periods.
  • The authors conclude that a significant gap exists between raw data and the complexities of medical reality, so models trained solely on patient data are “grossly insufficient and have many dangerous blind spots.”

The Takeaway

The promise of AI remains just as immense as ever, but studies like this provide constant reminders that we need a diligent approach to adoption – not just for the technology itself but for the lives of the patients it touches. Ensuring that medical knowledge gets incorporated into clinical AI models also seems like a theme that we’re about to start hearing more often.

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