Month: October 2025

Bain & Company: Top Healthcare IT Priorities

Posted on by Jason Barry

Payors and providers are fighting different operational battles, but they’re using the same two-letter weapon to come out on top: AI, you guessed it. 

A joint report from Bain & Company and KLAS found that 80% of payors and 70% of providers now have an AI strategy in place, up from just 60% last year.

  • Providers are up against structural workforce shortages and rising patient volumes, while payors are contending with higher medical loss ratios and more regulatory scrutiny.
  • Bain and KLAS’ survey of 228 U.S. healthcare execs suggests that all signs point to one solution, and that’s deploying tech to improve margins.

Where are payors investing? Care coordination (57%) and utilization management (55%) were the top IT investment priorities for the second straight year.

  • Payors place total cost of ownership, functionality, and scalability ahead of suite convenience, so best‑of‑breed is still the default buying motion.
  • Plans are leveraging AI for everything from member engagement (35%) and enrollment (26%) to risk adjustment (26%) and prior auth automation (20%).

Where are providers investing? Revenue. Cycle. Management.

  • Half of providers ranked RCM among their top IT priorities, placing it above clinical workflows (34%) and EHRs (32%).
  • RCM = ROI. Accurate documentation and coding results in cleaner claims and fewer denials, which directly translates to higher revenue and lower expenses.
  • It’s also a match made in heaven for AI automation, and RCM currently represents the four most common AI use cases: ambient documentation (62%), clinical documentation improvement (43%), coding (30%), and prior authorization (27%).

Here’s the kicker. Providers cite EHR integration and interoperability as their biggest pain points, so most of them prioritize their EHR vendors for new solutions.

  • Only 20% of providers are primarily best-of-breed buyers, and two-thirds of Epic customers would choose an Epic option that’s “good enough” over a better competing product.

The Takeaway

It’s getting pretty hard to not be bullish on AI. There’s still plenty of uncertainty, but both payors and providers now seem to agree that inaction is the riskiest action.

Rock Health Q3 Overview: Signals Out of Sync

Posted on by Jason Barry

Rock Health’s always-excellent digital health market overview painted an interesting picture for Q3, with venture funding continuing to climb despite several “signals out of sync.”

We’re steady on the surface. Digital health startups raised $3.5B across 107 deals in the third quarter, outpacing last year by a decent margin and bringing the year-to-date total to $9.9B across 351 rounds [Chart: Q3 Funding].

  • Deal volume continued to slow, but fewer raises yielded larger checks. Q3 saw 107 funding rounds, down from 120 in Q2 and 124 in Q1.
  • The average raise in 2025 now stands at $28.1M (up from $20.4M in 2024), and we’ve already seen 19 mega-rounds above $100M – surpassing last year’s total with a quarter left to go.

The middle is missing. Rock Health rolled up its sleeves and calculated some great funding velocity numbers. Of companies that raised their Series B in 2025, the median time since their Series A was 27 months, up from 17 months in the 2023 cohort.

  • Series B deal flow has also thinned, with just 30 raises through Q3, compared to more than 60 annually over the past four years.
  • Pair that with the persistent prevalence of unlabeled raises, and the thinning Series B pipeline suggests that startups are traveling increasingly winding roads to reach scale.

Activity is concentrating around workflows. The biggest theme of the Q3 report was that Clinical Workflow and Non-Clinical Workflow are now 2025’s two most-funded value propositions, capturing a combined 42% of the total funding [Chart: Value Propositions].

  • A $1.3B lead separates these value propositions from the rest of the pack, and workflow tools now appear to be in a league of their own.

Startups are heading horizontal. The report also highlighted a growing group of startups pushing into adjacent workflows, such as Abridge’s partnership with Highmark Health (expanding into prior auths) and Judi Health acquiring Amino (moving into patient navigation).

  • M&A volume is up 37% from last year, with 166 acquisitions through Q3 (already topping 2024’s 121 total), in large part due to these horizontal moves. 

The Takeaway

The numbers look steady, but the market is also steadily splitting in half. That means that the real story going forward won’t be whether digital health startups can attract investors (they can), but whether companies can demonstrate the impact needed to land on the right side of the divide. 

Assort Health Raises $76M for Experience Agents

Posted on by Jason Barry

A good patient experience starts with a good first impression, and Assort Health just closed $76M of Series B funding to prove its AI agents can deliver exactly that.

Accessing care is a painful process. The Assort OS platform delivers AI agents that can help manage patients’ needs, give staff more bandwidth for higher-level tasks, and reduce friction (AKA frustration) for everyone involved.

  • The agents go beyond scheduling, handling everything from care navigation and prescription renewals to physician referrals and lab tests. 

Goodbye hold music, hello agents. Providers have less staff to manage more patients, so they’re desperately looking for ways to offload the burden on the front office. Voice agents have become the go-to solution, and it’s tough to stand out.

Assort lists several core features that separate it from the pack:

  • Specialty-specific agents – PCPs and oncologists aren’t asking patients the same questions, and Assort adapts to the unique needs of each specialty.  
  • Seamless integration – Assort’s agents integrate directly into the EHR and practice management systems, allowing them to work within the unique clinical rules and workflows of each provider.
  • 90% resolution rate – The specialty-specific tuning allows Assort’s agents to maintain a resolution rate above 90%, while limiting errors like misdirected referrals.

Momentum builds momentum. There might be a ton of startups jumping into the voice AI arena, but not many are closing a Series B less than four months after their Series A. 

  • Assort’s investors will tell you that it’s “leading the re-platforming of patient engagement into the AI-native era.” At least they’re putting their money where their mouth is.
  • Assort now plans to use the $102M it’s raised over the last four months to get its agents into as many practices as possible before the competition gets their first.

The Takeaway

The race is on to transform the patient experience with the magic of AI, and Assort might just have enough agents (and VC dollars) to pull a rabbit out of its hat.

AI Learns the Natural History of Human Disease

Posted on by Jason Barry

Clinical decision-making relies on understanding patients’ past health to improve their future health, an impossible task without first understanding how diseases progress over time.

That’s where a new study in Nature suggests AI is ready to help.

It starts with generative pretrained transformers. Researchers built a GPT, dubbed Delphi-2M, to predict the “progression and competing nature of human diseases.” 

  • Delphi-2M was trained on 400k UK Biobank participants (which lean healthier than the average person), and then externally validated on 1.9M Danish patients.
  • The training was designed to predict a patient’s next diagnosis and the time to it, using only data readily available within the EHR: past medical history, age, sex, BMI, and alcohol/smoking status.

How’d it do? The results speak for themselves:

  • Delphi-2M was able to forecast the incidence of over 1,000 diseases with comparable accuracy to existing models that are fine-tuned to predict single diseases.
  • Death could be predicted with eerily impressive accuracy (AUC: 0.97), and the survival curves that it simulated lined up almost perfectly with national mortality statistics.
  • Comorbidities emerged naturally from the training, and Delphi-2M was able to understand the progression from type 2 diabetes to eye disease to nerve damage.
  • Delphi-2M’s ability to predict heart attack and stroke matched established scores like QRisk, and it even outperformed leading biomarker-based AI models.

Better forecasts inform better policies. If policymakers can consult the Oracle of Delphi to see how many people will develop a disease over the next decade, the authors conclude that they’ll also be able to implement better regulations to prepare. 

  • Not a bad theory, assuming models trained on historical data can make forecasts that hold up to evolving treatments and populations (and that politicians act in the best interest of the people:).

The Takeaway

AI is reaching the point where it can predict thousands of diseases as well as the best narrowly focused models, and that could have big implications for everything from early screening to policymaking.

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