Month: May 2026

Enhanced Goes Public With World Record Twist

Posted on by Jason Barry

The Enhanced Group just kept the digital health SPAC dream alive with its public market debut, but it took more than some juiced up athletes to justify its billion dollar valuation.

What if the Olympics allowed steroids? Next week’s Enhanced Games might be the answer.

Enhanced made a big splash when it first announced the events kicking off in Vegas this Sunday. It’s easy to see why:

  • Athletes – 50
  • Events – Swimming, Track, Weightlifting, Strongman
  • World Record Bounty – $25,000,000
  • Performance Enhancing Drugs – Go for it. There’s no testing.

That’ll definitely pull viewers. It’s also only a sliver of Enhanced’s actual gameplan.

  • Every event is open to the public, at no cost, and so are the media rights. The entire spectacle is designed to get social media clips and as many eyeballs as possible.
  • It might sound like Enhanced is building a new-gen marketing company, but it’s actually building a patient funnel that most telehealth companies could only dream of.

The real revenue comes after the records are broken. Earlier this month, Enhanced quietly added some sleek new buttons to its website. They all point to Live Enhanced.

  • Live Enhanced is a direct-to-consumer telehealth platform that offers the full catalogue of 2026 trending medications. Peptites, GLP-1s, TRT, you name it.
  • While other startups are busy peddling these with nothing but fake doctor Facebook accounts, Enhanced is doing it with genuine world records and famous athletes.

Telehealth makes more than TikTok. Enhanced has been putting the athletes that are participating in the games through a gauntlet of clinical studies using the drugs available on Live Enhanced. 

  • Every dose, every rep, and every rest is getting packaged into protocols for fans and patients alike. 
  • If an athlete breaks a record, they’ll probably have people lining up to try their protocol – even if Live Enhanced doesn’t mention a few extracurricular enhancements.

The Takeaway

Despite all the hype around the Games, Enhanced’s real product is the protocols – the rest is just lead gen. It’s the same blueprint as Bryan Johnson’s Blueprint, only on steroids;)

Ad-verse Effects in Consumer-Facing AI

Posted on by Jason Barry

As AI companies embed more ads in their user interfaces for clinicians and consumers, the BRIDGE GenAI Lab decided to take a look at whether these ads impact model performance.

Turns out, they do. BRIDGE ran four experiments across 12 leading LLMs from Anthropic, Google, and OpenAI. The models were far more recent than most studies we cover, an upside of not waiting around for peer-review before publishing a preprint.

  • Each experiment paired a clinical scenario with a system prompt containing a pharmaceutical advertisement, then asked the model for a treatment recommendation.

Ads definitely moved the needle. Across 74,880 calls and 13 scenarios, advertising shifted the model’s choice toward the advertised drug from a baseline of 34% to 48%. 

  • That’s a jump of +12.7 percentage points on average.

The LLMs had some nice range. Model bias varied widely by developer.

  • Google’s advertising DNA was on full display when Gemini led the pack with an average shift of +29.8 percentage points toward the advertised drug. 
  • Five models from OpenAI were swayed by an average of +10.9 pp.
  • Anthropic’s models were the most resilient at +2.0 pp, and the ever-skeptical Opus 4.6 actually steered away from the promoted drug by -3.8 pp.

Three experiments contrasted three different conditions. That let BRIDGE triangulate the bias across a trio of distinct categories.

  • Equipoise (+12.7 pp) – When two drugs were guideline-equivalent, the ad acted as a tiebreaker. The output was clinically correct, but biased.
  • Suboptimal Drug (+0.6 pp) – When the advertised drug was clinically inferior, models resisted. Only 4.4% of responses chose the suboptimal advertised option.
  • Wellness Supplements (-0.6 pp) – For supplements lacking evidence, endorsement decreased. Anthropic models actively pushed back at -2.4 pp.

The picture was consistent. Advertising didn’t override medical knowledge, but it did tip the scales when two or more options were medically defensible. 

  • Another important note: When models were asked to justify their choices, they almost never disclosed the ad. If they chose the advertised drug, the justification echoed the ad in 52.7% of cases.

The Takeaway

BRIDGE just showed why the real harm with AI advertising might not be patients receiving dangerous drugs. It could be that they receive clinically sound recommendations that were shaped by commercial interests – without them knowing it, and without a mechanism to flag it.

Mispricing the RCM Bundle

Posted on by Jason Barry

Recovering consultant Andrew Tsang is back with another top tier analysis exploring why healthcare’s revenue cycle management bundle is currently mispriced. 

Great bundles lead to great unbundling. The term “unbundling” was first coined in a 2010 Tumblr post that applied the concept to Craigslist, a patchwork homepage of loosely related categories waiting to be peeled off as specialized startups.

  • AirBnB eventually took housing, Indeed took jobs, and dating apps took personals. Investors were standing by with checkbooks in hand every time. 

RCM is healthcare’s Craigslist. It’s a $300B monster of about a dozen different steps that exist to process the disagreement when payors and providers can’t agree on what care is worth.

  • RCM is practically begging to be broken into its component parts (prior auth, clinical documentation, denials), but the same investors funding the unbundling thesis are also the ones writing huge checks to fuse the wedges back together.

That’s because Craigslist isn’t linked like RCM. You don’t need a new love interest to get a new couch, and you don’t need a new couch to get a new love interest. Although it couldn’t hurt.

  • With RCM, optimize coding and the patient’s bill goes up. Optimize collections and patients defer future care. Every optimization at one step ripples through the others.

Hospital execs know this. They’re not buying best-of-breed point solutions, they’re consolidating onto platforms that cover the full lifecycle, and vendors are behaving accordingly.

  • Tsang argues that RCM vendors are rational actors that are being pushed to acquire nearby wedges rather than build them, and you don’t have to look much further than Waystar or Smarter Technologies to find evidence to support that.
  • “The payor-provider fight is structurally dysfunctional, and that dysfunction rewards positioning over performance.”

RCM isn’t getting unbundled, it’s getting rolled up. When IT budgets get cut, CFOs pick the partner who covers enough of the arc to be worth keeping.

  • The worse the market gets, the more valuable broad coverage becomes, and the RCM platform moat continues compounding. That’s the state of the RCM market.

The Takeaway

Tsang makes a compelling case that the RCM vendors that survive the next decade won’t be the ones that reduce the claims disagreement. They’ll be the ones that own the channel for it.

Photon Raises $16M to Power Up Prescriptions

Posted on by Jason Barry

Electronic prescribing was designed to move prescriptions from point A to point B, not to give patients the information needed to pick those points on the spot. Photon just raised $16M in Series A funding to fill that gap.

Photon got its start in 2021 building prescription infrastructure for direct-to-consumer telehealth companies, powering some of the fastest-growing brands of the pandemic and GLP-1 era.

That experience provided a solid foundation. Photon built deep integrations across pharmacy networks, real-time formulary data, and a patient-first prescribing experience.

  • That foundation not only proved compelling to DTC startups, but also to health systems looking for better ways to engage patients at the point of care and drive visibility into their in-house pharmacies. 
  • Fast forward to the Series A, and Photon is leaning in on the enterprise market where it can make the biggest impact.

Photon isn’t a pricing widget. It’s an end-to-end platform that includes:

  • Modern prescribing and routing infrastructure
  • A network of pharmacy partners across retail and home delivery
  • A consumer-facing marketplace that surfaces real-time price and stock information
  • A full suite of capabilities including prior auths and clinical decision support 

AI underpins everything. Photon’s AI ingests fragmented data across pharmacy networks, benefit structures, and formularies, then translates it into info that patients can actually use.

  • That gives patients a sense of what’s convenient, what’s covered, what delivery options are available, and most importantly – price. 
  • Put it all together, and patients can make informed choices before prescriptions ever get sent.

Health systems get something equally valuable. The prescription becomes a patient engagement touchpoint instead of a handoff.

  • In-house pharmacy teams gain real-time visibility into fill activity, and having patients that are actually informed results in fewer abandoned scripts, less reroutes, and a meaningfully better experience for everyone involved.

The Takeaway

Right when healthcare services are getting disrupted by LLMs and agents, patients are hitting a boiling point with affordability and transparency. The pharmacy experience is a major intersection for both roads, and Photon just raised $16M to modernize all four corners.

Study Questions Value of Management Consultants in Healthcare

Posted on by Jason Barry

If hospitals spend billions of dollars on management consultants, they will at least get:

  • A) stronger finances
  • B) better quality of care
  • C) streamlined operations
  • D) none of the above

The correct answer: D as in Deloitte! You guessed it, at least according to a new study in JAMA.

  • Researchers analyzed 2,343 non-profit hospitals in the U.S. from 2009 to 2023, finding that they collectively spent over $7.8B on management consulting over that period.
  • More than 20% of the hospitals brought on consultants, and the hefty total in the previous bullet means they spent an average of $15.7M for their services.

Here’s what that got them. Researchers compared 306 hospitals that enlisted management consultants for the first time during the study period to 513 matched hospitals that toughed it out on their own.

  • Despite the substantial investment, the study found “little evidence of substantial, statistically significant, or systematic improvements” attributable to the consulting engagements.

Consultants couldn’t catch a break. The analysis showed that the hospitals that hired them saw no significant impact across any of the primary measures.

Not the financial measures. 

  • Net patient revenue was down 2.22% (P = .14).
  • Total margin was down 0.19 percentage points (P = .71).

Not the operational measures. 

  • Inpatient length of stay was up 1.71% (P = .10).
  • Total inpatient days were up 0.29% (P = .85).

Not the quality measures.

  • All insignificant, besides 30-day stroke readmissions: up 1.37 percentage points (P = .03).

Big results, with limitations. An accompanying editorial applauded the analysis, but pointed out that struggling hospitals are also more likely to seek outside help. Future research should investigate this selection bias and “the factors that predict a hospital’s decision to hire a consulting firm.”

The Takeaway

High P-values don’t mean that management consultants aren’t making an impact. They just mean that the most notable study to investigate the impact couldn’t find one…

OpenAI o1 Outperforms Physicians on Clinical Reasoning Tasks

Posted on by Jason Barry

A landmark study in Science found that OpenAI’s o1 series outperformed human physicians at multiple clinical reasoning tasks, but that doesn’t mean it’s time to hang up the scrubs just yet.

Researchers at Harvard and Beth Israel Deaconess Medical Center designed the study to evaluate whether LLMs are ready to do what physicians do on a daily basis: review messy patient charts and use that data to determine diagnosis and next steps.

  • They evaluated o1 on clinical cases ranging from patient vignettes to second opinions on 76 real-world ED assessments, which included all the noise and incomplete information that clinicians routinely encounter in the EHR.
  • The refreshingly well-designed study also incorporated a blinded evaluation with two attending physicians at BIDMC and GPT-4.

o1 came to play. On clinical vignettes evaluating management reasoning, o1-preview scored a median of 86%. Not too shabby.

  • It outperformed GPT-4, humans with GPT-4, and humans with conventional resources like UpToDate – all of which scored below 45%.

The ED cases were even more impressive. o1 offered second opinions about the diagnosis at three points along the patient’s ED journey:

  • At triage, o1 gave an exact or very close diagnosis in 67% of cases (when information in the record dump was most limited). The two physicians hit 55% and 50%. 
  • o1 still outperformed the physicians when given all the data collected by the end of the ED encounter.
  • It was only when the physicians were given the most information possible to inform their diagnosis – at the time the patient would have been admitted to the hospital – that the scores finally converged.

The cherry on top? Physician raters couldn’t tell whether the differentials came from o1 or a human. One rater couldn’t tell in 83.6% of cases, the other in 94.4%. 

  • The authors were quick to mention that these results don’t mean AI is ready to replace human physicians. They mean it’s time for rigorous research into how AI can augment care teams, serve as a second opinion, and become a safety layer for clinicians.

The Takeaway

o1 outperforming a couple internists at triage isn’t quite Deep Blue beating Gary Kasparov at chess, but it’s a step in that direction – especially considering OpenAI’s performance jump in just the last week (let alone since o1 launched in 2024).

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