A journalist investigates OpenEvidence's $15B valuation after the company signaled openness to acquisition, arguing the "ChatGPT for doctors" startup is worth roughly one-third that amount based on comparable healthtech deals and its claimed $300M ARR.

• OpenEvidence claims $300M in annual recurring revenue but comparable healthtech acquisitions suggest a $500M valuation maximum; founder Daniel Nadler is seeking 30x that amount, relying on his Goldman Sachs network and investor clout to pull it off.
• Livongo's acquisition by Teladoc for $18.5B followed by a $340M writedown serves as a cautionary tale for OpenEvidence's valuation, showing how healthtech M&A can collapse post-acquisition when growth claims don't materialize.
• The author's Part 2 investigation will examine OpenEvidence's actual technology, its lack of proprietary content (publishers can pull it anytime), and accuracy issues ranging from 100% on standardized tests to 27% on real-world medical cases.

A study of 2,000 specialty medical practices found that AI can generate roughly $125,000 per physician annually by fixing four specific operational problems: unanswered patient calls, lost follow-up appointments, insurance claim denials, and unpaid patient bills. The research comes as specialty practices face mounting financial pressure from rising costs and stagnant reimbursements.

• Medical practices miss 1 in 6 calls during business hours and 81 percent after hours; AI-enabled answering can convert these missed calls into $44,800 per physician in annual revenue, as shown by a dermatology practice that booked 3,315 additional appointments in one month using the system.
• Only 54 percent of patients return for scheduled follow-ups on time; AI-driven patient outreach can recapture $37,400 per physician annually by re-engaging patients who fall out of the care cycle.
• Front-end administrative errors (wrong insurance, missing approvals, data entry mistakes) cause nearly half of claim denials; AI-powered pre-visit benefits checks can prevent these and save practices $28,500 per physician yearly.
• Patients now cover 36 percent of care costs but practices collect only 39 percent of what patients owe at time of service; AI outbound calls can recover an additional $14,100 per physician annually.

A journalist interviews K Health's CEO about why the company quietly shut down its consumer symptom checker (which ranked last in independent testing) and what PatientGPT actually is. The CEO dodges the core question while defending K Health's move toward health system partnerships.

• K Health's consumer symptom checker scored 21.5% accuracy in a 2025 systematic review of 40 symptom checkers—dead last. GPT-4 scored 71.3%, and laypeople with no app scored 47-62%.
• K Health discontinued its consumer platform December 31, 2025 with no announcement. When asked if poor performance drove the shutdown, the CEO gave a 6-minute answer that didn't address the question.
• PatientGPT's architecture remains vague: the CEO describes "about a dozen agents" running on a mix of open-source and unnamed "other models," with costs determining which models get swapped in. Three different descriptions exist across seven months.
• K Health's own studies (where K Health funded or co-authored) show 77-85% accuracy, while independent research shows 21.5%—a pattern the journalist notes repeats across the AI health industry.

Hippocratic AI has terminated all its international contracts and fired its overseas sales team as revenue falls short of its $3.5 billion valuation and cash burn accelerates. The company’s heavy reliance on a handful of investor-customers has backfired, prompting a last-ditch pivot to analytics while core clients pull back.

• Hippocratic AI fired its entire international sales team and canceled contracts in Europe, the Middle East, and Asia-Pacific after failing to gain traction outside the U.S.
• The company raised a $3.5B Series C in November 2025 despite only $17-20M in annual recurring revenue—a 200x multiple exceeding OpenAI or Nvidia—while burning over $30M a month.
• Its investors (WellSpan, Cincinnati Children's, Universal Health Services) double as its main customers, with 80% of calls coming from a single client, and smaller clients are now leaving or not renewing.
• Insiders see only three paths forward: a dilutive rescue investment, a fire sale of parts of the business, or collapse.

Shiv Rao, cardiologist-turned-CEO of Abridge, explains why the company focuses on doctor-patient conversations rather than just notes, how it builds 70–80% of its own AI models, and why it retains full control despite raising $800 million. He also outlines Abridge’s edge over dozens of rival scribes, its new real-time prior-auth tool, and how it navigates liability and regulation.

• Abridge builds 70-80% of its own AI models in-house rather than relying on public/frontier models, reserving those for non-critical tasks
• Despite raising $800 million, Shiv Rao and co-founders retain full control and avoid strategic investors with board seats to prevent conflicts of interest
• The company differentiates itself from ~126 competing AI scribes by focusing on the full clinical conversation (not just notes) as the foundation for downstream workflows like billing and orders
• Abridge operates in 250+ health systems at a $5.3 billion valuation, targeting over a million recorded patient encounters per day this year