Ian Dorward

A great thesis for folks investing or building in healthcare lies around the fact that smaller clinics can't justify the overhead of on-site specialized services due to low patient volumes. Building virtual platforms that serve these use cases is a proven playbook. In the US, digital pharmacies are partnering with smaller specialty clinics, and MSK solutions are crushing it in rural areas... there's still a huge amount of whitespace in Canada. Pediatrics, Sleep and Mental Health are all additional areas with potential, and there are even more to list. The most exciting part of these solutions actually goes beyond better access to care--they have the potential to make work much more fulfilling, rewarding, and flexible for independent practitioners. Lemoine Fitzgerald, The Prairie, 1929

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Telehealth founders: if you’re still in denial about CPOM, you’re not “moving fast.” You’re building on a fault line. I’ve met the whole spectrum: The ones who don’t know CPOM exists. The ones who know, but assume they’re “too small to matter.” The ones who know, but say, “Everyone else is doing it.” The ones who know, but treat compliance like a vibe instead of a structure. And here’s the hard truth: CPOM isn’t a suggestion. It’s a physics law for healthcare businesses. Ignore it long enough and you don’t just get a compliance problem—you get a business problem. Because CPOM doesn’t only show up when a regulator calls. It shows up when: your bank asks questions about money movement and who controls clinical decisions a partner wants diligence and your docs can’t answer “who owns what” cleanly a provider quits and takes patients because the professional entity and decision rights were never real you try to sell, and your “value” evaporates under scrutiny a single bad outcome turns into a discovery process you were never structurally prepared to survive What denial usually sounds like is fear dressed up as confidence. Fear of slowing down. Fear of cost. Fear of discovering you’ve already built it wrong. Fear of losing control. But let me tell you something from the clinician side and the operator side: The goal isn’t to be “compliant.” The goal is to build something that survives scrutiny and still feels like care. CPOM done right isn’t a cage. It’s a container. It clarifies: who holds clinical authority who holds operational authority how money flows how incentives stay clean how providers are protected how patients are served without the business getting tempted to cross the line If your business requires clinical shortcuts to scale, you don’t have a scale problem. You have a model problem. The founders who win long-term aren’t the loudest. They’re the ones who can look at their structure with clear eyes and say: “If someone audited this tomorrow, we’d still be proud.” That’s the Daylight Test. If you’re building in telehealth and CPOM feels “annoying”… good. That discomfort is your nervous system telling you there’s a decision you’ve been avoiding. Make it now—on purpose—before someone else makes it for you. If you’re a founder/operator and you’ve had to unwind CPOM mistakes the hard way, I’d genuinely love to hear what surprised you most.

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US Healthcare administrative spending is ~$1T annually, but the workflows that underpin industry remain outdated and heavily labor-intensive. Behind every patient interaction sits a dense layer of administrative work: intake, documentation validation, eligibility checks, payer communication, and follow-ups that determine whether care actually gets delivered and reimbursed. Despite decades of digitization efforts, much of this work remains manual, fragmented, and deeply inefficient. AI-native solutions like Synthpop - Healthcare AI are the only way to fulfill rising demand and increased patient expectations. The early value delivered to customers has been extremely compelling; Ansa Capital is thrilled to lead the Series A, join the Board and partner with Elad Ferber Jan Jannink and the Synthpop team. Read more about our investment in the comments. Allan Jean-Baptiste Josh Chin Arielle Warren Douglas Aaron, CPA Ashley Henry ilana stern Medha Agarwal Arun Penmetsa Mark Stephenson Scott Holzberg Hajdi Cenan

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Can AI Safely Own the Rx Refill? 💊 AI enabling Telemedicine, here we go. Utah recently (January) became the first state to allow autonomous AI to handle routine prescription refills. The NY based startup behind it, Doctronic, just announced a $40M Series B round. I say this all the time; the bigger use cases for AI are for the backend use cases. Places where the clinician or patient may not realize it's there. Prescription refills are sitting somewhere in the middle. It needs data from previous visits, medical records etc. but also requires a visit or some medical interaction. This is using AI for both which is why it's considered autonomous. How the "Refill Bot" works: 🔹 Safety: It only manages refills for stable patients already under a clinician's care. 🔹 Medication guardrails: It operates on a strict formulary of 190 routine medications (no controlled substances). 🔹 Data Integrity: The system verifies the state prescription database and patient records before authorizing, rather than "taking the patient's word." This is standard for scheduled medications; for other meds, this is a difference. 🔹 A "Guardian" Layer: A secondary AI monitors the primary chat in real-time to detect medical emergencies. That monitor would be useful in itself. 🔹 The Human Loop: Every interaction is reviewed by physicians to ensure the "contained box" of automation stays closed. Earlier in the year there had been a version that was 'tricked' into giving a tripled amount of Oxycontin doses but the company states that wasn't the one used for patient interactions (and they are strictly avoiding scheduled med refills). While there is the fact that this type of services isn't reimbursed yet, I have an example of how a better refill program can still save costs for a health system program. Will tell you all about it next week. My takeaway: Innovation in 2026 is about the infrastructure and oversight. The FDA is still looking into safety concerns despite the guardrails the company has ensured. Clinician and patient trust cannot be overstated - especially if we are using something truly autonomous. #digitalhealth #AI #medicine #telehealth

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2025: The State of AI in Healthcare — and Why We Built Cara Menlo Ventures’ new “State of AI in Healthcare” report couldn’t be more on point: AI adoption in health systems has exploded, but the biggest gains so far come from fixing operations, not replacing clinicians. That’s exactly why we built Cara: for every patient touchpoint and for clinical operations behind the scenes. Most “AI in healthcare” tools focus on one slice — intake, notes, triage, or follow-up. But the real problem isn’t the slice, it’s the fragmentation. Every handoff between systems or staff is friction, lost data, or missed opportunity to care. Cara connects those dots -> from patient discovery to scheduling, intake, labs, pharmacy, and follow-up, while automating the ops layer that burns out teams. My 2 cents as a technical cofounder: Healthcare doesn’t need more single point solutions. It needs workflow intelligence that’s safe, explainable, and embedded where care actually happens. That’s what we’re building: One connected AI layer that helps every patient, every time. #AI #HealthcareInnovation #DigitalHealth #HealthTech #GenerativeAI

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Healthcare AI adoption is no longer theoretical. It’s operational. This week, OpenEvidence (often described as “ChatGPT for clinicians”) announced a $250M Series D at a $12B valuation, doubling in just two months. But the valuation isn’t the story. Adoption is. Reported metrics: • Used by ~40% of U.S. physicians • 100M+ Americans treated by a clinician using it in 2025 • ~18M physician consultations per month, up from ~3M a year ago • ~$150M annualized revenue with 90%+ gross margins For lab operators, this matters more than it might appear at first glance. Physicians are rapidly normalizing AI as part of clinical decision-making workflows, not as an experiment, but as infrastructure. Once that mindset shifts, expectations shift everywhere downstream. What this signals for laboratories: • Clinicians will expect faster answers, fewer manual steps, and smarter automation from labs • Data latency, manual review, and fragmented systems will become less tolerable • “System of record” alone won’t be enough, systems of action will win • Labs that integrate AI into accessioning, QA, utilization, billing, and client services will compound advantages quickly Healthcare adoption has historically been slow. This is not slow. When 40% of doctors adopt a new workflow in under three years, it’s a clear signal that AI is crossing from early adoption into standard of care. For labs, the question isn’t if AI becomes table stakes. It’s how fast you operationalize it before your customers expect it by default. Curious how other lab leaders are thinking about AI readiness inside their operations.

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AI in Healthcare is being used for the most basic tasks - Documentation and transcription. Those expensive pilots intended to transform basic enterprise functions are failing to return investment because they do not solve human, organizational, and technical friction. Orgs need new governance, memory, and workflow design to deliver value on AI investments. Unsurprisingly, within this status quo, clinical models remain unchanged.

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