Abridge — From AI Scribe to the Operating System for Medicine

Two Playbooks: Go Broad or Go Niche — Who Wins? | Tempus AI vs. Valar Labs

Two companies chasing the same mission — getting the right cancer treatment to the right patient — with two completely opposite playbooks. One raised $1.3 billion to build an empire. The other ships FDA-recognized diagnostics with a seven-person engineering team. So which way should a startup go: broad or niche? And who actually wins? 📧 Connect with Host  Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/ 🌐 Visit: https://idaahub.com In this episode of the Innovation & Startup Series, we unpack Tempus AI and Valar Labs — how each was built, how each grew, and three lessons any founder can borrow. Tempus, founded by Groupon co-founder Eric Lefkofsky after his wife's cancer diagnosis, went all-in on owning the whole stack — genomics, clinical data, labs, and AI — and now does over $1.27B in annual revenue. Valar Labs went the opposite way: one sharply focused question — will this treatment work for this patient? — answered by running AI over routine pathology slides that already exist, built by seven engineers who optimized everything for one thing: speed of iteration. We dig into why capital intensity drives the broad-vs-niche decision, why data is the real moat (and the two ways to win it), and why fast iteration toward validated evidence beats a perfect initial design. What you'll learn: * How Tempus and Valar chose broad vs. niche — and the role capital played * Why proprietary data is the moat, whether you generate it or unlock it * Why speed of iteration is the quiet engine behind both companies * How a seven-person team out-ships rivals many times its size Sources & further reading: * Valar Labs — "5 Years, 7 Engineers, No Cloud": https://www.valarlabs.com/engineering/7-engineers [https://www.valarlabs.com/engineering/7-engineers] * Valar Labs — $22M Series A: https://www.valarlabs.com/news/series-a [https://www.valarlabs.com/news/series-a] * Tempus AI: https://www.tempus.com [https://www.tempus.com] If you enjoyed this, follow the show and share it with someone building in healthcare or AI. 📧 Connect with IDAAHub  Follow us on: LinkedIn: https://www.linkedin.com/company/idaahub/ https://www.youtube.com/@IDAAHUB https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327 [https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327]

What Cost Would You Put on the Care That Saved Your Son?

A team of doctors saved Atul Gawande's infant son. In that moment, he says, every one of them deserved a million dollars. The actual bill was $250,000. He paid $5. 📧 Connect with Host Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/ 🌐 Visit: https://idaahub.com That single moment opens up one of healthcare's biggest unanswered questions: how do we actually decide what medical care is worth? In this episode, I trace how physician pricing evolved — from ancient piecework fee schedules, to the 1980s Harvard formula that tried to put a number on "how much work" a surgery takes, to the 1929 Dallas hospital plan that quietly became the blueprint for American health insurance. I also pull in ideas from Atul Gawande's writing on performance and diligence in medicine, and close with a few open questions I'm still sitting with — including whether AI can actually be the innovation that brings costs down. In this episode: 00:00 – Why we're asking this question 00:45 – The piecework problem 02:30 – Trying to make pricing "rational" 04:15 – Where insurance came from 05:45 – It's not just pricing — it's performance 07:30 – The war nobody wins 08:15 – Closing thoughts and open questions If you work in healthcare, health tech, or health finance, I'd love to hear your take on the questions raised in the episode — drop a comment. 🎙️ Part of the IDAAHub Podcast — Innovation & Startups series #Healthcare #HealthTech #MedicalCosts #HealthInsurance #AtulGawande #HealthcareInnovation #AIinHealthcare #IDAAHub

Abridge — From AI Scribe to the Operating System for Medicine

In this new Innovation & Startups series, we break down Abridge — one of the fastest-growing AI startups in healthcare today. 📧 Connect with Host  Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/ [https://www.linkedin.com/in/deepti-kalghatgi/] 🌐 Visit: https://idaahub.com [https://idaahub.com/] We cover the real founding story (it started with a cardiologist, a patient, and one sentence about dignity), how Abridge grew faster than long-standing incumbents like Nuance, what just happened with Abridge's Nvidia and Eli Lilly partnerships, and why this isn't really a scribe company anymore — it's trying to become the operating system for how care gets delivered and paid for. We close with three takeaways every startup founder can learn from Abridge's playbook: why being first to market matters less than being first to solve the real problem, why credible partners can matter more than features, and why trust has to come before product in healthcare AI. 🎙️ Topics covered: * The real Abridge origin story * Why Abridge grew faster than Nuance * Abridge's Nvidia + Eli Lilly partnership news * 3 startup lessons every founder should know 📧 Connect with IDAAHub  Follow us on:  LinkedIn: https://www.linkedin.com/company/idaahub/ [https://www.linkedin.com/company/idaahub/] https://www.youtube.com/@IDAAHUB [https://www.youtube.com/@IDAAHUB] https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx [https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx] https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327 [https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327]

Who Owns Your Health Data? LLMs, Data Access & Venture Capital with Dr. Timothy Martens (Part 2)

15 years ago, getting your medical records meant visiting the hospital, signing forms, and walking out with a photocopied stack of 5,000 pages. Today you can import the same data into an LLM and ask it what's wrong with you. That shift — and everything in between — is what Part 2 of this conversation is about. In this episode, IDAAHub Podcast host Deepti continues her conversation with Dr. Timothy Martens — congenital heart surgeon at Northwell Health, Director of Data Strategy & Innovation at Cohen Children's Medical Center, PhD in Biomedical Engineering, and General Partner at Picap Fund — picking up at the question of patient data ownership and utility. Dr. Martens explains what patients can realistically do with their health data today: how LLMs are replacing the photocopied records stack with an instant, queryable medical history, why EMRs are caught between federal mandates to open their data and the disruption that follows when they do, and what it means to build a true "living health record" that updates dynamically alongside a patient's care journey. The second half of the episode traces a 25-year arc of healthcare data evolution that Dr. Martens experienced directly — from manually copying blood pressure readings row-by-row into Excel spreadsheets, to building relational databases and using HL7 for data transfer, to the emergence of data lakes and Tableau dashboards inside systems like Epic, and finally to today's AI-native stack. His core observation: the tools have changed dramatically, but the fundamental challenges of normalization, deduplication, and fuzzy matching across siloed systems remain structurally the same. 🧠 What you'll learn in Part 2: → What patients can realistically do with their health data right now — and where it still falls short → How LLMs are turning a 5,000-page records request into an instant, queryable health profile → Why EMRs are now in a difficult spot: penalized if they don't open up data, disrupted when they do → The 25-year arc of healthcare data: from copying blood pressures row-by-row into Excel, to HL7, to data lakes, Tableau, and AI — and why the core problems of normalization and deduplication never actually went away → How Dr. Martens built Mark Ventures as a venture studio, evolved it into an investing syndicate, and joined Picap Fund as General Partner → Why having a clinician on a VC investment committee changes which bets get made — and which ones don't The episode closes with Dr. Martens' transition from clinician to investor: founding Mark Ventures as a venture studio to build and advise healthcare technology companies, evolving it into an investing syndicate as founder demand for capital outpaced demand for advice, and ultimately joining Phycap Fund as General Partner — where his clinical domain expertise directly shapes the fund's investment decisions. Guest: Dr. Timothy Martens — Congenital Heart Surgeon, Northwell Health | Director of Data Strategy & Innovation, Cohen Children's Medical Center | General Partner, Phycap Fund Host: Deepti — Founder, IDAAHub | IDAAHub Podcast: AI in Finance & Healthcare [Part 1 available in the previous episode]

Healthcare Is NOT About Care - Part 1 with Dr Timothy Martens

What if the healthcare system was never built to make you healthier? In Part 1 of our conversation with Dr. Timothy Martens — congenital heart surgeon at Northwell Health, Director of Data Strategy & Innovation at Cohen Children's Medical Center, PhD in Biomedical Engineering, and General Partner at Picap Fund — we get into the structural truth most clinicians won't say out loud: the current system is optimized for billing, not care. Dr. Martens breaks down why episodic 15-minute visits can't move the outcome needle, how wearable technology and direct-to-consumer biomarker testing are converging to create a parallel care layer outside the EMR, and why AI-powered startups are outpacing hospitals and universities at actually solving healthcare's hardest problems. 🧠 What you'll learn in Part 1: → Why Dr. Martens calls himself a "Technology Fedaykin" — and what the Dune reference reveals about his mission → The real reason healthcare workflows are structured around CPT billing codes, not patient outcomes → Why pediatric congenital cardiac surgery attracted a data-obsessed biomedical engineer → The "great convergence" of EMR, wearable, lab, and genetic data — and which companies are winning it → Why predicting heart attacks and Alzheimer's risk is already possible — and what's still missing → How the same dopamine algorithms destroying kids on TikTok could be flipped to drive healthy behavior ⏱️ Timestamps: 00:00 — Intro & Dr. Martens' background 04:30 — Technology Fedaykin: the Dune philosophy behind healthcare innovation 06:00 — Healthcare is designed to maximize billing, not outcomes 07:00 — Why pediatric cardiac surgery attracted a data engineer 11:00 — EMRs are built for billing codes, not continuous care 16:00 — Wearables, biomarkers & the great data convergence 22:00 — Can AI actually predict a heart attack? What's still missing 🎙️ IDAAHub Podcast: AI in Finance & Healthcare Part 2 drops next — subscribe so you don't miss it. #HealthcareAI #AIinHealthcare #DigitalHealth #HealthTech #MedTech #IDAAHub #PodcastEpisode #HealthcareReform #WearableTech #PredictiveMedicine