The IDAA Hub Podcast: AI in Finance & Healthcare

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Join IDAA Hub as we explore the cutting edge of AI adoption in finance and healthcare. Each week, we bring you conversations with innovators, founders, and industry leaders who are transforming these critical sectors with artificial intelligence. From startup success stories to enterprise implementation strategies, we decode the complexities of AI integration and showcase products making real-world impact. Whether you're a healthcare executive, fintech founder, or AI enthusiast, discover actionable insights on building, scaling, and deploying AI solutions that matter. Hosted by Deepti & Deepak this is your gateway to the future of intelligent healthcare and finance

  1. 2d ago

    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 playedWhy proprietary data is the moat, whether you generate it or unlock itWhy speed of iteration is the quiet engine behind both companiesHow a seven-person team out-ships rivals many times its sizeSources & further reading: Valar Labs — "5 Years, 7 Engineers, No Cloud": https://www.valarlabs.com/engineering/7-engineersValar Labs — $22M Series A: https://www.valarlabs.com/news/series-aTempus AI: https://www.tempus.comIf 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

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

  2. Jul 16

    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

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

  3. Jun 25

    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/ 🌐 Visit: 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 storyWhy Abridge grew faster than NuanceAbridge's Nvidia + Eli Lilly partnership news3 startup lessons every founder should know📧 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

    Abridge — From AI Scribe to the Operating System for Medicine

  4. Jun 2

    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]

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

  5. May 26

    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

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

  6. May 12

    Hospital Revenue Loss, Cerner Blind Spots & How AI Innovation Can Fix These Issues

    Hospitals lost $48 billion in revenue in 2025 from claim denials alone — a 25% increase year over year. In this episode we break down why Cerner EMR hospitals are structurally exposed to this problem, and how AI innovation is being built to fix it. On April 22, 2026, Community Health Systems reported a $58 million net loss for Q1 — on nearly $3 billion in revenue. Leadership pointed to two compounding pressures: a challenging payer mix with fewer commercial patients, and a slow, steady ramp-up in claim denials that nobody's system was surfacing until it was too late. Three weeks earlier, Kodiak Solutions published the most comprehensive analysis of hospital revenue cycle performance ever conducted — analyzing 2,300 hospitals. The number they found: hospitals lost more than $48 billion in revenue in 2025 from claim denials and uncollected bills. A 25% increase from the prior year. And the increases were specifically for lack of prior authorization and for medical necessity. In this episode we break down exactly why hospitals running Cerner EMR are structurally exposed to these gaps — from the split between clinical and revenue cycle data models, to the inability to track true denial and appeal overturn rates, to the lack of machine learning needed to surface payer-specific denial patterns before they become systemic revenue leaks. We then explain Bloom Value's patented enterprise visibility system — US Patent 20230260638 — and what cooperative machine learning engines, simultaneous real-time and historical data processing, and role-specific financial dashboards actually mean for a CFO trying to see where the money is going.

    Hospital Revenue Loss, Cerner Blind Spots & How AI Innovation Can Fix These Issues

  7. Mar 10

    Credit Unions vs Banks: How Big is the AI Adoption Gap?, Part-1 with Nupur Daruka

    Banks are years ahead of credit unions in AI adoption — but why? And what does it actually take for a credit union to catch up? In this episode of the IDAA Hub Podcast, host Deepti sits down with Nupur Daruka — a credit union technology executive with 20+ years spanning fintech, e-commerce, and banking — to answer exactly that.  From a crisis-driven legacy code migration to building real-time fraud detection powered by AI, this is the ground-level story financial services leaders need to hear. 📌 WHAT YOU'LL LEARN: Why credit unions are structurally slower than banks at adopting AI How a major M&A event forced an urgent legacy-to-modern code migration using Claude and GitHub Copilot The PII guardrails that must be in place before ANY AI scales in finance The tiered risk framework: when AI can decide alone vs. when humans must stay in the loop How fraud detection became the gateway AI use case for credit unions Why rule-based fraud systems can't keep up — and how AI behavioral analysis fills the gap ⏱ CHAPTERS: 00:00 — Welcome & Introduction 00:25 — Meet Nupur: 20+ Years in Fintech & Credit Unions 01:15 — Where AI Adoption Really Stands Today 02:06 — The Legacy Code Crisis That Forced AI Adoption 03:59 — Regulatory & Capital Barriers at Credit Unions 04:52 — Templates, Guardrails & Starting Small 06:26 — Tools: Claude + GitHub Copilot in Action 08:06 — Vendor-Driven Architecture vs. Banks' Proprietary Systems 09:23 — Batch vs. Real-Time: The Core Divide 12:32 — Trust, Transparency & Auditable AI Decisions 14:00 — The Tiered Risk Approach Explained 17:36 — Fraud Detection: The #1 AI Win for Credit Unions 20:22 — Real-Time Fraud Response: Speed Is Everything 🔔 Subscribe for weekly conversations on AI in Finance & Healthcare. #AIinFintech #CreditUnion #FintechAI #LegacyCode #FraudDetection #AIAdoption #FiservDNA #IDAHubPodcast #GitHubCopilot #ClaudeAI About the Guest Nupur Daruka is a senior technology executive with a proven track record leading engineering organizations, platform modernization, and data-driven transformation across fintech, financial services, and credit unions. Known for aligning technology strategy with business outcomes to deliver secure, scalable platforms in highly regulated environments.A trusted partner to executive leadership https://www.linkedin.com/in/nupurdaruka/ 📧 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 📧 Connect with Host  Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/ 🌐 Visit: https://idaahub.com

    Credit Unions vs Banks: How Big is the AI Adoption Gap?, Part-1 with Nupur Daruka

  8. Mar 10

    Why Credit Unions Choose a Startup Over a Big Vendor? - Part 2 with Nupur

    Big vendors have the brand name. The sales team. The client list. So why credit unions walk away from all of that — and bet on a startup instead? Because brand names don't customize for you. Startups do. In Part 2 of this IDAA Hub Podcast episode, host Deepti and Nupur Daruka — credit union tech executive with 20+ years in fintech — pull back the curtain on the vendor decisions, ROI frameworks and future predictions that every financial services leader needs to hear but rarely does. 📌 WHAT YOU'LL LEARN: Why credit unions are increasingly turning to startups over big vendors for AI The real reason most AI pilots never reach production Why measuring AI ROI in dollars alone is the wrong framework entirely Trust + adoption + compliance — the metrics that actually matter A 3–5 year prediction: AI as decision support, not decision maker The biggest misconception keeping credit unions stuck in pilot mode Why AI creates new work rather than eliminating jobs ⏱ CHAPTERS: 20:54 — AI vs. AI: The Fraud Arms Race 21:35 — Why Startups Win: Flexibility Over Scale 22:03 — The Problem with Big Vendors: They Won't Customize for You 22:44 — Customization Was Key — Startups Said Yes, Big Vendors Said No 23:31 — The Real ROI of AI in Finance 24:50 — Trust + Adoption = The True Success Metric 25:15 — Why Most AI Pilots Die Before Reaching Production 25:56 — Compliance as ROI: The Metric Nobody Talks About 26:32 — How Personal AI Use Builds Institutional Trust 27:48 — Models Get Better As More People Use Them 28:12 — Chatbots: Now Standard at Every Financial Institution 29:02 — Where Will AI Be in 3–5 Years for Credit Unions? 29:28 — AI as Decision Support — Not Decision Maker 30:53 — Medium Risk Automated; High Risk Still Needs Humans 31:37 — Future: Integrated, Real-Time, Explainable, Predictive AI 32:54 — Biggest Misconception About AI in Finance 33:51 — Take Calculated Risks in a Controlled Way 35:16 — AI Creates New Work, Not Just Job Losses 36:08 — 2026: The Year of AI Transformation 📧 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 📧 Connect with Host  Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/ 🌐 Visit: https://idaahub.com 🔔 Subscribe for bi-weekly conversations on AI in Finance & Healthcare. #AIinFinance #CreditUnion #StartupVsEnterprise #AIAdoption #FintechROI #FraudDetection #IDAHubPodcast #FinancialServices #futureofwork

    Why Credit Unions Choose a Startup Over a Big Vendor? - Part 2 with Nupur
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Join IDAA Hub as we explore the cutting edge of AI adoption in finance and healthcare. Each week, we bring you conversations with innovators, founders, and industry leaders who are transforming these critical sectors with artificial intelligence. From startup success stories to enterprise implementation strategies, we decode the complexities of AI integration and showcase products making real-world impact. Whether you're a healthcare executive, fintech founder, or AI enthusiast, discover actionable insights on building, scaling, and deploying AI solutions that matter. Hosted by Deepti & Deepak this is your gateway to the future of intelligent healthcare and finance

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