Faces of Digital Health

Hosted by Tjasa Zajc

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Aug 2026

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Faces of Digital Health is a healthcare podcast about digital health technology, solutions, and innovations in practice, presented through real healthcare systems and the people behind them. The show looks into how different countries adopt digital health, what barriers they face, and why similar approaches succeed in some places but not others.Episodes feature clinicians, patients, entrepreneurs, and health system leaders sharing their practical experience. The focus is on digital health trends, practical digital health, and actionable insights for anyone curious about how digital health works in practice.

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October 1, 202643 min

€200m and 20 years: What EU health data projects built, and what EHDS needs next (Henrique Martins)

Nearly €200m, twenty years of EU health data projects. Henrique Martins on what they built and what EHDS needs next. Before the European Health Data Space became law, the EU had already funded two decades of cross-border interoperability work: epSOS, EXPAND, Trillium Bridge, JAseHN, eHAction and more. Henrique Martins has worked on most of them. In this conversation he explains how those projects became MyHealth@EU, why the European EHR exchange format matters for every hospital and IT vendor, what digital health authorities and health data access bodies will do from 2027, and how far patient control of health data can realistically go. He also argues that Europe is not behind on cross-border exchange, that slow national progress is ultimately a political choice made by voters, and that 2027 will show whether the EHDS implementing acts can actually be applied. GUEST Henrique Martins — Associate Professor, ISCTE – University Institute of Lisbon; coordinator, i2X (Intelligent Implementations of the European EHR Exchange Format); former President of SPMS, Portugal's national digital health agency; former Member States co-chair, EU eHealth Network Host: Tjaša Zajc WHAT THE CONVERSATION COVERS What nearly €200m in EU interoperability funding bought over twenty years How epSOS, EXPAND and OpenNCP became MyHealth@EU Cross-border ePrescription and patient summary: who exchanges data today The European EHR exchange format (EEHRxF) and why discharge reports still don't travel Transformation layers: wrapping HL7 v2 messages in FHIR AI and natural language processing to structure clinical data at the point of care Trillium Bridge: EU–US patient data exchange, and why it couldn't be funded today Xt-EHR and how joint actions shape the EHDS implementing acts Digital health authorities vs health data access bodies: why one versus many Patient rights under EHDS: access, download, correction and upload Health data activism and patient health literacy The xShare Yellow Button, MyHealth@MyHands and the EU Digital Identity Wallet i2X: 35 pilots, 12 member states and 3 million citizens — and everyone else Is Europe slow? Cross-border exchange compared with North America The EHDS test for 2027: implementing acts and demonstration sites CHAPTERS 03:00 Fifteen years of European interoperability projects 03:36 €200m over twenty years: what EU funding built 06:38 FHIR, implementation guides and the European exchange format 10:10 From drop-down menus to AI: structuring health data 10:48 epSOS, eHAction and the projects that shaped EU digital health 14:02 Trillium Bridge: when the EU and US exchanged health data 15:58 Xt-EHR and why joint actions shape the implementing acts 18:58 Digital health authorities and health data access bodies explained 23:13 Patients and EHDS: access, correction and health data activism 29:04 Is Europe too slow? 3 million in pilots, 400 million waiting 39:08 The best case for 2027: implementing acts that work Recent FODH episodes on interoperability and EHDS: Herko Coomans on interoperability [   • Interoperability Isn't Failing — It's Unde...  ] • Nils Hellrung on openEHR and EHDS [   • Just Connecting systems isn't interoperabi...  ] • Karlien Hollanders, The Agentic Patient [   • "I stopped letting AI read my daughter's m...  ] FACES OF DIGITAL HEALTH Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com LinkedIn:   / faces-of-digital-health   Spotify: https://open.spotify.com/show/4cElKJH... Apple Podcasts: https://podcasts.apple.com/gb/podcast...#EHDS #EuropeanHealthDataSpace #interoperability #digitalhealth #healthdata #MyHealthEU #FHIR #ePrescription #healthpolicy #EEHRxF #patientdata #FacesOfDigitalHealth

September 25, 202653 min

What is NVIDIA actually doing in healthcare? (David Niewolny)

One ultrasound tech, four scanning rooms. NVIDIA's David Niewolny on what "augmented autonomy" asks of clinicians. NVIDIA is best known for GPUs, but its healthcare strategy is a full stack: compute to train models, compute to run them at the point of care, and simulation to generate the data that medical robotics lacks. In this interview, David Niewolny explains how that stack underpins autonomous X-ray and ultrasound with GE HealthCare, surgical robotics simulation with Johnson & Johnson MedTech, and the Open-H surgical robotics dataset. He argues that open models are a regulatory necessity in healthcare, that software-defined medical devices will shrink innovation cycles from years to months, and that the path to autonomy in surgery will follow the one taken by autonomous vehicles, one level at a time. We also press on the parts that remain unsettled: whether AI efficiency turns into clinician burnout, whether synthetic data reflects real patient populations, and what could still stall adoption. GUEST David Niewolny — Senior Director and Global Head of Business Development, Healthcare & Medical, NVIDIA Host: Tjaša Zajc WHAT THE CONVERSATION COVERS - NVIDIA's healthcare strategy: training, simulation and edge deployment for medical AI - Why open models and open datasets matter in regulated healthcare - Autonomous X-ray and ultrasound with GE HealthCare: one technician, multiple rooms - "Augmented autonomy": keeping a clinician in the loop - AI efficiency, cognitive load and the risk of a new wave of clinician burnout - Ambient clinical documentation as the clearest efficiency case - Surgical robotics and the autonomous-vehicle model of stepwise autonomy - Why robotics costs are falling, and what it means for hospital ROI - Synthetic data for healthcare robotics: Cosmos-H, Isaac for Healthcare and the Open-H dataset - Can simulated data reflect a local patient population? - Model drift, verification and validation, and governance of AI agents in healthcare - Is healthcare worried about superintelligence? - Software-defined medical devices and the FDA's predetermined change control plan (PCCP) - How regulators are adapting to AI-enabled devices - Change management and ROI as the real barriers to adoption CHAPTERS 02:00 NVIDIA in healthcare: more than GPUs 03:03 The full stack: training, simulation and edge deployment 08:46 Why open models matter in regulated healthcare 10:51 Autonomous X-ray and ultrasound: one technician, four rooms 14:49 AI augmentation and clinician burnout 18:16 What NVIDIA looks for in a partner, and the surgical robotics bet 23:38 Cheaper robots, more competition, clearer ROI 27:37 Synthetic data, Cosmos-H and the Open-H surgical dataset 31:43 Does simulated data reflect the real world? 34:37 Model drift and governing AI agents in healthcare 38:03 Superintelligence, change management and the autonomous-vehicle analogy 43:10 Software-defined medical devices, the FDA's PCCP and regulators 49:54 A golden age for medtech? What could still slow it down MENTIONED NVIDIA Isaac for Healthcare • Cosmos-H • Open-H-Embodiment dataset • NVIDIA Nemotron • BioNeMo Agent Toolkit GE HealthCare • Johnson & Johnson MedTech (MONARCH platform) • Abridge • Aidoc • OpenEvidence • Sword Health FDA Predetermined Change Control Plan (PCCP) FACES OF DIGITAL HEALTH Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com LinkedIn: https://www.linkedin.com/company/faces-of-digital-health Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 #NVIDIA #healthcareAI #medtech #surgicalrobotics #physicalAI #digitalhealth #medicalimaging #syntheticdata #openmodels #FDA #medicaldevices #healthcarerobotics

September 16, 202646 min

Epic or open platforms? The choice facing European hospitals (Nils Hellrung)

Most interoperability debates focus on exchange formats. In this discussion with Dr Nils Hellrung, medical informatician and CEO Strategy & Operations at vitagroup, Nils focuses on the need for shaping hospital IT with governance and financing. Linking proprietary systems through standards, he says, will always lose meaning, which is why he backs openEHR as a vendor-neutral clinical data core, with FHIR used for exchange. vitagroup in providing the data platform for Catalonia, and just signed a contract with a large hospital group in France. The discussion touches different market approaches to interoperability and challenges of markets such as Germany, where each federal state has its own authority to regulate and implement as they please. Tjasa Zajc and Nils Hellrung talk about what "AI-native" data integration means in practice, why 95% accuracy of AI is not good enough in healthcare, and whether Europe ends up with monolithic US EHRs or open platforms. This episode is supported by vitagroup. GUEST Dr Nils Hellrung — CEO Strategy & Operations, vitagroup; medical informatician; co-author, "Health Information Systems: Architectures and Strategies" Host: Tjaša Zajc WHAT THE CONVERSATION COVERS Why hospital IT architecture reflects governance, trust and financing: three professors, three PACS Billing versus therapy: why German hospital and ambulatory systems don't talk to each other AI in healthcare: the widening gap between what is possible and what happens in practice Why Europe doesn't have to be slower than the US in digital health Data availability as the main barrier to digital health implementation openEHR vs FHIR: clinical data repository vs communication standard FHIR version changes and national profiles: the semantic interoperability problem Is European Health Data Space (EHDS) interoperability achievable, or only for a core data set? Catalonia's single patient record on openEHR: 18 months to go-live and the next step to an open health platform Why Catalonia's single health system makes adoption easier Germany: 16 federal states, 17 data protection authorities, and a 20-year-old electronic patient record (ePA) programme Alliance SIH in France: competitors agreeing on a shared health data layer AI as a translator between clinical data and clinicians AI-native vs AI-enabled software, and why data pipelines must stay deterministic Deskilling risk: clinicians who stop thinking because AI seems to have the answer Epic at Charité, monolithic EHRs vs open platforms, and European digital sovereignty in healthcare Europe's health IT ecosystem in 2036: best, worst and realistic scenarios CHAPTERS 02:30 From textbook to practice: 15 years of health information systems 03:00 Why hospital IT looks the way it does: three professors, three PACS 06:50 Is AI speeding up healthcare? The gap between possible and real 10:39 EHDS and data availability: the biggest barrier to digital health 14:00 openEHR vs FHIR: how health data normalisation works in practice 16:40 Is European interoperability achievable? The semantic problem 20:00 Catalonia: why a single health system makes adoption possible 26:48 Germany: 16 states, 17 data protection authorities 30:45 France: Alliance SIH and a shared data layer 32:14 The data layer as Europe's AI advantage 35:38 AI in data integration: why "convincing" is dangerous 41:48 Agentic AI, guardrails and the risk of deskilling 45:00 Europe in 2036: Epic, open platforms and a "very nineties decision" FACES OF DIGITAL HEALTH Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com Spotify: https://open.spotify.com/show/4cElKJH... Apple Podcasts: https://podcasts.apple.com/gb/podcast... LinkedIn:   / faces-of-digital-health  #openEHR #EHDS #interoperability #digitalhealth #healthdata #FHIR #healthIT #healthcareAI #EuropeanHealthDataSpace #Catalonia #vitagroup #FacesOfDigitalHealth

September 9, 202655 min

Agentic Patient 11: ""I stopped letting AI read my daughter's medical records" (Karlien Hollanders)

She has 408 of her daughter's medical documents. She stopped letting AI read them — here's why. Karlien Hollanders is a pharmacist who became her daughter's medical record. When her firstborn went into complete renal failure at two months old and was diagnosed with primary hyperoxaluria type 1, she spent three years moving between Belgian specialist hospitals that could not exchange data with each other. Eleven years later, 408 documents sit on a national platform that can be filtered by doctor, hospital and date — and nothing else. This episode of The Agentic Patient is about what happens when you actually try to put AI to work on a real, fragmented, non-anonymised medical history: what it organised, what it got wrong, and the reason she has stopped. GUEST Karlien Hollanders — Patient expert and caregiver; consultant to the Belgian federal health service; pharmacist by training Host: Tjaša Zajc THE AGENTIC PATIENT A Faces of Digital Health series on how patients and caregivers actually use AI — which tools, which prompts, which guardrails — and what that does to the clinical relationship. Series hub: https://www.facesofdigitalhealth.com/agentic-patient WHAT THE CONVERSATION COVERS - Being your child's "walking medical record" across five specialist hospitals - Why case management exists for cancer and diabetes but not for rare disease - 408 PDFs, three filters: the missing-metadata problem in national health data platforms - Federated health data explained — and why you still download your records one click at a time - What AI document tools did well: organising records by organ and producing specialty-specific summaries - The timeline AI could not build, and why copy-pasted clinical letters break text-layer summarisation - Why internists and orthopaedic surgeons need the same records and completely different queries - The 15-minute consultation and the unprepared specialist - Local bulk de-identification: the tool that does not exist, and the vendors who could not supply it - A child's medical data on third-party AI platforms, twenty years forward - Patient summaries and cross-border care: what an emergency room in another country actually needs - EHDS versus AI extraction — structuring data at the point of documentation instead of after it - SNOMED CT and structured entry that clinicians do not know they are doing - ZAS Antwerp's AI-generated patient-friendly discharge letters, already in production - Why a shared medication scheme can be five years out of date in a country with electronic prescribing - Personal health vaults, EU wallets and itsme: identity is not the same as data portability - Practical advice: how to assemble your own records before letting AI near them CHAPTERS 03:00 A caregiver's view of patients using AI 04:28 Renal failure at two months: primary hyperoxaluria type 1 10:43 Ten years on: what has changed in Belgian health data sharing 13:04 408 documents, three filters: the metadata problem 15:03 Downloading a federated health record one PDF at a time 17:20 What AI document tools organised — and the timeline they couldn't build 22:27 Same records, different questions: internists vs orthopaedic surgeons 26:38 Not a technology problem: incentives and the missing business case 30:18 AI as a better Google — and where that gets dangerous 32:30 The anonymisation gap: why she stopped feeding AI real data 36:10 Patient summaries, EHDS and structuring data at the source 41:46 ZAS Antwerp's AI-written patient letters, already in production 47:49 Preparing for a specialist visit when your records are scattered MENTIONED European Health Data Space (EHDS) — Regulation (EU) 2025/327 ZAS (Ziekenhuis aan de Stroom), Antwerp — AI-generated patient-friendly letters FACES OF DIGITAL HEALTH Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com LinkedIn: https://www.linkedin.com/company/faces-of-digital-health Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 #TheAgenticPatient #digitalhealth #healthdata #EHDS #patientdata #caregiver #healthAI #rarediseases #interoperability #healthtech #patientempowerment #medicalrecords

August 27, 20261 hr 1 min

Interoperability Isn't Failing — It's Underfunded (Herko Coomans)

35 views Aug 24, 2026 In-person video interviews"Interoperability isn't failing — it's underfunded." Herko Coomans on standards as public infrastructure. Most interoperability conversations start with what's technically broken. This one starts by rejecting that framing. Herko Coomans argues that health data exchange is a wicked problem rather than an unsolved engineering task, that the real constraint is infrastructure funding and governance, and that the moment governments mandated standards, they took on a public accountability they haven't yet resourced. We cover the closing post-COVID funding window, what EHDS implementation actually looks like inside a country that has no national health data authority, why the EU started its data union with health, and where AI genuinely helps interoperability — and where it quietly doesn't. GUEST Herko Coomans — International Digital Health Coordinator, Ministry of Health, Welfare and Sport (VWS), the Netherlands; interoperability lead, Global Digital Health Partnership (GDHP) Host: Tjaša Zajc WHAT THE CONVERSATION COVERS Why interoperability is a "wicked problem," not a failure — and why it was never all-or-nothing Interoperability as an infrastructure funding crisis rather than a technical one The closing post-pandemic window for digital health investment Why healthcare executives are asking the ministry to be MORE directive on standards What changes when standards become law: parliamentary questions about SNOMED CT and nursing terminology Who funds SNOMED CT, HL7 FHIR and IHE for the next 20–50 years The national FHIR profile problem: why a Dutch profile may not work in Germany From product implementation to integration: national platforms as an emerging concept GDHP explained: 44 countries, 40%+ of the world's population, no legal existence by design The global stewardship gap after US and Argentine withdrawal from the WHO The International Patient Summary in practice — Canada, Brazil, and QR-code patient summaries at the Hajj EHDS implementation reality check: 2027, 2029, 2031 deadlines and national health data access bodies Why the EU chose health as the first pillar of its data union — and what Brexit had to do with it AI and interoperability: ambient scribes, ontology reasoning, and why a plausible SNOMED code isn't a correct one The OECD's interoperability valuation: 2.7–6.6% of annual health expenditure Shifting from project funding to sustainable public infrastructure funding for standards CHAPTERS 00:00 Interoperability in 2026: what are we still not getting? 01:06 The pushback: a wicked problem, not a failure 05:50 Why interoperability is an infrastructure funding problem 10:25 Who owns integration? From product rollout to national platforms 16:32 When standards become law: parliament, nurses and SNOMED CT 19:01 National FHIR profiles and the interoperability they don't deliver 25:49 GDHP: 44 countries, 40% of the world, no legal existence 30:52 The Dutch chairmanship and the handover to Portugal 35:35 The International Patient Summary in Canada, Brazil and Mecca 39:52 EHDS reality check: European excitement, national scrambling 47:41 Why the EU started its data union with health 50:58 AI and interoperability: "not the magic, but the magician" 1:00:06 The OECD number: what interoperability is actually worth MENTIONED OECD, "Interoperability in healthcare: Towards an interconnected future" (Health Working Paper No. 197, July 2026) International Patient Summary (IPS) — HL7 FHIR, CDA, ISO, SNOMED Global Patient Set, IHE European Health Data Space (EHDS) • 21st Century Cures Act • My Health Record legislation (Australia) • Ayushman Bharat Digital Mission (India) FACES OF DIGITAL HEALTH Podcast: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com LinkedIn:   / faces-of-digital-health   Apple Podcasts: https://podcasts.apple.com/us/podcast...#interoperability #EHDS #digitalhealth #healthdata #FHIR #SNOMEDCT #healthpolicy #healthIT #GDHP #healthcareAI #europeanhealthdataspace #InternationalPatientSummaryInteroperability Isn't Failing — It's Underfunded (Herko Coomans)

August 18, 202649 min

Agentic Patient 9: She built an AI companion for breast cancer patients - and won't upload her records to ChatGPT

"I am actually quite wildly uncomfortable with patients using LLMs." She built an AI companion for breast cancer patients — and she means it. Ellyn Winters-Robinson was diagnosed with breast cancer in March 2022, months before ChatGPT launched. She wrote a book about it on her iPhone during chemotherapy. That book became AskEllyn, an AI companion used across a hundred countries. In this episode of The Agentic Patient — a Faces of Digital Health series on how patients actually use AI, which prompts, which guardrails — she talks to Tjasa Zajc about what an AI companion can hold that a clinician cannot, and why she still worries about where patient data goes. Guest: Ellyn Winters-Robinson, CEO of The Lyndall Project and AskEllyn, author of "Flat Please Hold the Shame" What the conversation covers: - Building an AI companion from a book written on an iPhone during chemotherapy - Why she keeps AskEllyn strictly non-medical, and how that guardrail held up under health-insurer review - Whether one woman's lived experience can support patients with different cancers, cultures and languages - Why traditional cancer support groups can become "places of collective trauma" - Scanxiety, and what happened when she used her own chatbot during a CT scare - Why she is uncomfortable with patients uploading medical records to ChatGPT or Claude - Patient data rights, desperation, and the risk of being "victimized again" by AI tools - The Canadian Cancer Society-funded study now testing whether AI companions actually help - "The patient is the workflow" — lived experience as an untapped resource in health system design - How clinicians can coach patients to use AI safely instead of pretending they aren't Chapters: 00:00 Intro: why The Agentic Patient series exists 04:00 Meeting Ellyn Winters-Robinson 05:26 Diagnosed in 2022, before ChatGPT existed 07:36 From a book written on an iPhone to an AI companion 08:53 Why nurses and social workers started recommending it 09:54 Can one woman's story support every patient? 12:21 Shame, language, and cultures where breast cancer isn't discussed 13:25 Scanxiety — and taking a pep talk from your own chatbot 15:57 How AskEllyn is built on top of the LLMs 18:19 The non-medical guardrail, and how it held up under insurer review 21:51 Why patient AI use is outpacing the system 29:25 "The patient is the workflow": lived experience as untapped data 34:05 Inside the Canadian Cancer Society study 41:03 Why she's uncomfortable with patients uploading records to LLMs 45:54 The trauma healthcare never sees 6 tips on using AI as a patient: https://youtu.be/DGGVXxB4ygI?si=7m7HqCLKow51KSlQ Faces of Digital Health: Website: https://www.facesofdigitalhealth.com LinkedIn: https://www.linkedin.com/company/faces-of-digital-health Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 Newsletter: https://fodh.substack.com The Agentic Patient series: https://www.facesofdigitalhealth.com/agentic-patient AskEllyn: https://askellyn.ai #DigitalHealth #AIinHealthcare #BreastCancer #PatientAdvocacy #CancerSurvivorship #HealthTech #TheAgenticPatient

August 4, 202640 min

Agentic Patient 8: Why an AI Founder Won't Trust Chatbots With Her Own Health

In this episode of The Agentic Patient, a Faces of Digital Health series on how patients are using AI to find answers the healthcare system didn't give them, Tjasa Zajc talks to Elena Ikonomovska, CEO and Co-Founder of Diadia Health. She stopped trusting chatbots with her own health data — after building an AI company on the problem they create. Elena talks about her two-and-a-half-year journey that followed her mother's death and her own dismissed symptoms — and the causal-reasoning AI she built in response. Guest: Elena Ikonomovska, CEO, Co-Founder & Chief AI Officer, Diadia Health What the conversation covers: - Why Ikonomovska calls generative AI's confident wrong answers "faithful hallucinations" - Building a causal-reasoning engine instead of using large language models for clinical decisions - Why lab "normal" ranges differ by genetics — and what that means for your bloodwork - Her own dismissed thyroid and pre-diabetes symptoms, and what a two-and-a-half-year diagnosis journey actually looks like - The risk of self-diagnosing from ChatGPT-style tools, and what to ask any AI health platform about your data - Why she believes AI should strengthen, not replace, the doctor-patient relationship - Early clinical results: agreement rates with physician judgment and reductions in diagnostic trial-and-error - The equity risk in AI-driven healthcare — who gets access to validated tools, and who doesn't Chapters: 00:00 Intro: why The Agentic Patient series exists 02:30 Meet Elena Ikonomovska and the case for causal AI 03:35 From two decades in machine learning to health AI 07:07 What the model needs: blood panels, genetics, and interactions 09:45 Elena's own diagnosis journey — two and a half years to answers 11:32 Why she wouldn't trust chatbots with her health today 12:40 "Faithful hallucinations": the hidden risk in generative AI 15:04 Inside a causal-reasoning engine built without generative AI 17:32 What happens when clinicians outsource reasoning to chatbots 21:06 Redefining "normal": genetics and personalized lab ranges 27:08 Women's health data gaps and the DTC testing boom 28:13 Strengthening, not replacing, the doctor-patient relationship 31:42 Chatbot safety advice: what patients should never share 38:07 Clinical validation, agreement rates, and what's next Faces of Digital Health: Website: https://www.facesofdigitalhealth.com LinkedIn: https://www.linkedin.com/company/faces-of-digital-health Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 Newsletter: https://fodh.substack.com The Agentic Patient series hub: https://www.facesofdigitalhealth.com/agentic-patient-blog #DigitalHealth #AIinHealthcare #PatientAdvocacy #WomensHealth #HealthTech #ClinicalAI #TheAgenticPatient

July 31, 202637 min

Will robots help us age at home? A historian who lives with one says not yet (Emily Kate Genatowski)

The viral robot videos are choreography, not capability. A historian who has lived with a humanoid robot for a year explains what they still can't do. Emily Kate Genatowski is a historian and AI domestic robotics researcher who bought a humanoid robot, lived with it for over a year, and turned the experience into a TED talk. In this episode of Faces of Digital Health, she separates the demo-reel hype of 2026 from what home robots can actually deliver — and what that means for aging populations, caregiver shortages, and healthcare systems hoping robots will help people stay independent at home. She explains why companion robots and chore robots are still entirely separate machines, why her robot cannot get into a car, why retirees are often more enthusiastic about robots than younger workers — and why she believes we are living through a "digital Engels pause," where productivity rises faster than the mechanisms that distribute its benefits. Guest: Emily Kate Genatowski, historian and AI domestic robotics researcher Her TED talk: https://www.youtube.com/watch?v=rIg-Zt7bFHY What the conversation covers: - Humanoid robot hype in 2026 vs real-world capability - Living with a humanoid robot: logistics, frustration, and transport - Robots for elderly care and aging in place - Companion robots vs domestic chore robots — why they're separate devices - The "digital Engels pause": AI productivity without shared gains - US, China, and EU approaches to AI and robotics regulation - Emotional attachment to robots and robot design - Dual-use risks: humanoid robots and drones in warfare - "One soul, many bodies": the future architecture of home robots CHAPTERS CHAPTERS: 00:00 Intro 02:25 Welcome: a historian who lives with a humanoid robot 03:40 2026 robot hype: why the viral videos are choreography 05:45 Why buy a robot? How the year-long experiment began 07:28 From board games to policy: what the project became 08:57 Robots for aging in place — and who's actually optimistic 09:53 Companion robots vs chore robots: two separate machines 13:55 The digital Engels pause: productivity without shared gains 20:09 US, China, Europe: three regulatory cultures for AI and robotics 25:55 Why the robot travels in a box — and can't get into a car 29:58 Robot guilt: emotional attachment without eyes 33:14 Policy, not technology, as the bottleneck for elderly care robots 35:02 Dual use: drones, warfare, and where robotics could go wrong 37:25 One soul, many bodies: the bifurcated future of home robots FOLLOW FACES OF DIGITAL HEALTH Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com LinkedIn: https://www.linkedin.com/company/faces-of-digital-health Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 #DigitalHealth #Robotics #HumanoidRobots #AgingInPlace #HealthTech #AI

July 22, 202639 min

Data challenges with AI: Omissions, bloated problem lists and unnecessary token burn

Hallucination is not the biggest risk in clinical AI. Omission is — and it is far harder to detect. John Laursen, SVP at IMO Health, has spent his career on the layer of healthcare AI that gets the least attention: clinical terminology and the semantic data infrastructure underneath every model deployed in a hospital. IMO Health's terminology has been built and curated since 1994 and now sits behind roughly 12 billion terminology search transactions a year across US provider organisations and every major EHR. In this interview with Tjaša Zajc, Laursen makes the case that structured data was necessary but is no longer sufficient. AI reasoning across a thirty-year patient chart needs semantic continuity — an understanding that clinical language recorded in the 1990s and language recorded today can mean the same thing. Without it, health systems are investing in models that cannot reliably interpret their own records. The conversation also covers what happens when ambient AI scribes get it wrong, why accumulated clinical data has become a computational cost rather than an asset, and why clinician trust is the constraint that determines how fast clinical AI can move. Guest: John Laursen — Senior Vice President, IMO Health (Chicago, US) Host: Tjaša Zajc — Faces of Digital Health What the conversation covers: - Why omissions, not hallucinations, are the underrated risk in clinical AI - What a semantic layer does that structured data alone cannot - How clinical terminology maps to SNOMED CT and ICD-10 — and why those code sets were built for different purposes - Ambient AI scribes: what happens when a model mishears or over-infers a diagnosis - The billing and clinical consequences of an error entering the patient record - Why problem lists hundreds of entries long now cost money in token burn - Patient-generated and AI-generated content entering the EHR, and why health systems resist it - Translating lay language into clinical terminology without losing specificity - Ambient documentation, billing intensity and friction with payers - How data quality expectations differ between the US, the NHS, the Gulf states and Singapore - Who governs clinical data as coding complexity increases - Why AI performance breaks down on rare disease and the difficult 20% of cases - Knowledge graphs as a grounding source for clinical AI models - What health systems should require from AI vendors before clinical deployment Chapters: 02:20 Why the data layer decides what clinical AI can do 03:27 Inside IMO Health: 12 billion terminology searches a year 05:36 Keeping terminology current: SNOMED, ICD-10 and clinical governance 07:35 The semantic bridge: why structured data alone is not enough 10:17 Patient language versus clinical language in the record 12:23 When an ambient scribe mishears: clinical and billing consequences 14:53 Omissions, bloated problem lists and unnecessary token burn 19:12 Outside the US: the NHS, the Gulf, Singapore and coding complexity 20:46 Who governs clinical data as complexity increases 23:35 Patient-side AI recorders and resistance to external data 26:08 Ambient documentation, billing intensity and payer friction 29:21 The last 20%: rare disease, model limits and AI governance 33:38 Grounding, clinician trust and the cost of misfiring Faces of Digital Health: Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 LinkedIn: https://www.linkedin.com/company/faces-of-digital-health #digitalhealth #healthcareAI #clinicalinformatics #EHR #ambientAI #interoperability #healthdata #SNOMED #healthIT #medicalcoding

July 17, 202643 min

Who Fills The Gap After OpenEvidence Left Europe (Philippe Habets, EvidenceHunt)

OpenEvidence didn't leave Europe because of regulation alone — its ad-and-data business model never fit the European market. When OpenEvidence, the $12B clinical AI search platform used daily by over 40% of US physicians, withdrew from the EU and UK in April 2026 citing the EU AI Act, European clinicians lost a tool many had quietly adopted. In this episode, Philippe Habets — physician-scientist and CEO of Amsterdam-based EvidenceHunt — argues the story is as much about business models as regulation: ad-funded clinical search and selling clinician search behaviour to pharma don't transfer to Europe, where clinicians distrust anything that looks commercial. We also examine what AI evidence search is measurably doing inside hospitals: more uniform knowledge across teams, fewer junior-to-senior consultations, faster decisions — and the open question of whether that convergence improves care or narrows clinical thinking. Guest: Philippe Habets, MD PhD, CEO & co-founder, EvidenceHunt (Amsterdam) What the conversation covers: - Why OpenEvidence left Europe: EU AI Act vs the ad-based and data-selling business model - What hospitals did after OpenEvidence's exit — governance, procurement, shadow AI use - How AI literature search changes clinical decision making and medical hierarchies - Automation bias, tunnel vision and who is liable when AI is wrong - Guardrails in practice: PII stripping, refusing clinical advice, reformulating case questions into research questions - Why LLM answers differ between tools — and the omission problem in complex patients - Living guidelines: automating systematic literature reviews and guideline updates (some protocols are 17 years old) - Should patients have access to the same evidence tools as clinicians? - EvidenceHunt vs OpenEvidence: data sources, GDPR, medical device regulation - What won't change in healthcare AI in the next three years Previous episode with Philippe Habets (2023): https://www.youtube.com/watch?v=F8tC0B4NvpM CHAPTERS 00:00 Introduction 03:11 OpenEvidence leaves Europe: what it meant for a European competitor 04:31 No user spike — but hospitals started asking questions 06:54 What EvidenceHunt is: from PubMed frustration to systematic reviews 11:35 How clinicians actually adopt AI evidence tools 13:46 Uniform knowledge, fewer senior consultations: measured effects on clinical thinking 16:24 Tunnel vision, automation bias and the liability question 18:04 Guardrails in practice: PII stripping and refusing clinical advice 20:31 The omission problem: evidence is group statistics, patients are N of 1 23:26 The real reason OpenEvidence left: ads, data-selling and European distrust 29:03 Should patients have the same evidence tools as clinicians? 31:51 Why disclaimers aren't enough — safeguards must be enforced in the product 33:43 Living guidelines: automating updates for protocols up to 17 years old 40:00 The biggest product challenge: too many features, one clean interface 41:40 What won't change in healthcare AI in the next three years FACES OF DIGITAL HEALTH Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com LinkedIn: https://www.linkedin.com/company/faces-of-digital-health Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 #digitalhealth #healthcareAI #OpenEvidence #EUAIAct #clinicaldecisionsupport #evidencebasedmedicine #healthtech