Faces of Digital Health

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.

Faces of Digital Health is a technology podcast hosted by Tjasa Zajc, with 397 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Tjasa Zajc hosts Faces of Digital Health, a technology show with 397 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

• 

  Episode #9

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

  Aug 18, 202649 minS15

  "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

  ListenShow Notes
• 

  Faces of Digital Health

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

  Aug 4, 202640 min

  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

  ListenShow Notes
• 

  Episode #387

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

  Jul 31, 202638 minS13

  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

  ListenShow Notes
• 

  Episode #386

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

  Jul 22, 202639 minS13

  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

  ListenShow Notes
• 

  Episode #285

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

  Jul 17, 202644 minS13

  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

  ListenShow Notes

Based on semantic analysis of episode topics and host coverage, this show is a strong guest fit for executives in:

Industry fit is computed by PitchCentric using vector embeddings of the show's episode catalog.