Faces of Digital Health

Tjasa Zajc
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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.

  1. 8月27日

    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)

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

  2. 8月18日

    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

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

  3. 8月4日

    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

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

  4. 7月31日

    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

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

  5. 7月22日

    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

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

  6. 7月17日

    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

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

  7. 7月14日

    The AI Model Race Is Over. The Data Race in Healthcare Is Just Starting (Robert Tovornik, Better)

    AI models are "eager to please" — and in healthcare, that's a liability. So what should LLMs never be allowed to do in clinical software? Three years after GPT-3 reached the public, frontier models have largely converged in capability. In this episode, Robert Tovornik, Innovation Lead at Better — the healthcare IT company building on openEHR — explains why the real differentiator in healthcare AI is no longer the model but the data layer underneath it. He makes the case for keeping clinical coding and terminology in deterministic systems, confining LLMs to retrieval and orchestration, and validating AI the way you'd come to trust a colleague: through experience, not certification. Guest: Robert Tovornik, Innovation Lead, Better What the conversation covers: - Why frontier LLMs are converging — and why context now matters more than model capability - What AI should never do in clinical software: inference vs retrieval - Why ICD-10 and SNOMED coding should stay in deterministic systems, not ChatGPT - How to validate non-deterministic AI systems when unit tests no longer work - Automation bias: what happens when users stop checking AI outputs - Conversational EHRs — solving the "missing button" problem in clinical interfaces - Vibe coding vs regulated clinical software: why one iterates in hours and the other in years - An ambient AI scribe built in two weeks — deployed in India, stalled in Europe - EU AI Act, data residency laws, and the cost of compliance - Digital twins, ambient AI, and what hospitals should invest in before deploying AI Faces of Digital Health explores how healthcare systems around the world adopt digital technologies and AI. 🔗 Website: https://www.facesofdigitalhealth.com 🎧 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 💼 LinkedIn: https://www.linkedin.com/company/faces-of-digital-health #healthcareAI #openEHR #digitalhealth #healthIT #EHR #clinicalAI #healthcareinnovation 02:20 Three years of GPT: from model capability back to data and context 04:26 How AI changed software development inside an openEHR vendor 06:13 Clients now arrive with AI-informed (and misinformed) requirements 09:05 Why clinical coding belongs in deterministic systems, not ChatGPT 11:27 "Eager to please": why LLMs shouldn't be trusted with inference 14:03 Validating non-deterministic AI when unit tests no longer work 16:50 How do you trust AI? The same way you trust a colleague 18:03 Regulation, compliance costs, and the automation bias problem 20:09 Conversational EHRs and the missing-button problem 23:02 Vibe coding vs iterating regulated clinical software 25:23 Users are building AI experience faster than health systems 27:50 An ambient scribe built in two weeks — adopted in India, stalled in Europe 29:16 Data quality as the differentiator between good and bad AI systems 31:10 Ambient AI, operation prep, and the digital twin horizon

    The AI Model Race Is Over. The Data Race in Healthcare Is Just Starting (Robert Tovornik, Better)

  8. 6月19日

    Agentic Patient 7: How to Use AI as a Caregiver — Without Letting It Diagnose | Pratik Desai

    AI couldn't cure his mother's stage 4 cancer. It caught three near-fatal errors, found a same-day appointment, and helped her leave on her own terms. When Pratik Desai's mother was diagnosed with stage four duodenal adenocarcinoma — a rare cancer with roughly 3,000 US cases a year — she was nearly discharged without an oncology appointment. Over the next 76 days, Desai used AI at her bedside, from 5am to 10pm, to understand each report, prepare for every appointment, and push a stretched health system to move at the pace her diagnosis demanded. This is a frank account of where AI helped, where it didn't, and the line he refuses to cross. This is a 1:1 interview in The Agentic Patient — a Faces of Digital Health series on how patients and caregivers actually use AI: which tools, which prompts, and which guardrails. GUEST Pratik Desai — New Jersey-based AI practitioner; caregiver and builder of a free, local AI tool for patients HOST Tjaša Zajc — Founder & host, Faces of Digital Health / The Agentic Patient WHAT THE CONVERSATION COVERS - Using AI to interpret a biopsy report and push for a same-day "stat" CT scan - Why AI and the doctors agreed on the care — and clashed on the speed - Finding a same-day oncology appointment through an AI-assisted network search - An error-riddled CT report the AI refused to read — and what it did to trust - Running three Claude "personas" as built-in second and third opinions - A local, open-source AI tool that keeps medical data off the cloud - How to prompt as a patient or caregiver: awareness, knowledge, advocacy — not diagnosis - Where AI failed him: prognosis, and the rule he broke under pressure - Defining quality of life when the outcome is already known CHAPTERS 0:00 How patients use AI — and the guardrails 1:20 Day one: a healthy mother, a diagnosis no one would name 3:34 The first prompt, and pushing for a stat CT scan 7:43 Using AI in the open: agreement on care, friction on speed 9:35 The counterfactual: 76 days with AI at the bedside 12:40 Finding a same-day appointment through a network search 13:40 The CT report the AI refused to read 15:50 When trust erodes: good faith, not competence 18:41 Why switching hospitals wasn't an option 21:54 Defining quality of life: her three goals 28:27 Three Claude personas, and a local private tool 35:12 How to prompt: awareness, knowledge, advocacy — not diagnosis 37:54 Where AI fell short, and the closing asks THE AGENTIC PATIENT SERIES New to the series? Start here → [PASTE PREVIOUS AGENTIC PATIENT EPISODE LINK] All episodes → https://www.facesofdigitalhealth.com/agentic-patient-blog MORE FROM FACES OF DIGITAL HEALTH 🌐 Website: https://www.facesofdigitalhealth.com 📨 Newsletter: https://fodh.substack.com 🎙 Podcast (Apple): https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 💼 LinkedIn: https://www.linkedin.com/company/faces-of-digital-health Pratik's tool Regana: https://github.com/RaganaCorp/openhealth-prototype-1 #DigitalHealth #HealthAI #AgenticPatient #PatientAdvocacy #AIinHealthcare #CancerCare #Caregiving #FacesOfDigitalHealth

    Agentic Patient 7: How to Use AI as a Caregiver — Without Letting It Diagnose | Pratik Desai
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予告編

番組について

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.

情報

  • クリエイター
    Tjasa Zajc
  • 配信期間
    17年 - 26年
  • エピソード
    399
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © Copyright 2018. All rights reserved.
  • 番組のWebサイト
    Faces of Digital Health

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