Faces of Digital Health

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
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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)

"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

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

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

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#DigitalHealth #Robotics #HumanoidRobots #AgingInPlace #HealthTech #AI

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