Faces of Digital Health

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

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

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)

"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