Faces of Digital Health

Her relative said it was nothing. Two weeks of symptom history in ChatGPT said get checked. She was diagnosed at stage one.

In early 2023, Alvina Nadeem was 36, a mother of two young boys, and tired in a way she put down to burnout or perimenopause. She was already a heavy ChatGPT user through her work in change management, so she tried something she had never done: she described her symptoms to it. When the model refused to answer first-person health questions, she reframed herself as a clinical case for a medical resident and asked it to explain its reasoning. Ovarian cancer appeared as one of four possibilities. What followed — a post-it note of symptoms to present together, a GP who ordered imaging, a stage one diagnosis — is the starting point for a wider conversation about how patients actually use AI: to navigate the health system, to filter misinformation, and to carry their own story between specialists who cannot see each other's notes.

This episode describes one patient's experience and is not medical advice. If you have persistent symptoms, speak to a clinician.

GUEST

Alvina Nadeem — Professional engineer (P.Eng.), change management advisor and leadership coach; Board member, Ovarian Cancer Canada; Co-director, Cancer and Work; Coalition Priorité Cancer au Québec

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 relationship with clinicians.

Series hub: https://www.facesofdigitalhealth.com/agentic-patient

WHAT THE CONVERSATION COVERS

• "No-lution": why dismissing patient AI use hides how people already use it

• Shadow AI in healthcare: patients were using ChatGPT long before health systems noticed

• Using ChatGPT for health questions in 2023, when the model refused first-person medical queries

• Role-play prompting: presenting yourself as a clinical case and asking the model to show its reasoning

• Prompting for possibilities rather than confirmation — and why that limits sycophancy

• Ovarian cancer symptoms: bloating, early satiety, fatigue, cramping and abnormal bleeding as a constellation

• Why ovarian cancer is diagnosed late, and why age 36 put her outside anyone's differential

• How to describe symptoms to your doctor so they lead to the right tests

• CA-125 testing and the routine pelvic pain workup

• Sharing health data with ChatGPT: privacy, identifiers and the risk-reward of a crisis

• AI as a health system navigation tool: which specialist, which questions, is this wait normal

• The worried well, wellness misinformation and AI as a filter

• Practical advice: approach the model as a tester, and end every session with questions for your doctor

• Emergency versus chronic illness: where general chatbots lose context

• Patient-built AI tools for specific conditions

• Continuity of care: the patient as the relay between family doctor and oncologist

CHAPTERS

03:00 The Agentic Patient: how patients use AI in their care

04:14 "No-lution" and the shadow AI already inside healthcare

07:38 ChatGPT in early 2023: a cautious, restricted tool

10:10 Learning to prompt: role, context and the role-play workaround

16:54 Challenging the model: sycophancy then and now

19:36 Presenting a symptom constellation to get the right tests

25:09 The post-it note, the pelvic exam and the imaging referral

27:49 CA-125 and the routine pelvic pain workup

29:00 What patients use AI for: health system navigation

33:31 The worried well, misinformation and AI as a filter

37:21 Practical advice: test the tool, end with questions for your doctor

39:45 Emergency versus chronic: where general chatbots fall short

46:28 AI as the keeper of a patient's story between specialists

MENTIONED

Ovarian Cancer Canada • Cancer and Work • Coalition Priorité Cancer au Québec • CA-125 (cancer antigen 125) test • ChatGPT

Discussion with Russ Read Barrow: https://podcasts.apple.com/us/podcast/the-agentic-patient-2-one-tool-one-job-cancer/id1194284040?i=1000764804039

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 #OvarianCancer #PatientAI #ChatGPT #healthAI #patientnavigation #earlydiagnosis #digitalhealth #patientempowerment #cancercare

Three in ten AI pilots at Sheba Medical Center never scale. Eyal Zimlichman on why, and what Europe's regulation holds back.

Nine years after his first Faces of Digital Health interview, when ARC was still being built inside Sheba Medical Center, Eyal Zimlichman returns with the numbers. He explains how Sheba measures the return on AI (clinical outcomes, cost and clinician burnout), why its chief quality officer rather than its AI officer signs off every deployment, and why about 30% of pilots are discontinued. We discuss what ambient scribes change for patient safety, documentation and malpractice, why hospitals still treat start-ups as a nuisance, why no European hospital belongs to Mayo Clinic Platform's federated data network, and how ARC plans to grow tenfold in 2027.

GUEST

Prof. Eyal Zimlichman — Chief Transformation, Innovation and AI Officer, Sheba Medical Center, Israel; founder and director, ARC Innovation

Host: Tjaša Zajc

WHAT THE CONVERSATION COVERS

- What has changed at Sheba Medical Center and ARC since 2017

- How a hospital measures AI return on investment: outcomes, cost, access and burnout

- Aidoc and AI triage for intracranial haemorrhage: the Sheba mortality study

- AI governance in hospitals: why the chief quality officer approves AI deployments

- Time-limited AI approvals: 3–12 month reviews based on risk

- Ambient scribes: accuracy differences between vendors and medication-name errors

- Ambient documentation, billing and the case for fewer malpractice claims

- Why 30% of hospital AI pilots fail to scale, including an internally built discharge alert

- Open innovation: why healthcare systems distrust start-ups

- Procurement, tenders and the barriers to bringing in new technology

- Europe as a "regulation superpower": unclear rules, cautious lawyers and the Charité ARC centre

- Mayo Clinic Platform and federated health data sharing without European partners

- Hospitals as economic engines: ARC start-ups, jobs and KPIs

- China, AI regulation and the speed of adoption

- ARC Global in 2027: tenfold scale-up and AI-native hospitals by 2030

CHAPTERS

02:43 Nine years on: Sheba, ARC and AI since 2017

06:52 How to measure AI return on investment in a hospital

10:58 Patient safety and ambient scribes: who approves AI

15:51 Ambient documentation, billing and malpractice

18:06 The pilots that didn't scale, and why

21:47 Open innovation: why hospitals distrust start-ups

26:35 Europe as a "regulation superpower"

30:43 A hospital as an economic engine: start-ups and jobs

36:13 China, AI regulation and speed

39:27 ARC in 2027: tenfold growth and AI-native hospitals

MENTIONED

ARC Innovation at Sheba Medical Center • ARC Global • Charité BIH ARC Innovation Center (Berlin)

Aidoc • Mayo Clinic Platform • ARC Summit, Tel Aviv, 22 October 2026

Orion et al., International Journal of Emergency Medicine (2023) — AI triage for intracranial haemorrhage

Earlier FoDH episode: Eyal Zimlichman, 2017 [LINK]

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

#healthcareAI #digitalhealth #hospitalinnovation #ambientAI #AIgovernance #patientsafety #ShebaMedicalCenter #healthtech #openinnovation #healthpolicy #AIregulation #FacesOfDigitalHealth

When prevention works, nothing happens. That's why it gets cut. Joanna Lane on financing prevention as long-term investment.

Health systems in Europe spend a small fraction of their budgets on prevention, and that fraction is renewed annually and cut first when treatment costs rise. Joanna Lane has spent decades working on health and regional development across Europe, and argues that prevention should be reclassified from expenditure to capital investment, financed the way we finance energy grids, transport and pensions. In this conversation she explains what the Horizon Europe project Invest4Health learned from eight regional testbeds, why Public Health Wales disinvested around £10m to reinvest it, how health systems' own procurement and hiring can improve the determinants of health, and what finance ministers need to hear before they will treat prevention as an investment. We also discuss wearables and the longevity trend, where personal responsibility ends and public policy begins, and a three-layer model for funding prevention at scale.

GUEST

Joanna Lane — Managing Director, Stichting Health ClusterNET; Chair, FH Europe Foundation; Invest4Health (Horizon Europe)

Host: Tjaša Zajc

WHAT THE CONVERSATION COVERS

- Why the benefits of prevention stay invisible in health budgets

- Why local prevention and health promotion budgets are the first to be cut

- Invest4Health: eight regional testbeds for financing health promotion and disease prevention

- Smart capacitating investment and the EU high-level task force on social infrastructure

- Why a single financing template didn't work across regions

- Financing toolkits and competence building for regional health authorities

- Reclassifying prevention as capital investment and soft infrastructure

- Public Health Wales: disinvesting from health promotion to reinvest around £10m

- Prevention spending during and after COVID, and the pull back to acute care

- Defence, energy and climate competing for health budgets

- The ripple effect of health spending: local procurement, inclusive employment and NEETs

- How to make the investment case for prevention to a finance minister

- Local Multiplier 3 (LM3) and the return on local procurement

- 20-minute neighbourhoods and multi-use community hubs for older people

- Remote monitoring for independent living: the SMILE project

- Wearables, longevity and the retention problem in consumer digital health

- Personal responsibility vs public policy: school food, screening and familial hypercholesterolaemia

- Three layers of prevention finance: protected budgets, social outcome funds and patient capital

- Metrics for treating prevention as investment in national statistics

CHAPTERS

2:00 The Finance Minister Anecdote
3:18 Welcome & 40 Years in Prevention
3:54 The Invisible Challenge of Prevention
6:12 The Invest for Health Project
10:47 Financing Toolkit & Training for Regions
12:02 Prevention as Capital Investment
13:03 Bold Examples: Disinvest to Reinvest
16:44 COVID's Regression in Prevention
18:04 Healthcare's Ripple Effect on Local Economies
20:50 Engaging Finance Ministries
23:25 Evidence for Return on Investment
25:07 Community Design for Prevention
27:34 Longevity & Digital Health Solutions
33:11 Personal Responsibility vs. Systemic Prevention
36:11 A Three-Layer Financing Model for Prevention

MENTIONED

Invest4Health — Horizon Europe project on financing health promotion and disease prevention

High-Level Task Force on Investing in Social Infrastructure in Europe (report, January 2018)

Syreon Research Institute, Budapest • Public Health Wales • Local Multiplier 3 (LM3)

SMILE project (Innlandet Hospital Trust, Norway) • FH Europe Foundation • EU Safe Hearts Plan

Earlier FoDH episodes: Herko Coomans on interoperability as an infrastructure funding problem [LINK] • Henrique Martins on twenty years of EU health data projects [LINK]

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

#prevention #publichealth #healthfinancing #healthpromotion #diseaseprevention #Invest4Health #healthpolicy #healtheconomics #socialinfrastructure #healthinvestment #digitalhealth #FacesOfDigitalHealth

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

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