GCRI · Health Nexus
Digital health risk
Health systems now depend on digital infrastructure for records, laboratory results, imaging, prescribing, supply logistics and surveillance, and increasingly on algorithms that support clinical and public health decisions. These dependencies bring their own failure modes: outages, cyberattacks, data breaches and models that perform unevenly or drift. This page sets out how digital risk develops in health, how it is measured, and what the evidence serves for those who govern health data and technology.
Digital health risk on the record.
Digital health risk: how the risk develops.
Data, AI safety and digital infrastructure. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Outages stop clinical work
When electronic records, laboratory systems or imaging archives go down, care continues on paper only if downtime procedures have been rehearsed. Shared suppliers and cloud services mean one failure can reach many hospitals at once.
Ransomware targets continuity
Attackers encrypt systems and exfiltrate data because health providers cannot tolerate downtime. Diverted ambulances, delayed diagnostics and cancelled procedures follow, and full recovery can take weeks.
Data harms persist
A health data breach exposes sensitive information that cannot be revoked. Weak consent, re-identification of shared datasets and secondary use beyond the original purpose erode trust, and with it the willingness to share data in an emergency.
Algorithms fail unevenly
Clinical and public health models trained on unrepresentative data can perform worse for some groups and degrade as practice, populations or data systems change. Without monitoring after deployment, drift and bias go unnoticed.
Environmental exposure in Africa.
Population density, daytime surface heat and night lights over Africa, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Environmental exposure, for this page
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS NOAA-20 Deep Blue AOD (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite retrieval; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 2 km grid
- Uncertainty
- Satellite measure of haze, smoke and dust through the whole air column on the day shown; an exposure indicator, not ground-level PM2.5, an air quality index or a health warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Environmental exposure, framed on Africa.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS and VIIRS atmosphere products, NASA LANCE and LAADS DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (14 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Aerosol optical depth, daily: VIIRS NOAA-20 Deep Blue AOD, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
Digital health depends on energy, networks and trust.
| Domain | How it couples here | What to watch |
|---|---|---|
| Energy | Records, imaging and telemedicine stop without power and connectivity. | Downtime procedures and their exercise |
| Water | Ransomware has disrupted hospitals and utilities alike, so shared suppliers matter. | Shared software suppliers across sectors |
| Food | Nutrition and supply data run on the same platforms as clinical systems in some programmes. | Platform concentration |
| Biodiversity | Genomic data and biological samples raise benefit-sharing questions. | Data governance agreements |
WHO’s Global strategy on digital health 2020–2025 and its guidance on the ethics and governance of AI for health (2021, with guidance on large multi-modal models in 2024) set the policy frame. ENISA’s threat landscape for the health sector (2023) found ransomware the leading threat to European health organisations in its reporting period.
Health supply continuity.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Health supply continuity
Digital health risk · Province B · Month 6
Tracer commodities
| Oral rehydration salts | 2.1 | 3 | 5 | Low |
| First-line antibiotic | 1.5 | 11 | 14 | Low |
| Insulin | 2 | 1 | 13 | Adequate |
| Rapid diagnostic tests | 0.8 | 13 | 6 | Critical |
Order in transit
- Order
- PO-44118
- Route
- Port of entry to central store
- ETA
- 12 days
- Cold chain
- Monitored
Facilities stocked out by district
Digital health evidence.
Studies of digital dependence in health are designed for the Exponential Technology series; AI tools studied would carry NS-12 cards under the proposed Nexus Standards. The Global Centre for Risk and Innovation (GCRI) responds to no incidents. Useful contributions: downtime experiences and evaluations of clinical AI on local populations.
Model digital health risk as a system. Run the cascade.
Assembled from the Nexus Standards area profile Health security and One Health: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Antimicrobial resistance
- Tuberculosis surge (set off downstream)
- Typhoid and paratyphoid fever (set off downstream)
- Health-system overload (set off downstream)
Systems and couplings
- Health security and One Health
- Biological hazard standards Mechanism
- Biodiversity and nature risk standards Common driver
- Food and agriculture risk standards Mechanism
- Global and systemic risk Cascade
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H83Hazard family: Health system capacity failure (NHS-1 health.system)NRS-H42Hazard family: Rare and persistent human infections (NHS-1 health.other-human)NRS-H41Hazard family: Animal-to-human spillover (NHS-1 health.zoonotic)NRS-H43Hazard family: Livestock and aquaculture epizootics (NHS-1 health.animal-health)NRS-D3Humanitarian response and resilient recoveryNRS-H46Hazard family: Environmental and product harm to people (NHS-1 health.harm)
Observation
- WHO Disease Outbreak News (listed for reference)
- WAHIS animal health events (listed for reference)
- WorldPop gridded population (listed for reference)
- International Health Regulations capacity and health emergency preparedness
- Percentage of bloodstream infections due to selected antimicrobial-resistant organisms
Example geography
- West Africa: 59 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Reported health data breaches | incidents; records affected | monthly | Regulatory breach notification registers | Published statistics |
| Ransomware incidents affecting health providers | incidents | quarterly | Sector threat reports and incident disclosures | REPORTED |
| Unplanned clinical system downtime | hours | monthly | Hospital IT service records | OBSERVED |
| Known exploited vulnerabilities on health technology | open vulnerabilities on deployed assets | weekly | Vulnerability catalogues matched to asset inventories | OBSERVED |
| Use of agreed exchange standards | percent of registered systems | annual | National digital health architecture registries | REPORTED |
| Adverse event reports for AI-enabled devices | reports | quarterly | Medical device adverse event databases | REPORTED |
| Digital health maturity | phase across the monitor’s indicators | periodic | Global Digital Health Monitor self-assessment | DECLARED |
How digital health risk is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in digital health risk: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| Global Digital Health Monitor | Country digital health maturity across governance, workforce, standards and infrastructure | Open, attribution, terms of use |
| US HHS Office for Civil Rights breach portal | Public records of reported breaches of protected health information | Public records |
| ENISA threat landscape for the health sector | Analysis of cyber incidents affecting European health organisations | Public report, ENISA terms of use |
| CISA Known Exploited Vulnerabilities catalogue | Vulnerabilities with evidence of active exploitation | Public domain |
| US FDA MAUDE and the list of AI-enabled medical devices | Device adverse event reports and authorised AI-enabled devices | Public domain |
| AI Incident Database | Documented incidents involving AI systems, health included | Open, attribution |
Who decides, and what the evidence serves.
Health Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Hospital boards and chief information officers | Which systems need offline fallback, network segmentation or redundant suppliers | Dependency maps; downtime scenarios with their clinical consequences stated |
| Data protection authorities | Whether health data sharing and secondary use meet legal and consent requirements | Data flow inventories; re-identification risk assessments |
| Medical device regulators | Whether an AI-enabled device needs further evidence, restrictions or post-market study | Performance by subgroup; drift monitoring; adverse event reports |
| Ministries of health and digital health agencies | Which standards and architectures national systems must adopt | Interoperability and maturity evidence; continuity requirements |
Outputs this topic produces
- Digital dependency maps of clinical, laboratory and supply systems
- Downtime and cyber incident scenarios for continuity planning
- Data governance and interoperability evidence read against named standards
- Post-deployment monitoring designs for health algorithms
What experts ask.
Digital health risk is a patient-safety question as much as a technical one.
- Which clinical services stop or slow when a core system is unavailable?
- How exposed are health organisations to ransomware and other cyber incidents, and how do they recover?
- How do AI tools used in diagnosis or triage perform across populations, and how is that monitored?
- Who can access health data, under what safeguards, and with what public trust?
- How dependent is care on a small number of software and cloud providers?
Mechanisms and drivers.
Health organisations run many interconnected systems, often including legacy software and medical devices that are hard to update. A cyber incident or major outage can force a hospital onto paper processes, delay results, divert ambulances and cancel treatment. Recovery can take weeks because systems must be rebuilt and verified.
AI tools learn from historical data and can perform worse for groups underrepresented in that data, or degrade when clinical practice or equipment changes. Concentration on a few vendors and cloud platforms creates shared points of failure across many organisations at once.
How it is studied, and where the methods stop.
| Method or data | What it shows | Limits |
|---|---|---|
| Incident analysis and after-action reviews | How outages and attacks affected care | Many incidents are unreported or reported without clinical detail |
| Dependency and recovery-time mapping | Which systems support which services | Requires internal information that organisations hold closely |
| Clinical AI evaluation and monitoring | Accuracy, calibration and fairness across groups | Performance can drift after deployment |
| Privacy and governance review | Whether data use meets legal and ethical standards | Rules differ by jurisdiction and change over time |
How it is studied, and what the methods cannot say.
- 01Data engineering, interoperability and stewardshipLinking health information systems · Privacy-preserving publicationApplied here
- 02Probability and statisticsSurvey-weighted and small-area estimation · Expected baselines for excess mortalityApplied here
- 03Causal inference and impact evaluationEvaluating public health measures · Exposure-response for heat and airApplied here
- 04Complex systems and agent-based modellingContact and mobility networks · Cascades across health and infrastructureApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
- Global strategy on digital health 2020–2025 (WHO, 2021)
- Ethics and governance of artificial intelligence for health (WHO, 2021) and guidance on large multi-modal models (WHO, 2024)
- ENISA Threat Landscape: Health Sector (European Union Agency for Cybersecurity, 2023)
- NS-12, Software, model and AI/ML artefacts, a proposed standard
Questions and answers.
Why are hospitals frequent targets of ransomware?
What does interoperability mean in health?
How is the safety of a clinical AI model checked?
Why do cyber incidents in health matter so much?
Does Health Nexus test medical AI products?
Where can I report a security vulnerability on this site?
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