LibraryFamily 9 · Patient Monitoring (ICU/Ward)
Continuous deterioration monitoring on general wards
CuratedPilotEmerging evidence
Reviewed for completeness and published to the Library.
Analyses continuous vital-sign streams to detect early physiological deterioration and reduce unrecognised clinical decline between observation rounds.
Plain-language summary
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Clinical context
- Clinical problem
- Monitor a patient's status
- Point of care
- At the bedside
- Nature of AI output
- An alert
- Clinical specialty
- Intensive care
- Care setting
- Hospital — inpatient
- Patient population
- Adult inpatients on general wards with continuous wearable vital-sign monitoring.
- Intended use
- Analyses continuous vital-sign streams to detect early physiological deterioration and reduce unrecognised clinical decline between observation rounds.
Technology
- AI technique
- Classical machine learning, Statistical model
- Input data
- Vital signs, Waveforms (ECG, EEG…)
- Output type
- Alert
- Autonomy level
- Informs a human (advisory)
- Model provenance
- Built in-house
- Model version
- —
- Built on a general-purpose model
- No
Deployment
- Status
- Pilot
- Country
- Denmark
- Deployment date
- 1 March 2025
- Sites
- 1
Regulatory & governance
- EU AI Act risk tier
- Not assessed
- High-risk basis
- Not applicable
- Medical device
- No
- EU MDR class
- Not a device
- CE marking
- Not required
- FDA status
- Not applicable
- ISO 14971 risk class
- Not assessed
- GDPR processing basis
- Public interest
- GDPR DPIA
- Pending
- Data identifiability
- Pseudonymised
- Explainability method
- Intrinsic
- Human oversight model
- Nurse-facing alerts trigger a structured review; no automated intervention.
NICE evidence standards
- ESF tier
- Tier B — inform / monitor
- Evidence category
- —
Performance summary
- Headline metric
- Sensitivity
- Value
- 0.82
- Subgroup performance assessed
- No
- Known bias signals
- —
Similar deployments
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Evidence records
Studies and evaluations attached to this use case.
Retrospective validation
Sensitivity: 0.82
Population: General ward inpatients, 6k admissions
Contributors
- Deploying organisation
- [demo] Rigshospitalet · Hospital / health system · Denmark
- AI vendor
- —
- Product name
- —