LibraryFamily 2 · Clinical Decision Support
CDS — sepsis bundle compliance prompts
Under reviewPilotDeveloping evidence
Undergoing in-depth clinical, technical and governance review.
Prompts the clinical team to deliver each element of the sepsis-6 bundle within the recommended time window, surfacing the next missing step on the ward dashboard.
Plain-language summary
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Clinical context
- Clinical problem
- Recommend a treatment
- Point of care
- Treatment planning
- Nature of AI output
- A recommendation
- Clinical specialty
- Intensive care
- Care setting
- Hospital — inpatient
- Patient population
- Adult inpatients diagnosed with sepsis on general medical wards.
- Intended use
- Prompts the clinical team to deliver each element of the sepsis-6 bundle within the recommended time window, surfacing the next missing step on the ward dashboard.
Technology
- AI technique
- Rule-based, Classical machine learning
- Input data
- Structured EHR data, Vital signs
- Output type
- Recommendation
- Autonomy level
- Informs a human (advisory)
- Model provenance
- Vendor proprietary
- Model version
- cds-2.1
- Built on a general-purpose model
- No
Deployment
- Status
- Pilot
- Country
- Netherlands
- Deployment date
- 12 April 2025
- Sites
- 1
Regulatory & governance
- EU AI Act risk tier
- High-risk
- High-risk basis
- Annex III use case
- Medical device
- No
- EU MDR class
- Not a device
- CE marking
- Not required
- FDA status
- Not applicable
- ISO 14971 risk class
- Medium
- GDPR processing basis
- Public interest
- GDPR DPIA
- Completed
- Data identifiability
- Pseudonymised
- Explainability method
- Intrinsic
- Human oversight model
- Senior nurse confirms each prompt; the AI never administers a treatment autonomously.
NICE evidence standards
- ESF tier
- Tier C — treat / diagnose / calculate risk
- Evidence category
- Category 2
Performance summary
- Headline metric
- Time saved
- Value
- 18
- Subgroup performance assessed
- Yes
- Known bias signals
- Night-shift compliance lower than day-shift; staffing pattern, not algorithm.
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Safety signals
Post-deployment concerns flagged by clinicians, patients or monitoring (proposal §3.7). High or critical signals re-enter a validated use case into maturation automatically.
Two missed bundle prompts during overnight shift hand-over; investigating UI fatigue.
Raised 17 May 2026, 21:01
MediumOpen
Evidence records
Studies and evaluations attached to this use case.
Retrospective validation
Accuracy: 0.91
Population: Retrospective bundle adherence audit, 4k admissions
Prospective observational
Time saved: 18
Population: Sepsis bundle compliance pilot, 1.2k admissions
Contributors
- Deploying organisation
- [demo] Amsterdam UMC · Hospital / health system · Netherlands
- AI vendor
- [demo] Aidoc · Israel
- Product name
- BriefCase CDS