LibraryFamily 12 · Care Coordination & Pathways
Hospital discharge readiness & follow-up scheduling
Under reviewPilotDeveloping evidence
Undergoing in-depth clinical, technical and governance review.
Predicts discharge readiness from clinical and operational signals, drafts the discharge summary structure, and proposes a follow-up appointment slot for primary care.
Plain-language summary
AIChoose an audience and generate a tailored summary on demand. The AI uses only what is on this page.
Clinical context
- Clinical problem
- Optimise a process
- Point of care
- Discharge & transition
- Nature of AI output
- A recommendation
- Clinical specialty
- General practice
- Care setting
- Hospital — inpatient
- Patient population
- Adult inpatients reaching the end of their hospital stay.
- Intended use
- Predicts discharge readiness from clinical and operational signals, drafts the discharge summary structure, and proposes a follow-up appointment slot for primary care.
Technology
- AI technique
- Classical machine learning, NLP / large language model
- Input data
- Structured EHR data, Clinical notes / free text
- Output type
- Recommendation
- Autonomy level
- Human in the loop (human acts)
- Model provenance
- Built in-house
- Model version
- disc-1.0
- Built on a general-purpose model
- Yes
Deployment
- Status
- Pilot
- Country
- Netherlands
- Deployment date
- 5 March 2025
- Sites
- 1
Regulatory & governance
- EU AI Act risk tier
- Minimal risk
- 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
- Low
- GDPR processing basis
- Public interest
- GDPR DPIA
- Pending
- Data identifiability
- Pseudonymised
- Explainability method
- Intrinsic
- Human oversight model
- Ward team approves every discharge decision and follow-up booking; the AI assembles drafts only.
NICE evidence standards
- ESF tier
- Tier A — system / service
- Evidence category
- Category 1
Performance summary
- Headline metric
- Time saved
- Value
- 24
- Subgroup performance assessed
- No
- Known bias signals
- —
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Evidence records
Studies and evaluations attached to this use case.
Prospective observational
Time saved: 24
Population: Discharge planning pilot, ward team minutes saved per discharge
Contributors
- Deploying organisation
- [demo] Erasmus MC · Hospital / health system · Netherlands
- AI vendor
- —
- Product name
- —