DESIRE at Erasmus MC
DESIRE predicts on day two after surgery whether a patient can safely be discharged home or to a nursing facility, in order to shorten length of stay.
- What this means:
- The source has been read and our reading of it confirmed — the classifications that let this record be compared with others, the AI product, and the clinical use. That is a check of our work, not a judgement on the AI.
- What happens next:
- Nothing, unless this use case is taken up for assessment. Then the AI product and its clinical use are examined together in seven sections — purpose, evidence, CE conformity, legal duties, ethics, safety, and what a hospital must have in place — each signed by a named institution.
- Checked by:
- A named reviewer at the AIH Lab. This is an independent check: neither the organisation nor the vendor did it.
- Submitted
- 2Curated
- 3Maturing
- 4Validated
- 5Scaled
- Clinical problem
- Predict a future risk
- Point of care
- Follow-up
- Nature of AI output
- A risk score
- Clinical specialty
- Surgery
- Care setting
- Hospital — inpatient
- Patient population
- Not specified in source (hospital harvest)
- Intended use
- DESIRE predicts on day two after surgery whether a patient can safely be discharged home or to a nursing facility, in order to shorten length of stay.
Full study description
Live pilot on real gastro-enterological and oncological surgery patients over a four-month evaluation period. Decision support only - the discharge decision remains with doctors and nurses. Erasmus MC quotes potential savings of "260 opnamedagen" per year on one ward and a reduction from an average stay "van vijf dagen"; these are the hospital own projections, not our measurement, and are not written to any structured metric field. IN-HOUSE model, no external vendor named, so Erasmus MC is recorded as its own vendor organisation. Modelling technique is not stated in the source; recorded as classical_ml without claiming deep learning.
- AI technique
- Classical machine learning
- Input data
- Structured EHR data
- Output type
- Risk score
- Autonomy level
- Informs a human (advisory)
- Model provenance
- Built in-house
- Status
- Pilot
- Country
- Netherlands
- Deployment date
- 1 January 2022
- Data identifiability
- Not stated in the source
Similar deployments
AIContributors
- Deploying organisation
- Erasmus MC · Hospital / health system · Netherlands
- AI vendor
- Erasmus MC · Netherlands
- Product name
- DESIRE
- Source record
- amazingerasmusmc.nl