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AI-Assisted Discharge Documents for Children and Families in a Pediatric Emergency Department
Sponsor: University Hospital, Montpellier
Summary
This study compared two ways of preparing discharge documents for children and families leaving a pediatric emergency department. In the usual workflow, information from the electronic health record was assembled into a fixed document template. In the alternative workflow, ERIOS Assistant, an artificial intelligence system hosted within the hospital, drafted documents for health professionals and for families or children. A clinician reviewed and validated the documents before they were handed to the family in both workflows. According to the completed study report, assignment concerned document production after the decision to discharge and did not determine diagnosis, treatment or disposition. Families were contacted by telephone after discharge to assess how their account of the care and instructions agreed with the medical record, as well as their perceived understanding, satisfaction and use of the documents. The protocol also planned assessment of document quality, readability, clinician experience, usability and human supervision. This study is being registered retrospectively after the randomized component was completed.
Official title: ERIOS ASSISTANT - Pediatric Emergency Department - Phase 3 Study Protocol
Key Details
Gender
All
Age Range
0 Years - 18 Years
Study Type
INTERVENTIONAL
Enrollment
153
Start Date
2025-09-04
Completion Date
2025-11-27
Last Updated
2026-10-15
Healthy Volunteers
No
Conditions
Interventions
Conventional Discharge Documentation Workflow
Routine deterministic discharge-document production workflow used in the pediatric emergency department. An office-automation module transferred selected electronic health record fields into a predefined document template. The resulting document was reviewed, edited if necessary, validated, signed, and delivered by the clinician.
LLM-Assisted Discharge Documentation Workflow
Discharge documentation was generated by Assistant Erios, a hospital-developed on-premises conversational assistant based on the open-weight GPT-OSS 120B language model. The assistant received structured clinical information derived from the electronic health record and generated discharge documentation according to the assigned document strategy. All generated documents were presented to a clinician for review, editing if necessary, validation, signature, and delivery. The intervention affected only document production and did not determine or modify diagnosis, treatment, or disposition.
Locations (2)
Montpellier University Hospital - Pediatric Emergency Department
Montpellier, France
Montpellier University Hospital
Montpellier, France