Drug-related iatrogenesis is a major public health issue, accounting for a significant proportion of adverse events and hospitalizations in emergency departments. Optimizing prescription management in this context is critical to improve both patient safety and physician efficiency This study aims to evaluate the impact of the POSOS AI-driven device on the medical time required for prescription management in polymedicated patients admitted to emergency departments. The main objective is to establish whether the use of POSOS can reduce transcription time compared to standard electronic management.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
OTHER
Masking
NONE
Enrollment
770
Prescription management using current hospital-standard databases and tools
Prescription management supported by POSOS device (OCR+AI) for structured data entry and clinical decision support
CHU Amiens
Amiens, France
Medical time required for the transcription of prescriptions
Medical time required for the transcription of prescriptions for at-risk polymedicated patients at emergency admission. This is measured by the duration needed to transcribe prescriptions into the structured electronic health record by physicians, assessed by direct observation with a stopwatch
Time frame: Day 1
Number of drug-related problems (DRPs) identified per patient
Time frame: day 1
Proportion and type of transcription errors (medication name or dosage)
Time frame: day 1
Identification of DRPs by subtype and severity
Time frame: day 1
Rate of reconciled medication histories and structured documentation
Time frame: day 1
Time delays between triage, anamnesis, and diagnosis
Time frame: day 1
Length of emergency department stay and downstream hospitalizations
Time frame: day 1
Readmission rates
Time frame: at 3 months
Overall survival
Time frame: at 6 months
Mapping of DRPs by subtype and severity
Time frame: day 1
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