This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (2004) Medication Reconciliation (17) Pharmacovigilance (177) Pharmacology and Pharmaceutics (121) Safety and Error Prevention in Health (250) Electronic Health Records (1354) E-Health / Health Services Research and New Models of Care (758) New Methods (1153)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55185, first published .
A Prediction Model to Identify Clinically Relevant Medication Discrepancies at the Emergency Department (MED-REC Predictor): Development and Validation Study

A Prediction Model to Identify Clinically Relevant Medication Discrepancies at the Emergency Department (MED-REC Predictor): Development and Validation Study

A Prediction Model to Identify Clinically Relevant Medication Discrepancies at the Emergency Department (MED-REC Predictor): Development and Validation Study

Authors of this article:

Greet Van De Sijpe1, 2 Author Orcid Image ;   Matthias Gijsen1, 2 Author Orcid Image ;   Lorenz Van der Linden1, 2 Author Orcid Image ;   Stephanie Strouven3 Author Orcid Image ;   Eline Simons1 Author Orcid Image ;   Emily Martens4 Author Orcid Image ;   Nele Persan4 Author Orcid Image ;   Veerle Grootaert4 Author Orcid Image ;   Veerle Foulon2 Author Orcid Image ;   Minne Casteels2 Author Orcid Image ;   Sandra Verelst5 Author Orcid Image ;   Peter Vanbrabant5 Author Orcid Image ;   Sabrina De Winter1 Author Orcid Image ;   Isabel Spriet1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Van De Sijpe G, Schrooten T, De Winter S, Van der Linden L, Vanbrabant P, Dompas A, Bertels B, De Vos M, Spriet I. Development and temporal validation of machine learning models for predicting clinically relevant medication reconciliation discrepancies at the emergency department: A single-center retrospective study. International Journal of Medical Informatics 2026;211:106309 View