A Prediction Model to Identify Clinically Relevant Medication Discrepancies at the Emergency Department (MED-REC Predictor): Development and Validation Study
Our software-implemented prediction model shows moderate performance, outperforming random or typical selection criteria for medication reconciliation. Depending on available resources, the probability threshold can be customized to increase either the specificity or the sensitivity of the model.
Authors
- Matthias Gijsen (ORCID: https://orcid.org/0000-0003-1378-3772)
- Stephanie Strouven
- Minne Casteels (ORCID: https://orcid.org/0000-0001-9401-5489)
- Veerle Grootaert
- Sandra Verelst (ORCID: https://orcid.org/0000-0002-4506-8629)
- Eline Simons
- Lorenz Van der Linden (ORCID: https://orcid.org/0000-0001-5195-1891)
- Greet Van De Sijpe (ORCID: https://orcid.org/0000-0003-1475-6178)
- Peter Vanbrabant (ORCID: https://orcid.org/0000-0003-0217-3303)
- Veerle Foulon (ORCID: https://orcid.org/0000-0002-4053-3915)
- Isabel Spriet (ORCID: https://orcid.org/0000-0001-6342-0676)
- Sabrina De Winter (ORCID: https://orcid.org/0000-0002-5253-2960)
- Emily Martens
- Nele Persan
Institutions
- AZ Sint-Jan (BE)
- KU Leuven (BE)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-11-27
- DOI
- https://doi.org/10.2196/55185
- Citations
- 1
- Primary Topic
- Pharmaceutical Practices and Patient Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.34
Funders
- KU Leuven