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Predicting self-intercepted medication ordering errors using machine learning
Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medicatio...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279397/ https://www.ncbi.nlm.nih.gov/pubmed/34260662 http://dx.doi.org/10.1371/journal.pone.0254358 |