Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention
| Autor: | Jorge M. Núñez-Córdoba, Ricardo Mateo, Cristina Gordo |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Drug
Root (linguistics) Drug-Related Side Effects and Adverse Reactions media_common.quotation_subject Identification error Mixed method design 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Nominal group technique Medicine Humans Medication Errors 030212 general & internal medicine Adverse effect General Nursing media_common 030504 nursing business.industry Medication administration Root cause Hospitals Cross-Sectional Studies Spain Artificial intelligence 0305 other medical science business |
| Zdroj: | Journal of advanced nursingREFERENCES. 77(7) |
| ISSN: | 1365-2648 |
| Popis: | Aims To identify and prioritize the root causes of adverse drug events (ADEs) in hospitals and to assess the ability of artificial intelligence (AI) capabilities to prevent ADEs. Design A mixed method design was used. Methods A cross-sectional study for hospitals in Spain was carried out between February and April 2019 to identify and prioritize the root causes of ADEs. A nominal group technique was also used to assess the ability of AI capabilities to prevent ADEs. Results The main root cause of ADEs was a lack of adherence to safety protocols (64.8%), followed by identification errors (57.4%), and fragile and polymedicated patients (44.4%). An analysis of the AI capabilities to prevent the root causes of ADEs showed that identification and reading are two potentially useful capabilities. Conclusion Identification error is one of the main root causes of drug adverse events and AI capabilities could potentially prevent drug adverse events. Impact This study highlights the role of AI capabilities in safely identifying both patients and drugs, which is a crucial part of the medication administration process, and how this can prevent ADEs in hospitals. |
| Databáze: | OpenAIRE |
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