Cargando…
Applicability of drug-related problem (DRP) classification system for classifying severe medication errors
BACKGROUND: Several classification systems for medication errors (MEs) have been established over time, but none of them apply optimally for classifying severe MEs. In severe MEs, recognizing the causes of the error is essential for error prevention and risk management. Therefore, this study focuses...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10334531/ https://www.ncbi.nlm.nih.gov/pubmed/37430249 http://dx.doi.org/10.1186/s12913-023-09763-3 |
_version_ | 1785070876130017280 |
---|---|
author | Linden-Lahti, Carita Takala, Anna Holmström, Anna-Riia Airaksinen, Marja |
author_facet | Linden-Lahti, Carita Takala, Anna Holmström, Anna-Riia Airaksinen, Marja |
author_sort | Linden-Lahti, Carita |
collection | PubMed |
description | BACKGROUND: Several classification systems for medication errors (MEs) have been established over time, but none of them apply optimally for classifying severe MEs. In severe MEs, recognizing the causes of the error is essential for error prevention and risk management. Therefore, this study focuses on exploring the applicability of a cause-based DRP classification system for classifying severe MEs and their causes. METHODS: This was a retrospective document analysis study on medication-related complaints and authoritative statements investigated by the Finnish National Supervisory Authority for Welfare and Health (Valvira) in 2013–2017. The data was classified by applying a previously developed aggregated DRP classification system by Basger et al. Error setting and harm to the patient were identified using qualitative content analysis to describe the characteristics of the MEs in the data. The systems approach to human error, error prevention, and risk management was used as a theoretical framework. RESULTS: Fifty-eight of the complaints and authoritative statements concerned MEs, which had occurred in a wide range of social and healthcare settings. More than half of the ME cases (52%, n = 30) had caused the patient’s death or severe harm. In total, 100 MEs were identified from the ME case reports. In 53% (n = 31) of the cases, more than one ME was identified, and the mean number of MEs identified was 1.7 per case. It was possible to classify all MEs according to aggregated DRP system, and only a small proportion (8%, n = 8) were classified in the category “Other,” indicating that the cause of the ME could not be classified to specific cause-based category. MEs in the “Other” category included dispensing errors, documenting errors, prescribing error, and a near miss. CONCLUSIONS: Our study provides promising preliminary results for using DRP classification system for classifying and analyzing especially severe MEs. With Basger et al.’s aggregated DRP classification system, we were able to categorize both the ME and its cause. More research is encouraged with other ME incident data from different reporting systems to confirm our results. |
format | Online Article Text |
id | pubmed-10334531 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103345312023-07-12 Applicability of drug-related problem (DRP) classification system for classifying severe medication errors Linden-Lahti, Carita Takala, Anna Holmström, Anna-Riia Airaksinen, Marja BMC Health Serv Res Research BACKGROUND: Several classification systems for medication errors (MEs) have been established over time, but none of them apply optimally for classifying severe MEs. In severe MEs, recognizing the causes of the error is essential for error prevention and risk management. Therefore, this study focuses on exploring the applicability of a cause-based DRP classification system for classifying severe MEs and their causes. METHODS: This was a retrospective document analysis study on medication-related complaints and authoritative statements investigated by the Finnish National Supervisory Authority for Welfare and Health (Valvira) in 2013–2017. The data was classified by applying a previously developed aggregated DRP classification system by Basger et al. Error setting and harm to the patient were identified using qualitative content analysis to describe the characteristics of the MEs in the data. The systems approach to human error, error prevention, and risk management was used as a theoretical framework. RESULTS: Fifty-eight of the complaints and authoritative statements concerned MEs, which had occurred in a wide range of social and healthcare settings. More than half of the ME cases (52%, n = 30) had caused the patient’s death or severe harm. In total, 100 MEs were identified from the ME case reports. In 53% (n = 31) of the cases, more than one ME was identified, and the mean number of MEs identified was 1.7 per case. It was possible to classify all MEs according to aggregated DRP system, and only a small proportion (8%, n = 8) were classified in the category “Other,” indicating that the cause of the ME could not be classified to specific cause-based category. MEs in the “Other” category included dispensing errors, documenting errors, prescribing error, and a near miss. CONCLUSIONS: Our study provides promising preliminary results for using DRP classification system for classifying and analyzing especially severe MEs. With Basger et al.’s aggregated DRP classification system, we were able to categorize both the ME and its cause. More research is encouraged with other ME incident data from different reporting systems to confirm our results. BioMed Central 2023-07-10 /pmc/articles/PMC10334531/ /pubmed/37430249 http://dx.doi.org/10.1186/s12913-023-09763-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Linden-Lahti, Carita Takala, Anna Holmström, Anna-Riia Airaksinen, Marja Applicability of drug-related problem (DRP) classification system for classifying severe medication errors |
title | Applicability of drug-related problem (DRP) classification system for classifying severe medication errors |
title_full | Applicability of drug-related problem (DRP) classification system for classifying severe medication errors |
title_fullStr | Applicability of drug-related problem (DRP) classification system for classifying severe medication errors |
title_full_unstemmed | Applicability of drug-related problem (DRP) classification system for classifying severe medication errors |
title_short | Applicability of drug-related problem (DRP) classification system for classifying severe medication errors |
title_sort | applicability of drug-related problem (drp) classification system for classifying severe medication errors |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10334531/ https://www.ncbi.nlm.nih.gov/pubmed/37430249 http://dx.doi.org/10.1186/s12913-023-09763-3 |
work_keys_str_mv | AT lindenlahticarita applicabilityofdrugrelatedproblemdrpclassificationsystemforclassifyingseveremedicationerrors AT takalaanna applicabilityofdrugrelatedproblemdrpclassificationsystemforclassifyingseveremedicationerrors AT holmstromannariia applicabilityofdrugrelatedproblemdrpclassificationsystemforclassifyingseveremedicationerrors AT airaksinenmarja applicabilityofdrugrelatedproblemdrpclassificationsystemforclassifyingseveremedicationerrors |