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...

Descripción completa

Detalles Bibliográficos
Autores principales: Linden-Lahti, Carita, Takala, Anna, Holmström, Anna-Riia, Airaksinen, Marja
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