Annotation Guidelines for Medication Errors in Incident Reports: Validation Through a Mixed Methods Approach.


Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
06 Jun 2022
Historique:
entrez: 8 6 2022
pubmed: 9 6 2022
medline: 10 6 2022
Statut: ppublish

Résumé

At present no adequate annotation guidelines exists for incident report learning. This study aims at utilizing multiple quantitative and qualitative evidence to validate annotation guidelines for incident reporting of medication errors. Through multiple approaches via annotator training, annotation performance evaluation, exit surveys, and user and expert interviews, a mixed methods explanatory sequential design was utilized to collect 2-stage evidence for validation. We recruited two patient safety experts to participate in piloting, three annotators to receive annotation training and provide user feedback, and two incident report system designers to offer expert comments. Regarding the annotation performance evaluation, the overall accuracy reached 97% and 90% for named entity identification and attribute identification respectively. Participants provided invaluable comments and opinions towards improving the annotation methods. The mixed methods approach created a significant evidential basis for the use of annotation guidelines for incident report of medication errors. Further expansion of the guidelines and external validity present options for future research.

Identifiants

pubmed: 35673034
pii: SHTI220095
doi: 10.3233/SHTI220095
doi:

Types de publication

Journal Article

Langues

eng

Pagination

354-358

Auteurs

Zoie S Y Wong (ZSY)

Graduate School of Public Health, St. Luke's International University, Tokyo, Japan.

Yuchen Qiao (Y)

The University of Tokyo, Tokyo, Japan.

Ryohei Sasano (R)

Nagoya University, Nagoya, Japan.

Hongkuan Zhang (H)

Nagoya University, Nagoya, Japan.

Kenichiro Taneda (K)

National Institute of Public Health, Saitama, Japan.

Shin Ushiro (S)

Kyushu University, Fukuoka, Japan.

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Classifications MeSH