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Events related to medication errors and related factors involving nurses’ behavior to reduce medication errors in Japan: a Bayesian network modeling-based factor analysis and scenario analysis
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Naotaka Sugimura
, Katsuhiko Ogasawara - J Educ Eval Health Prof. 2024;21:12. Published online June 11, 2024
- DOI: https://doi.org/10.3352/jeehp.2024.21.12
- 2,098 View
- 272 Download
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Abstract
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Supplementary Material - Purpose
This study aimed to identify the relationships between medication errors and the factors affecting nurses’ knowledge and behavior in Japan using Bayesian network modeling. It also aimed to identify important factors through scenario analysis with consideration of nursing students’ and nurses’ education regarding patient safety and medications.
Methods
We used mixed methods. First, error events related to medications and related factors were qualitatively extracted from 119 actual incident reports in 2022 from the database of the Japan Council for Quality Health Care. These events and factors were then quantitatively evaluated in a flow model using Bayesian network, and a scenario analysis was conducted to estimate the posterior probabilities of events when the prior probabilities of some factors were 0%.
Results
There were 10 types of events related to medication errors. A 5-layer flow model was created using Bayesian network analysis. The scenario analysis revealed that “failure to confirm the 5 rights,” “unfamiliarity with operations of medications,” “insufficient knowledge of medications,” and “assumptions and forgetfulness” were factors that were significantly associated with the occurrence of medical errors.
Conclusion
This study provided an estimate of the effects of mitigating nurses’ behavioral factors that trigger medication errors. The flow model itself can also be used as an educational tool to reflect on behavior when incidents occur. It is expected that patient safety education will be recognized as a major element of nursing education worldwide and that an integrated curriculum will be developed.
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ChatGPT (GPT-4) passed the Japanese National License Examination for Pharmacists in 2022, answering all items including those with diagrams: a descriptive study
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Hiroyasu Sato, Katsuhiko Ogasawara
- J Educ Eval Health Prof. 2024;21:4. Published online February 28, 2024
- DOI: https://doi.org/10.3352/jeehp.2024.21.4
- 3,334 View
- 291 Download
- 5 Web of Science
- 8 Crossref
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Abstract
PDFSupplementary Material
- Purpose
The objective of this study was to assess the performance of ChatGPT (GPT-4) on all items, including those with diagrams, in the Japanese National License Examination for Pharmacists (JNLEP) and compare it with the previous GPT-3.5 model’s performance.
Methods
The 107th JNLEP, conducted in 2022, with 344 items input into the GPT-4 model, was targeted for this study. Separately, 284 items, excluding those with diagrams, were entered into the GPT-3.5 model. The answers were categorized and analyzed to determine accuracy rates based on categories, subjects, and presence or absence of diagrams. The accuracy rates were compared to the main passing criteria (overall accuracy rate ≥62.9%).
Results
The overall accuracy rate for all items in the 107th JNLEP in GPT-4 was 72.5%, successfully meeting all the passing criteria. For the set of items without diagrams, the accuracy rate was 80.0%, which was significantly higher than that of the GPT-3.5 model (43.5%). The GPT-4 model demonstrated an accuracy rate of 36.1% for items that included diagrams.
Conclusion
Advancements that allow GPT-4 to process images have made it possible for LLMs to answer all items in medical-related license examinations. This study’s findings confirm that ChatGPT (GPT-4) possesses sufficient knowledge to meet the passing criteria. -
Citations
Citations to this article as recorded by
- Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study
Shiben Zhu, Wanqin Hu, Zhi Yang, Jiani Yan, Fang Zhang
JMIR Medical Informatics.2025; 13: e63731. CrossRef - ChatGPT (GPT-4V) Performance on the Healthcare Information Technologist Examination in Japan
Kai Ishida, Eisuke Hanada
Cureus.2025;[Epub] CrossRef - Medication counseling for OTC drugs using customized ChatGPT-4: Comparison with ChatGPT-3.5 and ChatGPT-4o
Keisuke Kiyomiya, Tohru Aomori, Hisakazu Ohtani
DIGITAL HEALTH.2025;[Epub] CrossRef - Potential of ChatGPT to Pass the Japanese Medical and Healthcare Professional National Licenses: A Literature Review
Kai Ishida, Eisuke Hanada
Cureus.2024;[Epub] CrossRef - Performance of Generative Pre-trained Transformer (GPT)-4 and Gemini Advanced on the First-Class Radiation Protection Supervisor Examination in Japan
Hiroki Goto, Yoshioki Shiraishi, Seiji Okada
Cureus.2024;[Epub] CrossRef - Performance of ChatGPT‐3.5 and ChatGPT‐4o in the Japanese National Dental Examination
Osamu Uehara, Tetsuro Morikawa, Fumiya Harada, Nodoka Sugiyama, Yuko Matsuki, Daichi Hiraki, Hinako Sakurai, Takashi Kado, Koki Yoshida, Yukie Murata, Hirofumi Matsuoka, Toshiyuki Nagasawa, Yasushi Furuichi, Yoshihiro Abiko, Hiroko Miura
Journal of Dental Education.2024;[Epub] CrossRef - An exploratory assessment of GPT-4o and GPT-4 performance on the Japanese National Dental Examination
Masaki Morishita, Hikaru Fukuda, Shino Yamaguchi, Kosuke Muraoka, Taiji Nakamura, Masanari Hayashi, Izumi Yoshioka, Kentaro Ono, Shuji Awano
The Saudi Dental Journal.2024; 36(12): 1577. CrossRef - Evaluating the Accuracy of ChatGPT in the Japanese Board-Certified Physiatrist Examination
Yuki Kato, Kenta Ushida, Ryo Momosaki
Cureus.2024;[Epub] CrossRef
- Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study
Research Article
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Changes in flow experience among occupational therapy students: a 1-year longitudinal study
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Kazuki Hirao
- J Educ Eval Health Prof. 2014;11:14. Published online July 7, 2014
- DOI: https://doi.org/10.3352/jeehp.2014.11.14
- 23,962 View
- 148 Download
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Abstract
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- Purpose
The purpose of this 1-year longitudinal study is to investigate the change in flow experience among occupational therapy students (OTS). Methods: In December 2012, we prospectively recruited 97 OTS from the Department of Occupational Therapy, Kibi International University. To assess flow experience in daily life, we used the Flow Experience Checklist. Results: The dataset included 87 OTS, of which 75 participated in the assessment at 1 year (follow-up rate, 86.2%). The mean age at baseline of 45 male and 30 female OTS was 19.59±1.1 years (range, 18 to 24 years). A comparison of the ‘frequency of flow experience’ showed significant differences between baseline values and those after 1 year (December 2013) among male OTS (P<0.05). Conclusion: The present results indicate that, for male OTS, the frequency of flow was significantly reduced after 1 year compared with baseline values. This finding suggests the need for further education to increase the frequency of flow among male OTS.
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