Evaluating Cognitive Bias from Diagnostic Error Cases Using the Cognitive Bias Codex
-
- Miyagami Taiju
- Department of General Medicine, Juntendo University Faculty of Medicine
-
- Harada Taku
- Division of General Medicine, Showa University Koto Toyosu Hospital Department of Diagnostic and Generalist Medicine, Dokkyo Medical University Hospital
-
- Watari Takashi
- Postgraduate Clinical Training Center, Shimane University Hospital
-
- Shimizu Taro
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University Hospital
この論文をさがす
説明
[Introduction]Cognitive biases are difficult to evaluate because there are so many that exist. [Purpose] This study evaluated the efficacy of the “Cognitive Bias Codex” for assessing diagnostic errors. [Methods]This was a cross-sectional observational study. Questionnaires were distributed to diagnostic error presenters at the meeting of the Japanese Society of Hospital General Medicine on September 14 and 15, 2019. Then, cases were divided into four groups:infection, vascular disease, tumor, and others. Cognitive biases were divided into four categories, including “too much information,” “not enough meaning,” “need to act fast,” and “what should we remember?”. [Results]Twenty-one respondents met the inclusion criteria. (Inclusion rate was 87.5%). “Need to act fast” was the most common affecting factor, while “too much information” was the least. Vascular disease had a different trend than infection and tumor. “What should we remember?” was much more common in the vascular group. [Discussion]The difference in trend may be because patients with vascular issues are more likely to require urgent attention after sudden onset. Future research into this may lead to emergency department-specific de-biasing if results are similar. [Conclusions]The “Cognitive Bias Codex” can be used to effectively assess biases according to the given disease category.
収録刊行物
-
- JOURNAL OF HOSPITAL GENERAL MEDICINE
-
JOURNAL OF HOSPITAL GENERAL MEDICINE 3 (3), 79-83, 2021-05-31
一般社団法人 日本病院総合診療医学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390580018950678528
-
- ISSN
- 2436018X
-
- 本文言語コード
- en
-
- データソース種別
-
- JaLC
-
- 抄録ライセンスフラグ
- 使用不可