Prevalence and Risk Factors for Dangerous Abbreviations in Malaysian Electronic Clinical Notes

Author:

Mohd Sulaiman Ismat1ORCID,Bulgiba Awang2,Abdul Kareem Sameem3

Affiliation:

1. Health Informatics Centre, Planning Division, Ministry of Health Malaysia, Malaysia

2. Centre for Epidemiology and Evidence-based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Malaysia

3. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia

Abstract

Medical abbreviations can be misinterpreted and endanger patients’ lives. This research is the first to investigate the prevalence of abbreviations in Malaysian electronic discharge summaries, where English is widely used, and elicit the risk factors associated with dangerous abbreviations. We randomly sampled and manually annotated 1102 electronic discharge summaries for abbreviations and their senses. Three medical doctors assigned a danger level to ambiguous abbreviations based on their potential to cause patient harm if misinterpreted. The predictors for dangerous abbreviations were determined using binary logistic regression. Abbreviations accounted for 19% (33,824) of total words; 22.6% (7640) of those abbreviations were ambiguous; and 52.3% (115) of the ambiguous abbreviations were labelled dangerous. Increased risk of danger occurs when abbreviations have more than two senses (OR = 2.991; 95% CI 1.586, 5.641), they are medication-related (OR = 6.240; 95% CI 2.674, 14.558), they are disorders (OR = 7.771; 95% CI 2.054, 29.409) and procedures (OR = 3.492; 95% CI 1.376, 8.860). Reduced risk of danger occurs when abbreviations are confined to a single discipline (OR = 0.519; 95% CI 0.278, 0.967). Managing abbreviations through awareness and implementing automated abbreviation detection and expansion would improve the quality of clinical documentation, patient safety, and the information extracted for secondary purposes.

Publisher

SAGE Publications

Subject

Health Policy

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ChatGPT's performance before and after teaching in mass casualty incident triage;Scientific Reports;2023-11-21

2. Analysis of Kazakh Language Abbreviations Based on Machine Learning Approach;2023 8th International Conference on Computer Science and Engineering (UBMK);2023-09-13

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