ArticleCureus2022
Extracting Safety-II Factors From an Incident Reporting System by Text Analysis.
Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Strategies and tools to learn from work that goes well within healthcare patient safety practices: a mixed methods systematic review.BMC health services research · 2025Pooled it
- Exploring the development of safety culture among physicians with text mining of patient safety reports: a retrospective study.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2025Article
- Seeking systems-based facilitators of safety and healthcare resilience: a thematic review of incident reports.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024Article
- Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction The use of electric health records (EHRs) has spread worldwide and has helped record huge amounts of data. However, despite accumulated data from EHRs, especially text data, the information has been underutilized. Our research questions and aims are as follows: How can an incident report system extract common themes behind incidents, good practices, improved quality, and safety based on the Safety-II/resilient healthcare approach? Methods We extracted data from the electronic incident reporting system of the Yokohama City University Medical Center between April 1, 2016 and March 31, 2018. We utilized natural language processing and text mining to extract concept categories and word patterns. We also used the incident levels as outcomes, as well as classification and regression tree analysis to obtain associated text combinations. Results A total of 17,231 cases were reported through the electronic incident reporting system in our hospital during the study period. Hospital staff has to be prepared for incidents with complex mechanisms in daily practice. The hospital staff tend to focus on individual actions rather than considering a systematic approach. Conclusion Certain combinations of professions and contents may contribute to resilient management. Studies on Safety-II management utilizing clinical information and text records are needed.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.