ArticleJournal of the American Medical Informatics Association : JAMIA2026
Fine-tuning and evaluating large language models for patient safety tasks: classification of contributing factors in incident reports.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveTo evaluate and compare the performance of large language models (LLMs) in identifying contributing factors (CFs) underlying patient safety incident investigations. MATERIALS AND
methodsFour open-source, lightweight LLMs, including BERT, LLaMA2, GPT2, and Phi-2 were applied to classify CFs across 6 sociotechnical system-levels encompassing 12 categories (eg, person, task, and organizational factors). Reports of real-world patient safety investigations from public health systems were extracted and labelled by domain experts (n_report/CFs = 300/1338). Data were split into training (n = 852), validation (n = 98), and test sets (n = 388). Performance was evaluated using specificity, precision, recall, and F1 scores.
resultsThe fine-tuned encoder-based BERT model achieved the highest performance, with a micro-averaged F1 score of 63.6%, outperforming all decoder-based models. Among the decoder models, Phi-2 demonstrated the strongest performance (F1 = 54.9%), exceeding both LLaMA2 and GPT2. BERT performed consistently across 6 system-levels but often misclassified "organization" as "person". DISCUSSION: LLMs hold promise for automating the extraction of CFs from complex safety narratives, particularly for frequently reported system-levels such as "person" and "tasks". Such automation may substantially reduce the manual effort required to analyse reports of patient safety investigations while supporting more consistent analysis across large incident datasets.
conclusionApplying LLMs to analyse the underlying causes of patient safety incidents depends on developing high-quality, domain-specific datasets that enhance the representation of patient safety knowledge and improve model understanding of incident causation. Improving data coverage for rare system-levels is essential to address the current limitations of LLMs in capturing nuanced patient safety concepts and domain-specific reasoning.
Indexed as
Identifiers
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.