ArticleNPJ digital medicine2026
Artificial intelligence-based incident analysis and learning system to enhance patient safety and improve treatment quality.
Article in NPJ digital medicine, 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
14 authors.
Funding
Abstract
Patient safety and high treatment quality are essential in modern healthcare, but analyzing safety incidents for insights is labor-intensive and inconsistent process. To address this, we developed the Artificial Intelligence-based Incident Analysis and Learning System (AI-ILS), trained on 1548 expertly curated incidents categorized by the Human Factors Analysis and Classification System (HFACS). AI-ILS identifies latent safety threats and classifies incident causes with high accuracy, achieving an average AUROC of 0.92, MCC of 0.72, and overall accuracy of 79%. In testing on 350 real-world clinical incidents, AI-ILS showed 88% concordance with expert reviewers and operated 29 times faster than manual analysis. We deployed and validated AI-ILS using real-world radiation oncology data, where it improved retrospective incident analysis at our institution by generating aggregated HFACS-based results and addressing challenges related to inconsistent review processes and lack of standardized taxonomies.
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.