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<p>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.</p>

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Artificial intelligence-based incident analysis and learning system to enhance patient safety and improve treatment quality

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月16日

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  • Abbas J. Jinia,
  • Katherine Chapman,
  • Shi Liu,
  • Cesar Della Biancia,
  • Elizabeth Hipp,
  • Eric Lin,
  • Robin Moulder,
  • Dhwani Parikh,
  • Jason Cordero,
  • Caralaina Pistone,
  • Mary Gil,
  • John Ford,
  • Anyi Li,
  • Jean M. Moran

摘要

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.