ArticleJMIR formative research2026
AI-Assisted Clinical Data Abstraction From Electronic Health Records: Retrospective Concordance Study.
Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
6 authors.
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Abstract
Background: Manual chart abstraction from electronic health records is a critical step in clinical outcomes research but is time-intensive and prone to human error. Advances in artificial intelligence (AI), particularly large language models, offer the potential to automate the extraction of structured data from unstructured clinical documentation with improved efficiency and consistency. Objective: This study aimed to evaluate the accuracy and efficiency of an AI-assisted approach for extracting patient-reported outcomes from clinical notes compared with traditional human abstraction. Methods: We conducted a retrospective study of 26 patients treated with low-dose radiation therapy for osteoarthritis. Human reviewers abstracted numeric rating scale (NRS; 0-10) pain scores at baseline, the end of treatment, and the first follow-up, and von Pannewitz score (VPS; 0-4) improvement scores at posttreatment time points. A HIPAA (Health Insurance Portability and Accountability Act)-compliant generative pretrained transformer-based AI system was prompted to extract the same end points from clinical notes. Concordance was assessed using exact match rates, the intraclass correlation coefficient for the NRS, and weighted Cohen κ for the VPS. The time required for AI vs manual abstraction was recorded. The AI system was not trained or fine-tuned on study data, and performance was evaluated directly against human abstraction to reflect real-world deployment. Results: The AI system demonstrated high concordance with human abstraction, achieving an exact match rate of 92% for the NRS (95% CI 84-96; intraclass correlation coefficient=0.96) and 94% for the VPS (95% CI 84-98; κ=0.91). All discrepancies were minor, and no spurious values were generated. The AI system identified 1 clinically relevant data point missed during manual review. Average abstraction time per patient decreased from approximately 30 minutes to 2 minutes, representing time savings of >90%. The system also captured trends in analgesic use, but these results were not statistically significant, including reductions without escalation. Conclusions: AI-assisted data abstraction demonstrated high concordance with human review in this single-institution cohort while substantially reducing the time requirements. These findings support the feasibility of AI-assisted abstraction workflows, although further validation across larger and more diverse datasets is needed.
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