ArticleJournal of assisted reproduction and genetics2026
AI-assisted assessment of narrative documentation quality in ART consultations using a domain-based framework.
Article in Journal of assisted reproduction and genetics, 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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Abstract
purposeTo assess narrative clinical documentation quality in assisted reproductive technology (ART) consultations, by applying a transparent, reproducible AI-assisted scoring framework.
methodsIn this cross-sectional observational study, 90 consecutive initial ART consultation records from 9 clinicians were evaluated using a predefined, domain-based documentation scoring framework. Documentation quality was assessed across three domains: Regulatory Alignment, Clarity of Plan of Action, and Disclosure Readiness. A rule-constrained AI-assisted tool applied fixed scoring criteria. Domain scores were normalised, and mixed-effects models were used to examine domain differences, associations, and clinician-level effects. Human-AI concordance was assessed in a blinded random subset scored by an independent expert clinician. Repeatability was assessed through repeated rescoring under identical conditions.
resultsMean normalised documentation scores differed significantly across domains (Regulatory Alignment 0.830, Clarity of Plan of Action 0.677, Disclosure Readiness 0.753, Wald χ
conclusionsA constrained, domain-based AI scoring framework enables transparent and reproducible quantification of narrative documentation quality in reproductive medicine. This approach may help generate formative feedback and hypotheses for targeted documentation improvement, which should be evaluated against downstream outcomes in future work.
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