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Impact <p><UnorderedList Mark="Bullet"> <ItemContent> <p>NLP applied to paediatric EHRs identified substantially more clinically documented bleeding events than ICD-10 coding.</p> </ItemContent> <ItemContent> <p>Demonstrates considerable loss of clinically assessed bleeding information when relying on administrative datasets alone.</p> </ItemContent> <ItemContent> <p>Shows that narrative-based ascertainment captures the full spectrum of documented bleeding events not transferred to structured coding.</p> </ItemContent> <ItemContent> <p>Provides a scalable method for documentation-based bleeding surveillance in hospitalised children.</p> </ItemContent> <ItemContent> <p>Supports use of NLP to improve outcome ascertainment in research, safety monitoring, and quality registries.</p> </ItemContent> </UnorderedList></p>

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Differences in bleeding outcome capture between electronic health record review using natural language processing and ICD-10 coding in hospitalised children

  • 期刊论文

  • 发表日期：

    2026年4月29日

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  • Signe H. Biørn,
  • Anne L. Lyster,
  • Rasmus S. Hansen,
  • Rasmus B. Lynggaard,
  • Martin S. Laursen,
  • Jannik S. Pedersen,
  • Anne B. Alnor,
  • Pernille J. Vinholt

摘要

Impact

NLP applied to paediatric EHRs identified substantially more clinically documented bleeding events than ICD-10 coding.

Demonstrates considerable loss of clinically assessed bleeding information when relying on administrative datasets alone.

Shows that narrative-based ascertainment captures the full spectrum of documented bleeding events not transferred to structured coding.

Provides a scalable method for documentation-based bleeding surveillance in hospitalised children.

Supports use of NLP to improve outcome ascertainment in research, safety monitoring, and quality registries.