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Nursing records can improve pharmaceutical safety by identifying adverse effects (AEs) with AI. However, significant documentation gaps exist, necessitating standardized data collection for better patient safety.

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Area of Science:

  • Health Informatics
  • Patient Safety
  • Artificial Intelligence in Healthcare

Background:

  • Nursing records are a valuable data source for patient safety.
  • Improving pharmaceutical safety requires accurate adverse effect (AE) detection.
  • Current documentation practices present challenges in AE identification.

Purpose of the Study:

  • To explore the utility of nursing records for AE analysis using ChatGPT-4.
  • To identify documentation gaps in AE reporting within a Japanese hospital.
  • To analyze relationships between terms related to AEs and patient conditions.

Main Methods:

  • Analysis of 927 anonymized nursing records from a Japanese community hospital.
  • Utilized ChatGPT-4 for AE identification and data categorization.
  • Constructed a co-occurrence network to analyze term relationships and identify central patterns using network metrics.

Main Results:

  • High rates of "Unknown" classifications for AE status (674 cases) and patient conditions (865 cases) indicate significant documentation gaps.
  • Limited documentation of AEs related to medicines (6 cases) and devices (2 cases).
  • Co-occurrence network highlighted the importance of informed consent (IC) in communicating risks, especially for chemotherapy and antibiotics.

Conclusions:

  • Nursing records play a crucial role in AE management, despite current documentation limitations.
  • Standardized documentation and integration of nursing notes are essential for enhancing pharmaceutical safety.
  • Future research should focus on combining AI with human feedback for improved AE extraction from incomplete nursing notes.