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Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review.

Michela FerraraGiuseppe BertozziNicola Di FazioIsabella AquilaAldo Di FazioAniello MaieseGianpietro VolonninoPaola FratiRaffaele La Russa
Published in: Healthcare (Basel, Switzerland) (2024)
This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.
Keyphrases
  • patient safety
  • artificial intelligence
  • machine learning
  • quality improvement
  • randomized controlled trial
  • cardiovascular disease
  • type diabetes
  • electronic health record
  • induced pluripotent stem cells
  • drug induced