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Machine learning in clinical practice: Evaluation of an artificial intelligence tool after implementation.

Hamed Akhlaghi1,2,3, Sam Freeman1,4, Cynthia Vari1

  • 1Department of Emergency Medicine, St Vincent's Hospital Melbourne, Melbourne, Victoria, Australia.

Emergency Medicine Australasia : EMA
|September 29, 2023
PubMed
Summary

A novel artificial intelligence (AI) algorithm for predicting hospital admission from triage notes showed acceptable real-time performance in the emergency department (ED). However, its accuracy decreased post-implementation, highlighting the need for continuous AI training and evaluation in healthcare.

Keywords:
artificial intelligenceemergency departmentmachine learningresearch translationtriage note

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Area of Science:

  • Healthcare Technology
  • Clinical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) is increasingly integrated into healthcare.
  • Evaluating the real-time performance of AI in clinical settings is crucial.

Purpose of the Study:

  • To assess the accuracy of a novel AI algorithm predicting hospital admission from emergency department (ED) triage notes after clinical implementation.
  • This study is the first to investigate real-time AI performance in an emergency setting.

Main Methods:

  • The AI algorithm was integrated into St Vincent's Hospital Melbourne's electronic emergency patient management system.
  • Data from 77,125 ED presentations between January 1, 2021, and August 17, 2022, were analyzed.
  • Diagnostic accuracy metrics including sensitivity, specificity, PPV, and NPV were evaluated.

Main Results:

  • The live AI algorithm achieved an overall accuracy of 74%, with sensitivity of 73.1% and specificity of 74.3%.
  • Accuracy varied by admission type, lowest for psychiatric units (34%) and highest for gastroenterology (84%) and medical (80%) admissions.
  • Positive predictive value was 50% and negative predictive value was 88.7%.

Conclusions:

  • The real-time AI clinical decision-support tool demonstrated reduced accuracy compared to its original evaluation.
  • While sensitivity and specificity were acceptable for decision support in the ED, continuous training and evaluation are necessary.
  • Ongoing monitoring is essential to ensure consistent AI performance and prevent adverse outcomes in healthcare.