Using machine learning and clinical registry data to uncover variation in clinical decision making

Charlotte James*, Michael Allen, Martin James, Richard Everson

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

Abstract

Clinical registry data contains a wealth of information on patients, clinical practice, outcomes and interventions. Machine learning algorithms are able to learn complex patterns from data. We present methods for using machine learning with clinical registry data for quality improvement by identifying where variation in decision making occurs. Using a registry of stroke patients, we demonstrate how machine learning can be used to: investigate whether patients would have been treated differently had they attended a different hospital; group hospitals according to clinical decision making practice; identify where there is variation in decision making between hospitals; characterise patients that hospitals find it hard to agree on how to treat. Our methods should be applicable to any clinical registry and any machine learning algorithm to investigate the extent to which clinical practice is standardized and identify areas for improvement at a hospital level.

Original languageEnglish
Article number100098
JournalIntelligence-Based Medicine
Volume7
Early online date25 Apr 2023
DOIs
Publication statusPublished - 13 May 2023

Bibliographical note

Publisher Copyright:
© 2023

Keywords

  • Audit
  • Classification
  • Clinical registry
  • Machine learning
  • Stroke

Fingerprint

Dive into the research topics of 'Using machine learning and clinical registry data to uncover variation in clinical decision making'. Together they form a unique fingerprint.

Cite this