Avoiding alert fatigue in pulmonary embolism decision support: a new method to examine ‘trigger rates’

Developed 'sensitivity and specificity trigger analysis' (SSTA) to enable programmers to analyze optimal trigger rates and limit inaccurate triggering of a CDS prior to its implementation into the EHR. Found that the most sensitive way to trigger the PE CDS while maintaining high specificity was to combine 1 or more pertinent symptoms with 1 or more Wells criteria.

  • Authors
  • Andy Schachter
  • Anne Press
  • Lauren McCullagh
  • Nina Kohn
  • Salvatore Pardo
  • Sundas Khan
  • Thomas McGinn
  • Published
  • Evidence-Based Medicine

Abstract

A clinical decision support system (CDSS) is integrated into the electronic health record (EHR) and allows physicians to easily use a clinical decision support (CDS) tool. However, often CDSSs are integrated into the EHR with poor adoption rates. One reason for this is secondary to ‘trigger fatigue’. Therefore, we developed a new and innovative usability process named ‘sensitivity and specificity trigger analysis’ (SSTA) as part of our larger project around a pulmonary embolism decision support tool. SSTA will enable programmers to examine optimal trigger rates prior to the integration of a CDS tool into the EHR, by using a formal method of analysis. We performed a retrospective chart review. The outcome of interest was physician ordering of a CT angiography (CTA). Phrases that signify common symptoms associated with pulmonary embolism were assessed as possible triggers for the CDSS tool. We then analysed each trigger’s ability to predict physician ordering of a CTA. We found that the most sensitive way to trigger the Pulmonary Embolism CDS tool while still maintaining a high specificity was by combining 1 or more pertinent symptoms with 1 or more elements of the Wells criteria. This study explored a unique methodology, SSTA, used to limit inaccurate triggering of a CDS tool prior to integration into the EHR. This methodology can be applied to other studies aiming to decrease triggering rates and increase adoption rates of previously validated CDSS tools.

  • Focus
  • Clinical Decision Support​
  • Evidence Based Medicine
  • User Experience​