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Avoiding alert fatigue in pulmonary embolism decision support: a new method to examine ‘trigger rates’
  1. Anne Press1,
  2. Sundas Khan1,
  3. Lauren McCullagh1,
  4. Andy Schachter1,
  5. Salvatore Pardo2,
  6. Nina Kohn3,
  7. Thomas McGinn1
  1. 1Department of Medicine, Hofstra Northwell School of Medicine, Manhasset, New York, USA
  2. 2Department of Emergency Medicine, Hofstra Northwell School of Medicine, Manhasset, New York, USA
  3. 3Feinstein Institute for Medical Research, Manhasset, New York, USA
  1. Correspondence to Anne Press,
    Department of Medicine, Hofstra Northwell School of Medicine, 300 Community Drive, Manhasset, NY 11030, USA; anniepress{at}gmail.com

Extract

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.

Acknowledgments

The authors would like to thank the US Agency for Healthcare Research and Quality for providing us with a grant to conduct this research (1 R24 HS022061). The abstract from this work was presented as a poster at AcademyHealth Concordium 2015 on 21–22 September 2015 in Washington, DC.

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Extract

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.

Acknowledgments

The authors would like to thank the US Agency for Healthcare Research and Quality for providing us with a grant to conduct this research (1 R24 HS022061). The abstract from this work was presented as a poster at AcademyHealth Concordium 2015 on 21–22 September 2015 in Washington, DC.

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