Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation

Aaron Peace; Salah Al-Zaiti; D Finlay; V. E. McGilligan; RR Bond

doi:10.1016/j.jelectrocard.2022.07.002

Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation

Aaron Peace, Salah Al-Zaiti, D Finlay, V. E. McGilligan, RR Bond

University of Pittsburgh

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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Abstract

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this ‘noise’ and improve the quality of ECG based decision making.

Original language	English
Pages (from-to)	157-161
Number of pages	5
Journal	Journal of Electrocardiology
Volume	73
Early online date	10 Jul 2022
DOIs	https://doi.org/10.1016/j.jelectrocard.2022.07.002
Publication status	Published (in print/issue) - 31 Aug 2022

Bibliographical note

Funding Information:
Aaron Peace, Raymond Bond and Victoria McGilligan are supported by the European Union 's INTERREG VA programme (managed by Special EU Programmes Body - SEUPB) under the project: ‘Centre for Personalised Medicine—Clinical Decision Making and Patient Safety’. The views and opinions in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).

Funding Information:
Aaron Peace, Raymond Bond and Victoria McGilligan are supported by the European Union's INTERREG VA programme (managed by Special EU Programmes Body - SEUPB) under the project: ‘Centre for Personalised Medicine—Clinical Decision Making and Patient Safety’. The views and opinions in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).

Publisher Copyright:
© 2022

Funding

Funding Information: Aaron Peace, Raymond Bond and Victoria McGilligan are supported by the European Union 's INTERREG VA programme (managed by Special EU Programmes Body - SEUPB) under the project: ‘Centre for Personalised Medicine—Clinical Decision Making and Patient Safety’. The views and opinions in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB). Funding Information: Aaron Peace, Raymond Bond and Victoria McGilligan are supported by the European Union's INTERREG VA programme (managed by Special EU Programmes Body - SEUPB) under the project: ‘Centre for Personalised Medicine—Clinical Decision Making and Patient Safety’. The views and opinions in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB). Publisher Copyright: © 2022

Keywords

Noise
decision making
ECG
STEMI
MI
Cath Lab
Decision making
Human factors
Cath lab
Coronary occlusion

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jelectrocard.2022.07.002

paperAuthor Accepted Manuscript -, 385 KBLicence: CC BY-NC-ND

Cite this

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abstract = "In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this {\textquoteleft}noise{\textquoteright} and improve the quality of ECG based decision making. ",

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author = "Aaron Peace and Salah Al-Zaiti and D Finlay and McGilligan, \{V. E.\} and RR Bond",

note = "Funding Information: Aaron Peace, Raymond Bond and Victoria McGilligan are supported by the European Union 's INTERREG VA programme (managed by Special EU Programmes Body - SEUPB) under the project: {\textquoteleft}Centre for Personalised Medicine—Clinical Decision Making and Patient Safety{\textquoteright}. The views and opinions in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB). Funding Information: Aaron Peace, Raymond Bond and Victoria McGilligan are supported by the European Union's INTERREG VA programme (managed by Special EU Programmes Body - SEUPB) under the project: {\textquoteleft}Centre for Personalised Medicine—Clinical Decision Making and Patient Safety{\textquoteright}. The views and opinions in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB). Publisher Copyright: {\textcopyright} 2022",

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Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

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