A Computer-Human Interaction Model to Improve the Diagnostic Accuracy and Clinical Decision-Making during 12-lead Electrocardiogram Interpretation

Andrew Cairns, Raymond R Bond, Dewar Finlay, Cathal Breen, Daniel Guldenring, Robert Gaffney, Anthony Gallagher, Aaron Peace, Pat Henn

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Introduction: The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. Methods: An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model ‘Interactive Progressive based Interpretation’ (IPI) as the user cannot ‘progress’ unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). Results: For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI =42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD = 42.4%; CI = 49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI = 4.45, 22.35) An N-1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort.Conclusions: We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.
LanguageEnglish
Pages93-107
JournalJournal of Biomedical Informatics
Volume64
Early online date27 Sep 2016
DOIs
Publication statusPublished - Dec 2016

Fingerprint

Human computer interaction
Electrocardiography
Lead
Decision making
Clinical Decision-Making
Chi-Square Distribution
Graphical user interfaces
Workload
Diagnostic Errors
Routine Diagnostic Tests
User interfaces
Interfaces (computer)
Decision Making
Thorax
Extremities

Keywords

  • Clinical decision making
  • medical informatics
  • decision support
  • human-machine systems
  • electrocardiogram
  • cardiology

Cite this

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title = "A Computer-Human Interaction Model to Improve the Diagnostic Accuracy and Clinical Decision-Making during 12-lead Electrocardiogram Interpretation",
abstract = "Introduction: The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. Methods: An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model ‘Interactive Progressive based Interpretation’ (IPI) as the user cannot ‘progress’ unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). Results: For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45{\%}; SD=18.1{\%}; CI =42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85{\%} (SD = 42.4{\%}; CI = 49.12, 68.58), which indicates a positive mean difference of 13.4{\%}. (CI = 4.45, 22.35) An N-1 Chi-square test of independence indicated a 92{\%} chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort.Conclusions: We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.",
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A Computer-Human Interaction Model to Improve the Diagnostic Accuracy and Clinical Decision-Making during 12-lead Electrocardiogram Interpretation. / Cairns, Andrew; Bond, Raymond R; Finlay, Dewar; Breen, Cathal; Guldenring, Daniel; Gaffney, Robert; Gallagher, Anthony; Peace, Aaron; Henn, Pat.

In: Journal of Biomedical Informatics, Vol. 64, 12.2016, p. 93-107.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Computer-Human Interaction Model to Improve the Diagnostic Accuracy and Clinical Decision-Making during 12-lead Electrocardiogram Interpretation

AU - Cairns, Andrew

AU - Bond, Raymond R

AU - Finlay, Dewar

AU - Breen, Cathal

AU - Guldenring, Daniel

AU - Gaffney, Robert

AU - Gallagher, Anthony

AU - Peace, Aaron

AU - Henn, Pat

N1 - Compliant in UIR; evidence uploaded in 'Other files'

PY - 2016/12

Y1 - 2016/12

N2 - Introduction: The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. Methods: An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model ‘Interactive Progressive based Interpretation’ (IPI) as the user cannot ‘progress’ unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). Results: For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI =42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD = 42.4%; CI = 49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI = 4.45, 22.35) An N-1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort.Conclusions: We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.

AB - Introduction: The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. Methods: An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model ‘Interactive Progressive based Interpretation’ (IPI) as the user cannot ‘progress’ unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). Results: For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI =42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD = 42.4%; CI = 49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI = 4.45, 22.35) An N-1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort.Conclusions: We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.

KW - Clinical decision making

KW - medical informatics

KW - decision support

KW - human-machine systems

KW - electrocardiogram

KW - cardiology

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DO - 10.1016/j.jbi.2016.09.016

M3 - Article

VL - 64

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JO - Journal of Biomedical Informatics

T2 - Journal of Biomedical Informatics

JF - Journal of Biomedical Informatics

SN - 1532-0464

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