A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogram

The 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation...

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Vydáno v:Journal of electrocardiology Ročník 50; číslo 6; s. 781 - 786
Hlavní autoři: Cairns, Andrew W., Bond, Raymond R., Finlay, Dewar D., Guldenring, Daniel, Badilini, Fabio, Libretti, Guido, Peace, Aaron J., Leslie, Stephen J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Inc 01.11.2017
Elsevier Science Ltd
Témata:
12-lead Electrocardiogram
Algorithm
Algorithms
Clinical Competence
Decision support
Decision Support Systems, Clinical
Diagnoses
Diagnosis, Differential
Diagnostic Errors - prevention & control
ECG criteria
Electrocardiography
Electronic health records
Humans
Interpretation
Man-Machine Systems
Medical diagnosis
Software
ECG criteria
Interpretation
12-lead Electrocardiogram
Algorithm
Decision support
Diagnoses
ISSN:0022-0736, 1532-8430, 1532-8430
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Shrnutí:The 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named ‘Interactive Progressive based Interpretation’ (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreter's annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation. To improve interpretation accuracy and reduce missed co-abnormalities. The Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI+DDA approach. A total of 375 interpretations were collected. The IPI+DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value=0.1852), the IPI+DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated. Although results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerised diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size.
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ISSN:0022-0736
1532-8430
1532-8430
DOI:10.1016/j.jelectrocard.2017.08.007