Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F18%3A00069250" target="_blank" >RIV/65269705:_____/18:00069250 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/18:00105777

Result on the web

<a href="http://dx.doi.org/10.1016/j.jelectrocard.2018.08.007" target="_blank" >http://dx.doi.org/10.1016/j.jelectrocard.2018.08.007</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jelectrocard.2018.08.007" target="_blank" >10.1016/j.jelectrocard.2018.08.007</a>

Result language

angličtina

Original language name

Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms

Original language description

Introduction: Interpretation of the 12-lead Electrocardiogram (ECG) is normally assisted with an automated diagnosis (AD), which can facilitate an &apos;automation bias&apos; where interpreters can be anchored. In this paper, we studied, 1) the effect of an incorrect AD on interpretation accuracy and interpreter confidence (a proxy for uncertainty), and 2) whether confidence and other interpreter features can predict interpretation accuracy using machine learning. Methods: This study analysed 9000 ECG interpretations from cardiology and non-cardiology fellows (CFs and non-CFs). One third of the ECGs involved no ADs, one third with ADs (half as incorrect) and one third had multiple ADs. Interpretations were scored and interpreter confidence was recorded for each interpretation and subsequently standardised using sigma scaling. Spearman coefficients were used for correlation analysis and C5.0 decision trees were used for predicting interpretation accuracy using basic interpreter features such as confidence, age, experience and designation. Results: Interpretation accuracies achieved by CFs and non-CFs dropped by 43.20% and 58.95% respectively when an incorrect AD was presented (p &lt; 0.001). Overall correlation between scaled confidence and interpretation accuracy was higher amongst CFs. However, correlation between confidence and interpretation accuracy decreased for both groups when an incorrect AD was presented. We found that an incorrect AD disturbs the reliability of interpreter confidence in predicting accuracy. An incorrect AD has a greater effect on the confidence of non-CFs (although this is not statistically significant it is close to the threshold, p = 0.065). The best C5.0 decision tree achieved an accuracy rate of 64.67% (p &lt; 0.001), however this is only 6.56% greater than the no information-rate. Conclusion: Incorrect ADs reduce the interpreter&apos;s diagnostic accuracy indicating an automation bias. Non-CFs tend to agree more with the ADs in comparison to CFs, hence less expert physicians are more effected by automation bias. Incorrect ADs reduce the interpreter&apos;s confidence and also reduces the predictive power of confidence for predicting accuracy (even more so for non-CFs). Whilst a statistically significant model was developed, it is difficult to predict interpretation accuracy using machine learning on basic features such as interpreter confidence, age, reader experience and designation.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30201 - Cardiac and Cardiovascular systems

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Electrocardiology

ISSN

0022-0736

e-ISSN

—

Volume of the periodical

51

Issue of the periodical within the volume

6

Country of publishing house

US - UNITED STATES

Number of pages

6

Pages from-to

"S6"-"S11"

UT code for WoS article

000454674000002

EID of the result in the Scopus database

2-s2.0-85051492534