Imperial College London
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DrLukeMoore

Faculty of Medicine, Department of Infectious Disease

Honorary Clinical Senior Lecturer
 
 
 
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Contact

 

l.moore Website CV

 
 
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Location

 

Chelsea and Westminster HospitalChelsea and Westminster Campus

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Summary

 

Publications

Citation

BibTex format

@article{Abdulaal:2021:10.2196/preprints.27992,
author = {Abdulaal, A and Patel, A and Al-Hindawi, A and Charani, E and Alqahtani, SA and Davies, GW and Mughal, N and Moore, LSP},
doi = {10.2196/preprints.27992},
title = {Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis (Preprint)},
url = {http://dx.doi.org/10.2196/preprints.27992},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <sec>                    <title>BACKGROUND</title>                        <p>The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging artificial intelligence technology in the health care setting has been the relative inability to translate models into clinician workflow.</p>                </sec>                                <sec>                    <title>OBJECTIVE</title>                        <p>Here we demonstrate the development of a COVID-19 outcome prediction app that utilizes an ANN and assesses its usability in the clinical setting.</p>                </sec>                                <sec>                    <title>METHODS</title>                        <p>Usability assessment was conducted using the app, followed by a semistructured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analyzed using the thematic framework method, which allowed for the development of themes from the interview narratives. In total, 31 National Health Service physicians at a West London teaching hospital, including foundation physicians, senior house officers, registrars, and consultants, were included in this study.</p>                </sec>                                <sec>                    <title>RESULTS</title>                        <p>All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 (SD 10.35) seconds. The mean system usability scale score was 91.94 (SD 8.54), which corresponds to a qualitative rating of “excellent.” The clinicians found the app intuitive and easy to use, with the
AU  - Abdulaal,A
AU  - Patel,A
AU  - Al-Hindawi,A
AU  - Charani,E
AU  - Alqahtani,SA
AU  - Davies,GW
AU  - Mughal,N
AU  - Moore,LSP
DO  - 10.2196/preprints.27992
PY  - 2021///
TI  - Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis (Preprint)
UR  - http://dx.doi.org/10.2196/preprints.27992
ER  -
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