A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study

Despite the unprecedented performance of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). Howeve...

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Bibliographic Details
Published in:Journal of medical Internet research Vol. 24; no. 1; p. e28659
Main Authors: Hwang, Jeonghwan, Lee, Taeheon, Lee, Honggu, Byun, Seonjeong
Format: Journal Article
Language:English
Published: Canada Journal of Medical Internet Research 19.01.2022
Gunther Eysenbach MD MPH, Associate Professor
JMIR Publications
Subjects:
Algorithms
Artificial Intelligence
Automation
Circadian rhythm
Clinical decision making
Data mining
Decision support systems
Decision Support Systems, Clinical
Deep learning
Electroencephalography
Grammatical aspect
Helping behavior
Human-computer interaction
Humans
Information
Intelligence
Interfaces
Interrater reliability
Interviews
Medical decision making
Original Paper
Predictions
Reliability
Reproducibility of Results
Sleep
Support networks
Technicians
User needs
User-Centered Design
Visualization
Work experience
medical artificial intelligence
user-centered design
sleep staging
clinical decision support
ISSN:1438-8871, 1439-4456, 1438-8871
Online Access:Get full text
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Summary:Despite the unprecedented performance of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces hinders the adoption of these AI systems in practice. This study aims to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered manner. Our study is based on a user-centered design framework for developing explanations in a CDSS that identifies why explanations are needed, what information should be contained in explanations, and how explanations can be provided in the CDSS. We conducted user interviews, user observation sessions, and an iterative design process to identify three key aspects for designing explanations in the CDSS. After constructing the CDSS, the tool was evaluated to investigate how the CDSS explanations helped technicians. We measured the accuracy of sleep staging and interrater reliability with macro-F1 and Cohen κ scores to assess quantitative improvements after our tool was adopted. We assessed qualitative improvements through participant interviews that established how participants perceived and used the tool. The user study revealed that technicians desire explanations that are relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of AI predictions. Here, technicians wanted explanations that could be used to evaluate whether the AI models properly locate and use these patterns during prediction. On the basis of this, information that is closely related to sleep EEG patterns was formulated for the AI models. In the iterative design phase, we developed a different visualization strategy for each pattern based on how technicians interpreted the EEG recordings with these patterns during their workflows. Our evaluation study on 9 polysomnographic technicians quantitatively and qualitatively investigated the helpfulness of the tool. For technicians with <5 years of work experience, their quantitative sleep staging performance improved significantly from 56.75 to 60.59 with a P value of .05. Qualitatively, participants reported that the information provided effectively supported them, and they could develop notable adoption strategies for the tool. Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.
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ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/28659