Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2020

Abstract

Clinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist's evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspectives with quantitative information on patient's exercise motions). As a result, we co-designed and developed an intelligent decision support system that automatically identifies salient features of assessment using reinforcement learning to assess the quality of motion and generate patient-specific analysis. We evaluated this system with seven therapists using the dataset from 15 patients performing three exercises. The results show that therapists have higher usage intent on our system than a traditional system without patient-specific analysis ($p < 0.05$). While presenting richer information ($p < 0.10$), our system significantly reduces therapists' effort on assessment ($p < 0.10$) and improves their agreement on assessment from 0.66 to 0.71 F1-scores ($p < 0.01$). This work discusses the importance of human centered design and development of a machine learning-based decision support system that presents contextually relevant information and salient explanations on its prediction for better adoption in practice.

Keywords

Human-AI Interaction, Explainable AI, Machine Learning, Decision Support Systems, Stroke Rehabilitation Assessment

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the ACM on Human-Computer Interaction

Volume

4

Issue

CSCW2

First Page

1

Last Page

27

ISSN

2573-0142

Identifier

10.1145/3415227

Publisher

Association for Computing Machinery (ACM)

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