Co-Design and Evaluation of an Intelligent Decision Support System for Stroke Rehabilitation Assessment

Decision support systems have a 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 perspective on assessment with quantitative information on patient's exercise motions). As a result, we developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning to assess the quality of motion and summarize patient-specific analysis. We evaluated this system with seven therapists using the dataset from 15 patients performing three exercises. The results show that our system is preferred over a traditional system without analysis while presenting more richer information ($p<0.1$) to significantly reduce effort on assessment ($p < 0.1$)} and increase the agreement on therapists' assessment from 0.6600 to 0.7108 F1-scores ($p < 0.01$). This work discusses the importance of human centered design and development of a decision support system that presents contextually relevant information and salient explanation on its prediction for better adoption in practice.