Healthcare Informatics & Health Information Technology (SIG Health)
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Paper Type
Complete
Paper Number
1562
Description
Physical Therapy has long been a challenging area of medicine as it suffers many inefficiencies among which are: costly clinic visits, lack of motivation, and complex decision-making. Game-based physical therapy systems have arisen to transform the tedious physical therapy process into a more engaging and efficient one. With the rise of such systems, streams of data generated call for the integration of data analytics for proper utilization of this pool of data. The application of data analytics in such context promises much potential in leveraging the quality of physical therapy. In this paper, we propose a data analytics approach for game-based physical therapy systems, through which we utilize a variety of data mining techniques for extracting hidden patterns to support therapists in clinical decision-making. Our solution has been evaluated by several therapists. Evaluation results support the efficacy and utility of the system, and the reliability of the data analytics results.
Recommended Citation
Salama, Maha and Kassem, Gamal, "A Data Analytics Approach for Game-based Physical Therapy Systems" (2021). AMCIS 2021 Proceedings. 16.
https://aisel.aisnet.org/amcis2021/healthcare_it/sig_health/16
A Data Analytics Approach for Game-based Physical Therapy Systems
Physical Therapy has long been a challenging area of medicine as it suffers many inefficiencies among which are: costly clinic visits, lack of motivation, and complex decision-making. Game-based physical therapy systems have arisen to transform the tedious physical therapy process into a more engaging and efficient one. With the rise of such systems, streams of data generated call for the integration of data analytics for proper utilization of this pool of data. The application of data analytics in such context promises much potential in leveraging the quality of physical therapy. In this paper, we propose a data analytics approach for game-based physical therapy systems, through which we utilize a variety of data mining techniques for extracting hidden patterns to support therapists in clinical decision-making. Our solution has been evaluated by several therapists. Evaluation results support the efficacy and utility of the system, and the reliability of the data analytics results.
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