Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Remote patient monitoring (RPM) has been widely used for monitoring patients’ health and tracking their behavior outside the traditional healthcare setting. One important behavior to understand is patients’ compliance with medical advice and treatment regimes. Existing methods detect non-compliance based on health parameters i.e., weight and vital signs, which can only be identified by the deterioration in health conditions. This study proposes an RPM system artifact to record patients’ feelings and concerns through short messages; these messages are used to develop a non-compliance prediction model. A prototype of the design artifact was implemented and tested with chronic patients taking home hemodialysis. Our model revealed that the counts of messages recorded are related to non-compliance behavior, and the negative emotions depicted in the messages implied a higher likelihood of non-compliance. Our study demonstrated the feasibility of understanding patients’ status based on non-health parameters and provided a way to enhance RPM for patients outside the hospital settings.
Recommended Citation
Cai, Ye; Liu, Na; Huang, Robin; Sud, Kamal; and Kim, Jinman, "Predicting Remote Monitoring Patients’ Non-compliance Behavior Through App-mediated Communications" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 4.
https://aisel.aisnet.org/hicss-56/hc/beyond_hospital/4
Predicting Remote Monitoring Patients’ Non-compliance Behavior Through App-mediated Communications
Online
Remote patient monitoring (RPM) has been widely used for monitoring patients’ health and tracking their behavior outside the traditional healthcare setting. One important behavior to understand is patients’ compliance with medical advice and treatment regimes. Existing methods detect non-compliance based on health parameters i.e., weight and vital signs, which can only be identified by the deterioration in health conditions. This study proposes an RPM system artifact to record patients’ feelings and concerns through short messages; these messages are used to develop a non-compliance prediction model. A prototype of the design artifact was implemented and tested with chronic patients taking home hemodialysis. Our model revealed that the counts of messages recorded are related to non-compliance behavior, and the negative emotions depicted in the messages implied a higher likelihood of non-compliance. Our study demonstrated the feasibility of understanding patients’ status based on non-health parameters and provided a way to enhance RPM for patients outside the hospital settings.
https://aisel.aisnet.org/hicss-56/hc/beyond_hospital/4