Background: The etiology and risk factors for progression of many chronic pain conditions (CPC) are still unknown. Moreover, real-time patient`s monitoring remains a challenge.
Aim: To develop a new digital tool for monitoring and analyzing big data of CPC patients in order to discover predictive patterns for :exacerbation ,remission, and response to treatment.
Methods: We developed the Painscreen, a new web application that works on mobile and stationary devices, in order to collect data and monitor patients continuously.
Results: The framework creates and stores answers to baseline and daily questionnaires and sensor data (such as :sleep patterns, steps) .Painscreen supports application, to the integrated longitudinal data, of big temporal data mining methods to discover predictive patterns, and present analysis results to the doctor.
Conclusions: The painscreen will lead to a more comprehensive understanding of CPC from a big data base, and will lead to better personalized care of CPC patients.