On the Design and Evaluation of Decision Support Platform for Prostate Cancer Disease

The study aims to design and evaluate the Decision Support Platform (DSP) for prostate cancer disease. The system was developed based on COX regression model for predicting survival analysis by training data of the patients' profiles with factors that are relevant to prostate cancer, which includes age and laboratory examination results using R script. The results are the mathematics component analysis. First, they uploaded various file types of input to decision support system for prostate cancer disease which converted those formulas to a matrix. Second, a new profile from patients is retrieved to DSP for patient decision aids. Third, the reports from the second step can support the treatment decision making process to physicians. They can empower patients to take a proactive role in their treatment pathway and to make participatory medicine possible. Finally, web service engine and endpoints were used to design the system and publish to the co-research developers, the medical statistician staff and other patients. In evaluation, the developed decision support platform for prostate cancer disease was measured in terms of system performance of “uploading 6 types of input” and user satisfaction with 60% of excellent to see how the system can support the users. The results show that system usage assessment category satisfaction level is 48% of excellent “content”, 45% excellent on “design”, and 66.67% excellent on “utilization”.