Abstract
Background and Objective Prostate cancer (PCa) is a severe public health issue and the most common cancer
worldwide in men. Early diagnosis can lead to early treatment and long-term survival.
The addition of the multiparametric magnetic resonance imaging in combination with
ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate
cancer detection. Use of both tools gradually increases in every day urological practice.
Furthermore, advances in the area of information technology and artificial intelligence
have led to the development of software platforms able to support clinical diagnosis
and decision-making using patient data from personalized medicine.
Methods We investigated the current aspects of implementation, architecture, and design of
a health care information system able to handle and store a large number of clinical
examination data along with medical images, and produce a risk calculator in a seamless
and secure manner complying with data security/accuracy and personal data protection
directives and standards simultaneously. Furthermore, we took into account interoperability
support and connectivity to legacy and other information management systems. The platform
was implemented using open source, modern frameworks, and development tools.
Results The application showed that software platforms supporting patient follow-up monitoring
can be effective, productive, and of extreme value, while at the same time, aiding
toward the betterment medicine clinical workflows. Furthermore, it removes access
barriers and restrictions to specialized care, especially for rural areas, providing
the exchange of medical images and patient data, among hospitals and physicians.
Conclusion This platform handles data to estimate the risk of prostate cancer detection using
current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary
and intersectoral approaches. This work offers the research community an open architecture
framework that encourages the broader adoption of more robust and comprehensive systems
in standard clinical practice.
Keywords
prevention - diagnosis - clinical workflow - prostate cancer - multiparametric MRI-U/S
fusion - health care system framework
