Privacy-by-Design and Minimization within a Small Electronic Health Record: The Health360 Case Study

Abstract

Electronic health records are playing an important role in todays’ clinical research, with the possibility to collect a wide amount of data from different sources, not only within a structured clinical setting, but also making best use of new portable technologies, such as smartphones, sensors and Internet-of-Things, as an unprecedented spring of data. In this way, even in small clinical centers, often featuring limited financial availabilities, not only clinicians can have a complete, timely outlook on patients’ health, but also data scientists could use such information to build and train tailored models in the broader perspective of “p4 medicine”. However, all this should align with the strict regulations and needs concerning data privacy and security, safeguarding the rights of the individual and the confidentiality of information related to their healthcare status. Here, we present a case study dealing with Health360, a platform designed to fill in this gap, representing the ideal solution for small clinical centers, where usability and cost-affordability are key characteristics for such a system, to collect multimodal data from various sources actually employed in the framework of neuromuscular conditions. The platform, designed under the Software-as-a-Service paradigm, actually collects data from different clinical centers active in the field of neuromuscular diseases, and therefore was designed to grant access to the data to specific professionals depending on their roles. At the same time, to the benefit of data scientists, Health360 enables joint data processing, with the management of authorization principles for various health professionals from different clinical centers, which is regulated by the data minimization principle, based on the accessing profile. Under such premises, we present here the approach followed for the implementation of the platform, managing the trade-off between the need from various professionals for accessing the complete dataset and the privacy requirements, as well as confidentiality maintenance for sensitive data of patients enrolled on the project.