Developing an infrastructure for secure patient summary exchange in the EU context: Lessons learned from the KONFIDO project.

BACKGROUND: The increase of healthcare digitalization comes along with potential information security risks. Thus, the EU H2020 KONFIDO project aimed to provide a toolkit supporting secure cross-border health data exchange. METHODS: KONFIDO focused on the so-called "User Goals", while also identifying barriers and facilitators regarding eHealth acceptance. Key user scenarios were elaborated both in terms of threat analysis and legal challenges. Moreover, KONFIDO developed a toolkit aiming to enhance the security of OpenNCP, the reference implementation framework. RESULTS: The main project outcomes are highlighted and the "Lessons Learned," the technical challenges and the EU context are detailed. CONCLUSIONS: The main "Lessons Learned" are summarized and a set of recommendations is provided, presenting the position of the KONFIDO consortium toward a robust EU-wide health data exchange infrastructure. To this end, the lack of infrastructure and technical capacity is highlighted, legal and policy challenges are identified and the need to focus on usability and semantic interoperability is emphasized. Regarding technical issues, an emphasis on transparent and standards-based development processes is recommended, especially for landmark software projects. Finally, promoting mentality change and knowledge dissemination is also identified as key step toward the development of secure cross-border health data exchange services.

Comprehensive user requirements engineering methodology for secure and interoperable health data exchange.

BACKGROUND: Increased digitalization of healthcare comes along with the cost of cybercrime proliferation. This results to patients' and healthcare providers' skepticism to adopt Health Information Technologies (HIT). In Europe, this shortcoming hampers efficient cross-border health data exchange, which requires a holistic, secure and interoperable framework. This study aimed to provide the foundations for designing a secure and interoperable toolkit for cross-border health data exchange within the European Union (EU), conducted in the scope of the KONFIDO project. Particularly, we present our user requirements engineering methodology and the obtained results, driving the technical design of the KONFIDO toolkit. METHODS: Our methodology relied on four pillars: (a) a gap analysis study, reviewing a range of relevant projects/initiatives, technologies as well as cybersecurity strategies for HIT interoperability and cybersecurity; (b) the definition of user scenarios with major focus on cross-border health data exchange in the three pilot countries of the project; (c) a user requirements elicitation phase containing a threat analysis of the business processes entailed in the user scenarios, and (d) surveying and discussing with key stakeholders, aiming to validate the obtained outcomes and identify barriers and facilitators for HIT adoption linked with cybersecurity and interoperability. RESULTS: According to the gap analysis outcomes, full adherence with information security standards is currently not universally met. Sustainability plans shall be defined for adapting existing/evolving frameworks to the state-of-the-art. Overall, lack of integration in a holistic security approach was clearly identified. For each user scenario, we concluded with a comprehensive workflow, highlighting challenges and open issues for their application in our pilot sites. The threat analysis resulted in a set of 30 user goals in total, documented in detail. Finally, indicative barriers of HIT acceptance include lack of awareness regarding HIT risks and legislations, lack of a security-oriented culture and management commitment, as well as usability constraints, while important facilitators concern the adoption of standards and current efforts for a common EU legislation framework. CONCLUSIONS: Our study provides important insights to address secure and interoperable health data exchange, while our methodological framework constitutes a paradigm for investigating diverse cybersecurity-related risks in the health sector.

A DBSCAN based approach for jointly segment and classify brain MR images.

In recent years a growing interest has grown in Magnetic Resonance images segmentation techniques, due to their usefulness in many applications. Within this manuscript, a novel segmentation approach is presented, based on two main innovations. First, it exploits the estimated proton density and relaxation times for each pixel, instead of its gray-level intensity. This feature makes the algorithm particularly robust and allows the classification of identified segments. Secondly, it implements a specifically evolved version of the DBSCAN approach, gaining advantages in the effectiveness of region estimation. The technique, compared to an euclidean distance based one, is able to improve the correct classification rate. The effectiveness of the approach is evaluated on a simulated case study, and will be extended to real data within next weeks.