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The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of structured data, coupled with contextual information about their creation, expressed in domain-specific standards and machine readable formats. This paper analyses the potential support to FAIRness of the openEHR e-health standard, by theoretically assessing the compliance with each of the 15 FAIR principles of a hypothetical Clinical Data Repository (CDR) developed according to the openEHR specifications. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant CDRs.

Current high-throughput sequencing technologies allow us to acquire entire genomes in a very short time and at a relatively sustainable cost, thus resulting in an increasing diffusion of genetic test capabilities, in specialized clinical laboratories and research centers. In contrast, it is still limited the impact of genomic information on clinical decisions, as an effective interpretation is a challenging task. From the technological point of view, genomic data are big in size, have a complex granular nature and strongly depend on the computational steps of the generation and processing workflows. This article introduces our work to create the openEHR Genomic Project and the set of genomic information models we developed to catch such complex structure and to preserve data provenance efficiently in a machine-readable format. The models support clinical actionability of data, by improving their quality, fostering interoperability and laying the basis for re-usability.

Mastoidectomy is one of the most common surgical procedures relating to the petrous bone. In this paper we describe our preliminary results in the realization of a virtual reality mastoidectomy simulator. Our system is designed to work on patient-specific volumetric object models directly derived from 3D CT and MRI images. The paper summarizes the detailed task analysis performed in order to define the system requirements, introduces the architecture of the prototype simulator, and discusses the initial feedback received from selected end users.

We describe a strategy for collecting experimental data and validating a bone-burr haptic contact model developed in a virtual surgical training system for middle ear surgery. The validation strategy is based on the analysis of data acquired during virtual and real burring sessions. Our approach involves intensive testing of the surgical simulator by expert surgeons and trainees as well as experimental data acquisition in a controlled environment.