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Research data sharing requires provision of adequate security. The requirements for data privacy are extremely demanding for medical data that is reused for research purposes. To address these requirements, the research institutions must implement adequate security measurements, and this demands large effort and costs to do it properly. The usage of adequate access controls and data encryption are key approaches to effectively protect research data confidentiality; however, the management of the encryption keys is challenging. There are novel mechanisms that can be explored for managing access to the encryption keys and encrypted files. These mechanisms guarantee that data are accessed by authorised users and that auditing is possible. In this paper we explore these mechanisms to implement a secure research medical data sharing system. In the proposed system, the research data are stored on a secure cloud system. The data are partitioned into subsets, each one encrypted with a unique key. After the authorisation process, researchers are given rights to use one or more of the keys and to selectively access and decrypt parts of the dataset. Our proposed solution offers automated fine-grain access control to research data, saving time and work usually made manually. Moreover, it maximises and fortifies users' trust in data sharing through secure clouds solutions. We present an initial evaluation and conclude with a discussion about the limitations, open research questions and future work around this challenging topic.

Acute stroke care demands fast procedures and collaboration of different healthcare organisations and professionals. The development of cloud computing and the wide adoption of electronic health records (EHR) foster healthcare systems to improve data availability and potentially enhance acute-stroke care quality. However, the design of a secure and privacy-preserving EHR cloud-based application is challenging. This paper presents the leading security and privacy requirements for healthcare applications and contextualises each requirement in the acute stroke care use case. Moreover, we deploy the ASCLEPIOS eHealth Cloud-based framework to address each requirement in designing an EHR cloud-based application for data sharing during acute stroke care. Our initial prototype combines the ASCLEPIOS framework with a public cloud service infrastructure and a private signature scheme. The prototype meets the security and privacy requirements since it protects the EHR data against unauthorised access, data breaches, and data exposure on public cloud providers. Moreover, we present preliminary results and discussions about the usability and overhead of the ASCLEPIOS framework.

Delirium, affecting up to 52% of cardiac surgery patients, can have serious long-term effects on patients by damaging cognitive ability and causing subsequent functional decline. This study reports on the development and evaluation of predictive models aimed at identifying the likely onset of delirium on an hourly basis in intensive care unit following cardiac surgery. Most models achieved a mean AUC > 0.900 across all lead times. A support vector machine achieved the highest performance across all lead times of AUC = 0.941 and Sensitivity = 0.907, and BARTm, where missing values were replaced with missForest imputation, achieved the highest Specificity of 0.892. Being able to predict delirium hours in advance gives clinicians the ability to intervene and optimize treatments for patients who are at risk and avert potentially serious and life-threatening consequences.