Douglas Russell

Accessing and aggregating legacy data sources for healthcare research, delivery and training

The aggregation of data from disparate sources offers clear benefits for healthcare researchers and practitioners. Such aggregation, however, must satisfy ethical, legal and social requirements: data ownership must be respected; patient privacy must not be compromised; data storage and transfer must be secure; etc. In this paper we describe the query aspects of sif (for service-oriented interoperability framework), a system which has been developed to support healthcare-related applications that depend upon the secure aggregation of data from multiple legacy databases. Importantly, the system allows the federation of data stored in varying database management systems utilising varying schemas.

On the Facilitation of Fine-Grained Access to Distributed Healthcare Data

As an increasing amount of healthcare-related data is captured in both clinical and research contexts, the drive to provide appropriate access to such data becomes stronger. The very nature of such data means that simplistic approaches to authorisation--be they coarse-grained or role-based--are insufficient: the needs of the domain give rise to requirements for authorisation models capable of capturing fine-grained, expressive access control policies. We describe the development of a framework for the secure sharing and aggregation of healthcare-related data, called sif(for service-oriented interoperability framework). In particular, we concentrate on the access control aspects of the system and describe its utilisation of XACML in this respect.

On The Development of Secure Service-Oriented Architectures to Support Medical Research

In this article we report upon our experiences of developing Web-services based infrastruc-tures within two e-health projects. The first—a small demonstrator project funded by the UK’s National Cancer Research Institute (NCRI)—is concerned with facilitating the aggregation of different types of data (specifically, MRI scans and histopathology slides) to aid the treatment of colorectal cancer; the second—a rather larger project funded by the UK’s Medical Research Council (MRC)—is concerned with the development of a virtual research environment to support neuro-imaging research. In both cases, the underlying infrastructures are being developed by a team that is based in Oxford; it is the experiences of this team that we report upon in this article. We also report upon how we have considered the future potential for our systems interoperating with other systems which are deployed within the UK’s National Health Service (NHS).

The Challenges of Developing a Collaborative Data and Compute Grid for Neurosciences

The three-year UK NeuroGrid project aims to develop a grid-based collaborative research environment to support the data and compute needs for a UK Neurosciences community. This paper describes the challenges in developing this architecture and details initial results from the development of its first prototype to support psychosis, dementia and stroke research and the social challenges of such a collaborative research project. The paper discusses approaches being taken to explore the collaborative science process to inform the requirements for follow on prototypes and methods utilized to develop an effective project team

GIMI: the past, the present and the future.

In keeping with the theme of this year’s e-Science All Hands Meeting—past, present and future—we consider the motivation for, the current status of, and the future directions for, the technologies developed within the GIMI (Generic Infrastructure for Medical Informatics) project. This analysis provides insights into how some key problems in data federation may be addressed. GIMI was funded by the UK’s Technology Strategy Board with the intention of developing a service-oriented framework to facilitate the secure sharing and aggregation of heterogeneous data from disparate sources to support a range of healthcare applications. The project, which was led by the University of Oxford, involved collaboration from the National Cancer Research Institute Informatics Initiative, Loughborough University, University College London, t+ Medical, Siemens Molecular Imaging and IBM UK.

On the Secure Sharing of Legacy Data

We describe a framework for the secure sharing and aggregation of legacy data. The framework, \emph{sif} (for service-oriented interoperability framework), has two key principles at its core: that it should be possible to expose data from any legacy data source, irrespective of the underlying technologies or data models, and that data owners should be afforded the opportunity for expressive access control policies. sif abstracts issues such as secure transport and heterogeneous federation from application developers via a Java API. Our particular focus in this paper is sifs plug-in mechanism, which gives rise to a simple and elegant means of facilitating interoperability.

On the utilisation of a service-oriented infrastructure to support radiologist training

The rise of service-oriented architectures and technology has, in recent years, started to lead to the opportunity for data sharing on a large-scale. In this paper we report upon how a service-oriented framework has been leveraged to support the development of a training application for radiologists. The application and framework have been developed separately — but sympathetically — with experience in both domains being leveraged to develop the prototype of an end-to-end training system. While the initial system utilises previously collected data, it is intended that in the future the system will interoperate with systems deployed within hospital environments.

Maintaining the provenance of microscopy metadata using OMERO.forms software

The creation of datasets that are findable, accessible, interoperable and reproducible (the FAIR standard) requires that data provenance be maintained1. Provenance is particularly important for microscopy data, whose interpretation is dependent on the biological context (e.g. cell state) and detection reagent (e.g. antibody.) This paper describes a new software tool, OMERO.forms, that extends the OMERO microscopy data management system2 to simplify and enhance metadata entry and provenance tracking.