James A. Cunningham

Integrating Object-Oriented and Ontological Representations: A Case Study in Java and OWL

The Web Ontology Language (OWL) provides a modelling paradigm that is especially well suited for developing models of large, structurally complex domains such as those found in Health Care and the Life Sciences. OWLs declarative nature combined with powerful reasoning tools has effectively supported the development of very large and complex anatomy, disease, and clinical ontologies. OWL, however, is not a programming language, so using these models in applications necessitates both a technical means of integrating OWL models with programs and considerable methodological sophistication in knowing how to integrate them. In this paper, we present an analytical framework for evaluating various OWL-Java combination approaches. We have developed a software framework for what we call hybrid modelling , that is, building models in which part of the model exists and is developed directly in Java and part of the model exists and is developed directly in OWL. We analyse the advantages and disadvantages of hybrid modelling both in comparison to other approaches and by means of a case study of a large medical records system.

On moving targets and magic bullets: Can the UK lead the way with responsible data linkage for health research?

Highlights • We explore key elements of good governance in health linkage.• Adaptive reflexive governance models are essential.• Two examples illustrate how we can achieve standardisation of practice.• Distinct elements of governance compiled in a composite fashion tend to challenges.

Data Safe Havens and Trust: Toward a Common Understanding of Trusted Research Platforms for Governing Secure and Ethical Health Research.

In parallel with the advances in big data-driven clinical research, the data safe haven concept has evolved over the last decade. It has led to the development of a framework to support the secure handling of health care information used for clinical research that balances compliance with legal and regulatory controls and ethical requirements while engaging with the public as a partner in its governance. We describe the evolution of 4 separately developed clinical research platforms into services throughout the United Kingdom-wide Farr Institute and their common deployment features in practice. The Farr Institute is a case study from which we propose a common definition of data safe havens as trusted platforms for clinical academic research. We use this common definition to discuss the challenges and dilemmas faced by the clinical academic research community, to help promote a consistent understanding of them and how they might best be handled in practice. We conclude by questioning whether the common definition represents a safe and trustworthy model for conducting clinical research that can stand the test of time and ongoing technical advances while paying heed to evolving public and professional concerns.

Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project.

Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93–100%), while drug-based components were the main contributors in RLDs (81–100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.

eLab: Bringing Together People, Data and Methods to Enhance Knowledge Discovery in Healthcare Settings.

The discovery of knowledge from raw data is a multistage process, that typical requires collaboration between experts from disparate disciplines, and the application of a range of methods tailored to the research question. The aim of the eLab is to provide a web-based environment for health professionals and researchers to access health datasets, share knowledge and expertise and to apply methods for analysis and visualization of the results. The eLab is built around the core concept of the Research Object as the mechanism for preserving, reusing and disseminating the knowledge discovery process. The possible range of applications of the eLab is vast, and so the consideration of the trade off between specificity and generality is an important one, that is reflected in the requirements. The architecture and implementation of the eLab is described, and we report on the deployment of eLabs for applications in primary care, long-term conditions management, bariatric surgery and public health.

Proposal for a European Public Health Research Infrastructure for Sharing of health and Medical administrative data (PHRIMA).

In Europe, health and medical administrative data is increasingly accumulating on a national level. Looking further than re-use of this data on a national level, sharing health and medical administrative data would enable large-scale analyses and European-level public health projects. There is currently no research infrastructure for this type of sharing. The PHRIMA consortium proposes to realise the Public Health Research Infrastructure for Sharing of health and Medical Administrative data (PHRIMA) which will enable and facilitate the efficient and secure sharing of healthcare data.

Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe

Objectives: To present the European landscape regarding the re-use of health administrative data for research. Methods: We present some collaborative projects and solutions that have been developed by Nordic countries, Italy, Spain, France, Germany, and the UK, to facilitate access to their health data for research purposes. Results: Research in public health is transitioning from siloed systems to more accessible and re-usable data resources. Following the example of the Nordic countries, several European countries aim at facilitating the re-use of their health administrative databases for research purposes. However, the ecosystem is still a complex patchwork, with different rules, policies, and processes for data provision. Conclusion: The challenges are such that with the abundance of health administrative data, only a European, overarching public health research infrastructure, is able to efficiently facilitate access to this data and accelerate research based on these highly valuable resources.

Computable Information Governance Contracts

The risks of relinquishing control of electronic healthcare data for re-use in research are mitigated by the use of data sharing agreements and information governance procedures. These exist as legal, or quasi-legal, textual documents exchanged between data owners. Their existence outside of the digital realm leads to a situation where breaches of an agreement can only be detected and acted on post-hoc. We introduce the design of a system of computable contracts, specified formally, that can enforce the rules of data sharing agreements within the bounds of electronic health care systems.