Data and Process Harmonisation of Multi-National, Multi-Site Research Data.

To achieve a single fully harmonised research data set suitable for analysis from data collected at multiple sites requires not only semantic integration of collection concepts and convergence onto single collection units, but harmonisation of data collection processes. We describe our experience of identifying harmonisation challenges in the Precision ALS project, with particular focus on process alignment challenges in a multi-site multi-national research data collection project.

PRECISION ALS-an integrated pan European patient data platform for ALS.

Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative condition. Despite significant advances in pre-clinical models that enhance understanding of disease pathobiology, translation of candidate drugs to effective human therapies has been disappointing. There is increasing recognition of the need for a precision medicine approach toward drug development, as many failures in translation can be attributed in part to disease heterogeneity in humans.

Modelling temporal data in knowledge graphs: a systematic review protocol.

The benefits of having high-quality healthcare data are well established. However, high-dimensionality and irregularity of healthcare data pose challenges in their management. Knowledge graphs have gained increasing popularity in many domains, as a method for representing data to overcome such challenges.

Development of a framework to assess the quality of data sources in healthcare settings.

Objective: The purpose of this study was to develop a framework to assess the quality of healthcare data sources.

Materials And Methods: First, a systematic review was performed and a thematic analysis of included literature conducted to identify items relating to the quality of healthcare data sources. Second, expert advisory group meetings were held to explore experts' perception of the results of the review and identify gaps in the findings.

Using a generalised identity reference model with archetypes to support interoperability of demographics information in electronic health record systems.

Computerised identity management is in general encountered as a low-level mechanism that enables users in a particular system or region to securely access resources. In the Electronic Health Record (EHR), the identifying information of both the healthcare professionals who access the EHR and the patients whose EHR is accessed, are subject to change. Demographics services have been developed to manage federated patient and healthcare professional identities and to support challenging healthcare-specific use cases in the presence of diverse and sometimes conflicting demographic identities.