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Background: Electronic administrative data exist in several domains which, if linked, are potentially useful for research. However, benefits from data linkage should be considered alongside risks such as the threat to privacy. Avon Longitudinal Study of Parents and Children (ALSPAC) is a birth cohort study. The Project to Enhance ALSPAC through Record Linkage (PEARL) was established to enrich the ALSPAC resource through linkage between ALSPAC participants and routine sources of health and social data. Qualitative research was incorporated in the PEARL study to examine participants' views about data linkage and inform approaches to information sharing. This paper focusses on issues of consent.Methods: Digitally recorded interviews were conducted with 55 participants aged 17-19 years. Terms and processes relating to consent, anonymization and data linkage were explained to interviewees. Scenarios were used to prompt consideration of linking different sources of data, and whether consent should be requested. Interview recordings were fully transcribed. Thematic analysis was undertaken using the Framework approach.Results: Participant views on data linkage appeared to be most influenced by: considerations around the social sensitivity of the research question, and; the possibility of tangible health benefits in the public interest. Some participants appeared unsure about the effectiveness of anonymization, or did not always view effective anonymization as making consent unnecessary. This was related to notions of ownership of personal information and etiquette around asking permission for secondary use. Despite different consent procedures being explained, participants tended to equate consent with 'opt-in' consent through which participants are 'asked' if their data can be used for a specific study. Participants raising similar concerns came to differing conclusions about whether consent was needed. Views changed when presented with different scenarios, and were sometimes inconsistent.Conclusions: Findings from this study question the validity of 'informed consent' as a cornerstone of good governance, and the extent to which potential research participants understand different types of consent and what they are consenting, or not consenting, to. Pragmatic, imaginative and flexible approaches are needed if research using data linkage is to successfully realise its potential for public good without undermining public trust in the research process. [ABSTRACT FROM AUTHOR]

Background: Significant amounts of health data are stored as free-text within clinical reports, letters, discharge summaries and notes. Busy clinicians have limited time to read such large amounts of free-text and are at risk of information overload and consequently missing information vital to patient care. Automatically identifying relevant information at the point of care has the potential to reduce these risks but represents a considerable research challenge. One software solution that has been proposed in industry is the IBM Watson analytics suite which includes rule-based analytics capable of processing large document collections at scale. Results: In this paper we present an overview of IBM Watson Content Analytics and a feasibility study using Content Analytics with a large-scale corpus of clinical free-text reports within a UK National Health Service (NHS) context. We created dictionaries and rules for identifying positive incidence of hydronephrosis and brain metastasis from 5.6 m radiology reports and were able to achieve 94% precision, 95% recall and 89% precision, 94% recall respectively on a sample of manually annotated reports. With minor changes for US English we applied the same rule set to an open access corpus of 0.5 m radiology reports from a US hospital and achieved 93% precision, 94% recall and 84% precision, 88% recall respectively. Conclusions: We were able to implement IBM Watson within a UK NHS context and demonstrate effective results that could provide clinicians with an automatic safety net which highlights clinically important information within free-text documents. Our results suggest that currently available technologies such as IBM Watson Content Analytics already have the potential to address information overload and improve clinical safety and that solutions developed in one hospital and country may be transportable to different hospitals and countries. Our study was limited to exploring technical aspects of the feasibility of one industry solution and we recognise that healthcare text analytics research is a fast-moving field. That said, we believe our study suggests that text analytics is sufficiently advanced to be implemented within industry solutions that can improve clinical safety. [ABSTRACT FROM AUTHOR]