Title: Using unpublished data, text mining and social media to maximise the efficiency and effectiveness of the retrieval of adverse effects data
Funder: Department of Health and Social Care

Abstract: Objectives Information on the nature, frequency and severity of adverse effects of healthcare interventions is essential for informed decision making and can be a deciding factor, particularly when a range of options are available.I will develop the methods for identifying adverse effects data, building on my PhD. I will explore; A) the retrieval of unpublished adverse effects data of non-drug interventions (this will complement previous research on drug interventions) B) text mining for developing search filters for adverse effects of drug and non-drug interventions C) social media (such as Twitter, discussion forums) for information on adverse effects of drug and non-drug interventions Design There are six work streams: 1) Systematic Review of the Methodological Literature To put this research into context I will begin with a systematic review of research on social media and unpublished data as sources of adverse effects data, and on adverse effects database search filters. 2) Retrospective Analysis of Systematic Reviews of Non-drug Interventions I will conduct an analysis of a series of systematic reviews of non-drug interventions to assess; a) the extent to which reviews of non-drug interventions include data on adverse effects b) the success rate for identifying adverse effects data (published and unpublished) for non-drug interventions c) publication bias for adverse effects data of non-drug interventions 3) Adverse Effects Search Terms in Primary Studies of Non-drug Interventions I will use the included primary studies from systematic reviews, in work stream 2, to assess the presence and absence of adverse effects terms in database records and the retrieval rates for relevant studies with particular search terms. 4) Text Mining to Improve the Efficiency of Searching I will use text mining techniques to develop search filters for adverse effects of particular types of intervention (such as drugs, medical devices, diagnostics and surgery).5) Social Media as a Data Source I will explore patients' perceived role of social media as a source for information on adverse effects of healthcare interventions to inform their decision-making. 6) Case Study Systematic Reviews and Economic Models I will work with teams of researchers on forthcoming systematic reviews and economic models. I will assess the implications on the resources required and the impact on the results of reviews/models if social media were to be used to identify adverse effects data in addition to traditional information sources.Expected Outcomes The results will provide evidence on the most effective and efficient methods for identifying adverse effects in systematic reviews and economic models. In particular, the results will explore the susceptibility for publication bias in studies of non-drug interventions, the effectiveness of automating the search process using text mining and objectively derived adverse effects search filters, and the role of social media in providing information on adverse effects. Anticipated Benefits This will improve the methods used in searching for adverse effects, ensuring that as much information is identified as possible by the most efficient means, reducing potential biases and making the best use of resources. The results will inform guidance and training given in identifying adverse effects data. Improving current practice by researchers and information specialists will increase the quality, rigour and efficiency of systematic reviews and economic models incorporating adverse effects data. This will impact on the quality of evidence summaries (such as BMJ Evidence) and clinical information/decision tools (such as Dynamed, UpToDate) and enable better informed guidance, guidelines and patient information to be produced. NHS patients, health professionals, and policy makers will be able to make more informed balanced decisions, which consider potential adverse effects on a par with potential benefits and thus empower patients and enhance patient safety.

Lay abstract: All healthcare interventions have the potential to cause harm. Side effects can impact on patients' quality of life and lead to disability and death. Information about possible side effects is essential for informed decision-making, yet it is not easy to find. I will assess the usefulness of social media (e.g. Facebook, Twitter) and unpublished material (e.g. reports, manufacturer data) and will create database searches to find information on the side effects of healthcare interventions. This will lead to better informed patients, clinicians and policy makers within the NHS, in turn enabling more balanced decision-making, weighing-up potential benefits against the risks.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: GENERIC HEALTH RELEVANCE