Explore the vast range of titles available.

ObjectiveTo evaluate the safety of implementing structured medication optimisation reviews (SMORs) for patients with atrial fibrillation (AF) prescribed direct oral anticoagulants (DOACs). SMORs aimed to improve quality of care and facilitate large-scale alignment with national prescribing guidance (to switch patients onto edoxaban).InterventionComplex intervention including a SMOR embedded within primary care electronic patient records alongside clinical decision support tools.DesignDoubly robust difference-in-difference analysis using linked electronic health records, comparing changes in prescribing and bleeding admissions in patients undergoing SMOR with those in patients not reviewed.SettingSefton (intervention area) and Liverpool (comparator area) in the Northwest of England.ParticipantsAll patients with AF prescribed a DOAC in 2022.Main outcomes and measuresProportion of patients prescribed apixaban, proportion of patients prescribed edoxaban and rate of emergency hospital admissions for bleeding-related events.ResultsThe proportion of patients in Sefton prescribed edoxaban increased from 19% to 35%; 13% (95% CI 11% to 14%) of the increase was associated with the SMOR. There was an 11-percentage point decrease in patients prescribed apixaban (95% CI −12% to −10%). Undergoing review was associated with a non-significant reduction in the risk of bleeding admissions (eight fewer admissions per 1000 people reviewed per year; 95% CI −22 to 6).ConclusionsSMORs can be delivered at scale and used to switch medications for a large proportion of people. There was no evidence of an increased risk of admissions for bleeding complications in AF patients following a large-scale switch from apixaban to edoxaban supported by SMORs. Such reviews could improve prescribing quality and patient safety by ensuring patients are on the most appropriate dose and choice of DOAC and lead to cost savings to health services (by facilitating a switch to a better value product) while not increasing risks for patients.

Background Genomic and biosocial research data about individuals is rapidly proliferating, bringing the potential for novel opportunities for data integration and use. The scale, pace and novelty of these applications raise a number of urgent sociotechnical, ethical and legal questions, including optimal methods of data storage, management and access. Although the open science movement advocates unfettered access to research data, many of the UK’s longitudinal cohort studies operate systems of managed data access, in which access is governed by legal and ethical agreements between stewards of research datasets and researchers wishing to make use of them. Amongst other things, these agreements aim to respect the reasonable expectations of the research participants who provided data and samples, as expressed in the consent process. Arguably, responsible data management and governance of data and sample use are foundational to the consent process in longitudinal studies and are an important source of trustworthiness in the eyes of those who contribute data to genomic and biosocial research. Methods This paper presents an ethnographic case study exploring the foundational principles of a governance infrastructure for Managing Ethico-social, Technical and Administrative issues in Data ACcess (METADAC), which are operationalised through a committee known as the METADAC Access Committee. METADAC governs access to phenotype, genotype and ‘omic’ data and samples from five UK longitudinal studies. Findings Using the example of METADAC, we argue that three key structural features are foundational for practising responsible data sharing: independence and transparency; interdisciplinarity; and participant-centric decision-making. We observe that the international research community is proactively working towards optimising the use of research data, integrating/linking these data with routine data generated by health and social care services and other administrative data services to improve the analysis, interpretation and utility of these data. The governance of these new complex data assemblages will require a range of expertise from across a number of domains and disciplines, including that of study participants. Human-mediated decision-making bodies will be central to ensuring achievable, reasoned and responsible decisions about the use of these data; the METADAC model described in this paper provides an example of how this could be realised.

Abstract Motivation The validity of epidemiologic findings can be increased using triangulation, i.e. comparison of findings across contexts, and by having sufficiently large amounts of relevant data to analyse. However, access to data is often constrained by practical considerations and by ethico-legal and data governance restrictions. Gaining access to such data can be time-consuming due to the governance requirements associated with data access requests to institutions in different jurisdictions. Results DataSHIELD is a software solution that enables remote analysis without the need for data transfer (federated analysis). DataSHIELD is a scientifically mature, open-source data access and analysis platform aligned with the ‘Five Safes’ framework, the international framework governing safe research access to data. It allows real-time analysis while mitigating disclosure risk through an active multi-layer system of disclosure-preventing mechanisms. This combination of real-time remote statistical analysis, disclosure prevention mechanisms, and federation capabilities makes DataSHIELD a solution for addressing many of the technical and regulatory challenges in performing the large-scale statistical analysis of health and biomedical data. This paper describes the key components that comprise the disclosure protection system of DataSHIELD. These broadly fall into three classes: (i) system protection elements, (ii) analysis protection elements, and (iii) governance protection elements. Availability and implementation Information about the DataSHIELD software is available in https://datashield.org/ and https://github.com/datashield.