Abstract
BackgroundCare.data was a programme of work led by NHS England for the extraction of patient-identifiable and coded information from general practitioner (GP) records for secondary uses. This study analyses the forms (on the websites of GP practices) which enabled patients to opt out.MethodsTheoretical sampling and summative content analysis were used to collect and analyse dissent forms used by patients to opt out from care.data. Domains included basic information about the programme, types of objections and personal details required for identification purposes.ResultsOne hundred opt-out forms were analysed. Fifty-four forms mentioned that this programme was run by NHS England. 81 forms provided two types of objections to data-sharing, and 15 provided only one objection. Only 26 forms mentioned that direct care would not be affected and 32 that patients maintain their right to opt back anytime. All but one of the opt-out forms we reviewed requested the name of the person wishing to opt out. 94 required a date of birth and 33 an NHS number. 82 required an address, 42 a telephone number and 7 an email address.ConclusionsNumbers of patients (not) opting out should be treated with caution, because the variability of information provided and the varied options for dissent may have caused confusion among patients. To ensure that dissent is in accordance with individual preferences and moral values, we recommend that well-designed information material and standardised opt-out forms be developed for such data-sharing initiatives.
Funder
Research Executive Agency
Subject
Health Policy,Arts and Humanities (miscellaneous),Issues, ethics and legal aspects,Health (social science)
Reference46 articles.
1. Data Resource Profile: Clinical Practice Research Datalink (CPRD)
2. Gnani S , Azeem M . A user's guide to data collected in primary care in England. Imperial College London: Eastern Region Public Health Observatory (erpho) on behalf of the Association of Public Health Observatories, 2006. http://www1.imperial.ac.uk/resources/579D8B09-C1C1-4026-A7BE-C3E936EE9567/ (accessed 15 Oct 2015).
3. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network
4. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives
5. The Read clinical classification.
Cited by
24 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献