Hays Rebecca, Daker-White Gavin
NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre (PSTRC), Manchester Academic Health Science Centre, University of Manchester, Williamson Building, Oxford Road, Manchester, UK.
BMC Public Health. 2015 Sep 2;15:838. doi: 10.1186/s12889-015-2180-9.
Large, integrated datasets can be used to improve the identification and management of health conditions. However, big data initiatives are controversial because of risks to privacy. In 2014, NHS England launched a public awareness campaign about the care.data project, whereby data from patients' medical records would be regularly uploaded to a central database. Details of the project sparked intense debate across a number of platforms, including social media sites such as Twitter. Twitter is increasingly being used to educate and inform patients and care providers, and as a source of data for health services research. The aim of the study was to identify and describe the range of opinions expressed about care.data on Twitter for the period during which a delay to this project was announced, and provide insight into the strengths and flaws of the project.
Tweets with the hashtag #caredata were collected using the NCapture tool for NVivo. Methods of qualitative data analysis were used to identify emerging themes. Tweets were coded and analysed in-depth within and across themes.
The dataset consisted of 9895 tweets, captured over 18 days during February and March 2014. Retweets (6118, 62%) and spam (240, 2%) were excluded. The remaining 3537 tweets were posted by 904 contributors, and coded into one or more of 50 sub-themes, which were organised into 9 key themes. These were: informed consent and the default 'opt-in', trust, privacy and data security, involvement of private companies, legal issues and GPs' concerns, communication failure and confusion about care.data, delayed implementation, patient-centeredness, and potential of care.data and the ideal model of implementation.
Various concerns were raised about care.data that appeared to be shared by those both for and against the project. Qualitatively analysing tweets enabled us to identify a range of concerns about care.data and how these might be overcome, for example, by increasing the involvement of stakeholders and those with expert knowledge. Our findings also highlight the risks of not considering public opinion, such as the potential for patient safety failures resulting from a lack of trust in the healthcare system. However, caution is advised if using Twitter as a stand-alone data source, as contributors may lie more heavily on one side of a debate than another. A mixed-methods approach would have enabled us to complement this data with a more representative overview.
大型综合数据集可用于改善健康状况的识别与管理。然而，大数据计划因存在隐私风险而颇具争议。2014年，英国国民医疗服务体系（NHS）英格兰地区发起了一场关于“医疗数据”（care.data）项目的公众宣传活动，该项目将定期把患者病历数据上传至中央数据库。该项目的细节在包括推特等社交媒体网站在内的多个平台引发了激烈辩论。推特越来越多地被用于教育患者和护理人员并为其提供信息，同时也作为健康服务研究的数据来源。本研究的目的是识别并描述在宣布该项目延迟期间推特上关于“医疗数据”项目所表达的各种观点范围，并深入了解该项目的优势与缺陷。
使用NVivo的NCapture工具收集带有#caredata标签的推文。采用定性数据分析方法来识别新出现的主题。对推文进行编码，并在主题内部和主题之间进行深入分析。
数据集由2014年2月和3月的18天内捕获的9895条推文组成。转发推文（6118条，占62%）和垃圾信息（240条，占2%）被排除。其余3537条推文由904名贡献者发布，并被编码为50个次主题中的一个或多个，这些次主题被归纳为9个关键主题。它们分别是：知情同意与默认的“选择加入”、信任、隐私与数据安全、私人公司的参与、法律问题与全科医生的担忧、沟通失败与对“医疗数据”项目的困惑、实施延迟、以患者为中心，以及“医疗数据”项目的潜力与理想的实施模式。
对于“医疗数据”项目，支持和反对该项目的人似乎都表达了各种担忧。对推文进行定性分析使我们能够识别出对“医疗数据”项目的一系列担忧以及如何克服这些担忧，例如，通过增加利益相关者和具有专业知识者的参与度。我们的研究结果还凸显了不考虑公众意见的风险，比如因对医疗系统缺乏信任而导致患者安全出现问题的可能性。然而，如果将推特作为单一数据源使用则需谨慎，因为贡献者在辩论中可能会更倾向于某一方。采用混合方法本可以使我们用更具代表性的概述来补充这些数据。
