用数字健康数据来预防和照顾自杀:快速审查
Laura Bennett-Poynter1, Sridevi Kundurthi2, Reena Besa2,3
1Oxford Health NHS Foundation Trust, Littlemore Mental Health Centre, Oxford, UK.
这项研究凸显了在使用数字健康数据预防自杀方面缺乏共识. 它呼吁强有力的数据治理和先进的分析,以确保为弱势群体提供公平有效的战略.
科学领域:
- 数字健康数字健康
- 公共卫生 公共卫生
- 人工智能的人工智能
背景情况:
- 自杀是一个全球性的健康危机,特别影响到配当的群体.
- 人工智能和数字数据为个性化的自杀预防提供了新的途径.
- 缺乏公平数据收集和用于预防自杀的明确标准.
研究的目的:
- 确定在自杀预防和护理中收集和利用数字健康数据的关键考虑因素.
- 为一个跨国自杀预防mHealth平台开发数据治理框架提供信息.
主要方法:
- 使用了经过修改的柯克兰快速审查方法.
- 纳入标准侧重于使用数字健康数据预防自杀的初级研究 (2007年至今).
- 使用定性和定量合成方法来确定主题.
主要成果:
- 70项研究符合纳入标准,对最佳实践几乎没有共识.
- 持续存在的问题包括数据质量,公平性和公平性.
- 特别是在跨国背景下,人们注意到对隐私,信任和治理的关注不足.
结论:
- 为未来的研究和实践优先考虑与知识用户的接触.
- 建立与临床指南一致的强大数据治理框架.
- 使用高级分析,如自然语言处理,以提高健康公平数据质量.
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