Bennett-Poynter Laura, Kundurthi Sridevi, Besa Reena, Joyce Dan W, Kormilitzin Andrey, Shen Nelson, Sunwoo James, Szkudlarek Patrycja, Sequiera Lydia, Sikstrom Laura
Oxford Health NHS Foundation Trust, Littlemore Mental Health Centre, Oxford, UK.
Krembil Centre for Neuroinformatics, Center for Addiction and Mental Health, Toronto, Canada.
Digit Health. 2025 Feb 23;11:20552076241308615. doi: 10.1177/20552076241308615. eCollection 2025 Jan-Dec.
Suicide is a global public health issue disproportionately impacting equity-deserving groups. Recent advances in Artificial Intelligence and increased access to a variety of digital data sources have enabled the development of novel and personalized suicide prevention strategies. However, standards on how to harness these data in a comprehensive and equitable way remain unclear. The primary aim of this study is to identify considerations for the collection and use of digital health data for suicide prevention and care. The results will inform the development of a data governance framework for a multinational suicide prevention mHealth platform.
We used a modified Cochrane Rapid Reviews Method. Inclusion criteria focused on primary studies published in English from 2007 to the present that referenced the use of digital health data in the context of suicide prevention and care. Screening and data extraction was performed independently by multiple reviewers, with disagreements resolved through discussion. Qualitative and quantitative synthesis methods were employed to identify emergent themes.
Our search identified 2453 potential articles, with 70 meeting inclusion criteria. We found little consensus on best practices for the collection and use of digital health data for suicide prevention and care. Issues of data quality, fairness and equity persist, compounded by inadequate consideration of key governance issues including privacy and trust, especially in multinational initiatives.
Recommendations for future research and practice include prioritizing engagement with knowledge users, establishing robust data governance frameworks aligned with clinical guidelines, and leveraging advanced analytics, such as natural language processing, to improve the quality of health equity data.
自杀是一个全球公共卫生问题,对弱势群体的影响尤为严重。人工智能的最新进展以及对各种数字数据源的更多访问,使得新型个性化自杀预防策略的开发成为可能。然而,如何以全面、公平的方式利用这些数据的标准仍不明确。本研究的主要目的是确定在自杀预防和护理中收集和使用数字健康数据时应考虑的因素。研究结果将为一个跨国自杀预防移动健康平台的数据治理框架的制定提供参考。
我们采用了改良的Cochrane快速综述方法。纳入标准侧重于2007年至今以英文发表的、在自杀预防和护理背景下提及使用数字健康数据的原始研究。由多名评审员独立进行筛选和数据提取,通过讨论解决分歧。采用定性和定量综合方法确定新出现的主题。
我们的检索共找到2453篇潜在文章,其中70篇符合纳入标准。我们发现,在自杀预防和护理中收集和使用数字健康数据的最佳实践方面,几乎没有达成共识。数据质量、公平性和公正性问题依然存在,关键治理问题(包括隐私和信任)考虑不足使情况更加复杂,尤其是在跨国项目中。
对未来研究和实践的建议包括优先与知识使用者进行互动、建立与临床指南相一致的强大数据治理框架,以及利用自然语言处理等先进分析技术来提高健康公平数据的质量。