Harnessing digital health data for suicide prevention and care: A rapid review - Laura Bennett-Poynter; Sridevi Kundurthi; Reena Besa; Dan W. Joyce; Andrey Kormilitzin; Nelson Shen; James Sunwoo; Patrycja Szkudlarek; Lydia Sequiera; Laura Sikstrom (Digital Health)
Background and aim 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. Method 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. Results 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. Conclusions 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.