Santisteban Jose Arturo, Rotenberg David, Kloiber Stefan, Maslej Marta M, Ansari Adeel, Amani Bahar, Courtney Darren, Farrokhi Farhat, Freeman Natalie, Hassan Masooma, Kwan Lucia, Mozuraitis Mindaugas, Lau Michael, Potapova Natalia, Qureshi Farhad, Schoer Nicole, Shen Nelson, Yu Joanna, Coombe Noelle, Hunter Kimberly, Selby Peter, Thomson Nicole, Jankowicz Damian, Hill Sean L
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada.
Front Neuroinform. 2025 Aug 13;19:1616981. doi: 10.3389/fninf.2025.1616981. eCollection 2025.
Mental health care is undermined by fragmented data collection, as incomplete datasets can compromise treatment efficacy and research. The BrainHealth Databank (BHDB) at the Centre for Addiction and Mental Health (CAMH) establishes the governance and infrastructure for a Learning Mental Health System that integrates digital tools, measurement-based care, artificial intelligence (AI), and open science to deliver personalized, data-driven care.
Central to the BHDB's approach is its comprehensive governance framework, which actively engages clinicians, researchers, data scientists, privacy and ethics experts, and patient and family partners. This codesigned approach ensures that digital health technologies are deployed ethically, securely, and effectively within clinical settings.
By aligning data collection with clinical and research goals and harmonizing over 12 million data points from 33,000 patient trajectories, the BHDB enhances data quality, enables real-time decision support, and fosters continuous improvement.
The BHDB provides a model for integrating AI and digital tools into mental health care, as well as research data collection, analyses, storage, and sharing through the BHDB Portal (https://bhdb.camh.ca).
心理健康护理因数据收集碎片化而受到损害，因为不完整的数据集可能会影响治疗效果和研究。成瘾与心理健康中心（CAMH）的脑健康数据库（BHDB）为学习型心理健康系统建立了治理和基础设施，该系统整合了数字工具、基于测量的护理、人工智能（AI）和开放科学，以提供个性化的、数据驱动的护理。
BHDB方法的核心是其全面的治理框架，该框架积极吸引临床医生、研究人员、数据科学家、隐私和伦理专家以及患者和家属合作伙伴参与。这种共同设计的方法确保数字健康技术在临床环境中以符合道德、安全和有效的方式部署。
通过使数据收集与临床和研究目标保持一致，并整合来自33000条患者轨迹的1200多万个数据点，BHDB提高了数据质量，实现了实时决策支持，并促进了持续改进。
BHDB为将AI和数字工具整合到心理健康护理以及通过BHDB门户（https://bhdb.camh.ca）进行研究数据收集、分析、存储和共享提供了一个模型。
