Frontiers in Health Services (Oct 2023)

A holistic approach to integrating patient, family, and lived experience voices in the development of the BrainHealth Databank: a digital learning health system to enable artificial intelligence in the clinic

  • Joanna Yu,
  • Joanna Yu,
  • Nelson Shen,
  • Nelson Shen,
  • Nelson Shen,
  • Susan Conway,
  • Melissa Hiebert,
  • Benson Lai-Zhao,
  • Miriam McCann,
  • Rohan R. Mehta,
  • Morena Miranda,
  • Connie Putterman,
  • Connie Putterman,
  • Connie Putterman,
  • Connie Putterman,
  • Connie Putterman,
  • Jose Arturo Santisteban,
  • Nicole Thomson,
  • Courtney Young,
  • Lina Chiuccariello,
  • Kimberly Hunter,
  • Sean Hill,
  • Sean Hill,
  • Sean Hill

DOI
https://doi.org/10.3389/frhs.2023.1198195
Journal volume & issue
Vol. 3

Abstract

Read online

Artificial intelligence, machine learning, and digital health innovations have tremendous potential to advance patient-centred, data-driven mental healthcare. To enable the clinical application of such innovations, the Krembil Centre for Neuroinformatics at the Centre for Addiction and Mental Health, Canada's largest mental health hospital, embarked on a journey to co-create a digital learning health system called the BrainHealth Databank (BHDB). Working with clinicians, scientists, and administrators alongside patients, families, and persons with lived experience (PFLE), this hospital-wide team has adopted a systems approach that integrates clinical and research data and practices to improve care and accelerate research. PFLE engagement was intentional and initiated at the conception stage of the BHDB to help ensure the initiative would achieve its goal of understanding the community's needs while improving patient care and experience. The BHDB team implemented an evolving, dynamic strategy to support continuous and active PFLE engagement in all aspects of the BHDB that has and will continue to impact patients and families directly. We describe PFLE consultation, co-design, and partnership in various BHDB activities and projects. In all three examples, we discuss the factors contributing to successful PFLE engagement, share lessons learned, and highlight areas for growth and improvement. By sharing how the BHDB navigated and fostered PFLE engagement, we hope to motivate and inspire the health informatics community to collectively chart their paths in PFLE engagement to support advancements in digital health and artificial intelligence.

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