Improved Outcomes in Mental Healthcare Using Artificial Intelligence
Artificial intelligence (AI) presents opportunities and challenges in post-discharge psychiatric care. Leveraging structured data and machine learning, the Centre for Addiction and Mental Health aims to predict adverse outcomes, including readmissions, among patients recently discharged from psychiatric units. By identifying high-risk individuals, AI can guide referrals to resource-intensive outpatient clinics, enhancing continuity of care and improving outcomes. A governance framework addressing ethics, transparency and fairness underpins the development and implementation process. The study emphasizes using interpretable AI models over black-box systems to foster trust and clinical utility, aligning AI advancements with ethical mental health practices.
Authors
- Tania Tajirian (ORCID: https://orcid.org/0000-0003-0668-4397)
- Keith D’Souza
- Andrew Lustig (ORCID: https://orcid.org/0000-0002-6508-9283)
- Adam Tasca
- David Gratzer (ORCID: https://orcid.org/0000-0002-4578-0050)
- E. J. Kim (ORCID: https://orcid.org/0000-0003-1433-6018)
- Masooma Hassan (ORCID: https://orcid.org/0000-0001-6185-5051)
Institutions
- Centre for Addiction and Mental Health (CA)
- Toronto General Hospital (CA)
- College of Family Physicians of Canada (CA)
- Public Health Ontario (CA)
- Krembil Foundation (CA)
- Canadian Patient Safety Institute (CA)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- Healthcare Quarterly
- Published
- 2025-10-31
- DOI
- https://doi.org/10.12927/hcq.2025.27734
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00