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.

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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
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article

Improved Outcomes in Mental Healthcare Using Artificial Intelligence

Tania Tajirian, Keith D’Souza, Andrew Lustig, Adam Tasca et al.
Healthcare Quarterly
Artificial Intelligence in Healthcare and Education
article

Improved Outcomes in Mental Healthcare Using Artificial Intelligence

Tania Tajirian, Keith D’Souza, Andrew Lustig, Adam Tasca, David Gratzer, E. J. Kim, Masooma Hassan
article en

Abstract

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.

Healthcare QuarterlyVol. 28(3)
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)
Openalex Percentile: Top 66%
Artificial Intelligence in Healthcare and Education
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