Precision Child and Youth Mental Health

As many as 50% of children and youth do not respond to mental health treatment. The current “one-size-fits all” approach leaves many patients and families suffering.

The precision approach to mental healthcare brings researchers, clinicians, data scientists, patients and caregivers together to design treatment that works for the individual.

Precision Child and Youth Mental Health (PCYMH) uses comprehensive information about the individual ranging from the DNA code to the postal code to more precisely identify and treat children and youth with mental health problems. The goal is to detect mental health disorders sooner, provide better treatments tailored to the needs of each child and youth and improve long-term outcomes.

Data and Research informatics: We are building data and artificial intelligence infrastructure (people and platforms).

Clinical Transformation: Building capacity and literacy in PCYMH to prepare clinical teams and practice infrastructure to deliver care that is better targeted to the need of the individual child or youth.

Research: Supporting research and innovation in PCYMH.

Register for the 2026 PCYMH conference

Precision Child and Youth Mental Health Collaboratory

Strategic Priorities 2024-2027

A Unique Research Centre

Sharing ideas, tools, data and skills between patients, caregivers, researchers, data scientists and clinicians to build innovative mental health care tailored to the needs of individual children and youth.

Vision

Improved mental health in the current generation and beyond, designed with the whole child in mind, through collaborative precision mental health research and care.

Mission

Co-design innovative solutions blending research, data and technology to create mental health care tailored for every child and youth.

Nurture teams that translate into real-life PCYMH care.

Conduct impactful PCYMH Research.

Advance the growth of the PCYMH at home and worldwide.

Use technology to deepen the understanding of child and youth mental health.

See what we have accomplished in our phase 1 report.

PCYMH Phase 1 Report Infographic Text Version

As we actively build PCYMH at CHEO, we invite your ideas and participation. Contact us at [email protected].

Related News

Research Projects

Predicting child and adolescent mental health emergency department revisits: a machine-learning approach compared to a clinician-derived baseline
01/12/2025

This study aimed to develop and validate a machine‑learning–based algorithm using electronic health record data to predict child and youth mental health emergency department revisits, and to compare its performance with a clinician‑weighted model. The machine learning approach outperformed the clinician-driven baseline while identifying clinically meaningful predictors such as prior ED visits, medication history, substance use, and outpatient mental health care. These findings demonstrate that interpretable ML models can complement clinical expertise and support improved planning for CYMH emergency care.
Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.
07/04/2025

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history, resulting in many delayed or missed diagnoses.
Participatory logic model for a precision child and youth mental health start-up: scoping review, case study, and lessons learned
16/10/2024

This study aimed to support the implementation of precision child and youth mental health (PCYMH) by developing a participatory logic model grounded in implementation science and stakeholder input. Through a scoping review, extensive organizational assessment, and iterative co‑creation with diverse stakeholders, the authors produced the first reported logic model for a PCYMH program. The findings highlight that while participatory logic model development is resource‑intensive, it can accelerate program readiness, strengthen stakeholder engagement, and inform equitable PCYMH system transformation.
Using Neuropsychological Profiling to Tailor Mental Health Care for Children and Youth: a Quality Improvement Project to Measure Feasibility
30/09/2024

This quality improvement study aimed to test the feasibility of an innovative care pathway in a pediatric outpatient mental health (OPMH) clinic that integrates neuropsychological profiling into individual psychotherapeutic care for children and youth. The project showed high participation and strong acceptability from patients, caregivers, and clinicians, with no adverse effects or disruptions to outpatient service flow, indicating the pathway is feasible and well‑received. The authors conclude that the next step is to evaluate clinical effectiveness in an experimental trial.