Inishowen Child and Adolescent Mental Health Service: Developing a Learning Health Microsystem

Learning health systems (LHSs) offer a model for continuous improvement in complex, real-world clinical environments. While widely described internationally, little evidence exists regarding their implementation within Irish mental health services. Inishowen child and adolescent mental health service (CAMHS), a small rural microsystem with limited digital maturity, sought to apply a LHS framework through the creation of a learning community, manual data collection and a living standard operating policies and procedures (SOPP) manual.

This study examines the development and early outcomes of this locally driven learning health microsystem, taking wait times as an exemplar. It explores the organizational complexity using the non-adoption, abandonment, scale-up, spread and sustainability (NASSS) framework.

A descriptive case-study design was used. Data were drawn from routine audits, caseload analysis, patient experience measures, referral metrics, and outputs from the learning community. The NASSS framework was applied to analyze complexity across conditions, technology, adopters, organization, wider system influences, and the capacity for embedding and adaptation over time.

Implementation of LHS processes was feasible despite paper-based records and minimal infrastructure. Between 2020 and 2024, the caseload reduced by half; from 2023, 100% of young people were seen within 3 months, and maximum waiting times fell from 743 days in 2020 to 49 days in 2025. Patient and family experience remained highly positive. Several elements, including the SOPP and youth engagement, became embedded, whereas continuous data collection remained vulnerable to staff turnover and workload pressures.

A learning health microsystem can emerge in digitally immature, resource-constrained CAMHS settings. Inishowen CAMHS demonstrates measurable service improvements and offers a replicable early model for LHS development in Irish mental health care. Future work should focus on scaling this microsystem approach and spreading nationally, strengthening digital infrastructure to support faster, more reliable learning cycles and to sustain improvements over time.