According to the World Health Organization, addressing the mental health care gap for adolescents, especially in low-resource contexts, is a priority. Evidence-based assessment is crucial for selecting treatment strategies and for quality management.

To develop a digital platform for evidence-based assessments and implement it in different low-resource settings.

The project operates according to the principles of digital development (https://digitalprinciples.org/), including designing with the user, user testing, understanding the ecosystem, resusing software and being open source, think about sustainability and addressing privacy and security.

Different implementation contexts (in Tanzania, Kosovo and Chile) will be presented.

The learned lessons will be presented to the audience.

MHIRA is a promising tool that helps bridge the gap regarding adolescent mental health in low-resource settings. Challenges include the clinicans attitude towards evidence based assessment, sustainability of the project and integration with the existing information technology eco-system and regulations.

No significant relationships.

A variety of information sources are used in the best-evidence diagnostic procedure in child and adolescent mental healthcare, including evaluation by referrers and structured assessment questionnaires for parents. However, the incremental value of these information sources is still poorly examined.

To quantify the added and unique predictive value of referral letters, screening, multi-informant assessment and clinicians’ remote evaluations in predicting mental health disorders.

Routine medical record data on 1259 referred children and adolescents were retrospectively extracted. Their referral letters, responses to the Strengths and Difficulties Questionnaire (SDQ), results on closed-ended questions from the Development and Well-Being Assessment (DAWBA) and its clinician-rated version were linked to classifications made after face-to-face intake in psychiatry. Following multiple imputations of missing data, logistic regression analyses were performed with the above four nodes of assessment as predictors and the five childhood disorders common in mental healthcare (anxiety, depression, autism spectrum disorders, attention-deficit hyperactivity disorder, behavioural disorders) as outcomes. Likelihood ratio tests and diagnostic odds ratios were computed.

Each assessment tool significantly predicted the classified outcome. Successive addition of the assessment instruments improved the prediction models, with the exception of behavioural disorder prediction by the clinician-rated DAWBA. With the exception of the SDQ for depressive and behavioural disorders, all instruments showed unique predictive value.

Structured acquisition and integrated use of diverse sources of information supports evidence-based diagnosis in clinical practice. The clinical value of structured assessment at the primary–secondary care interface should now be quantified in prospective studies.