Abstract
Most mental disorders emerge before the age of 25 years and, if left untreated, have the potential to lead to considerable lifetime burden of disease. Many services struggle to manage high demand and have difficulty matching individuals to timely interventions due to the heterogeneity of disorders. The technological implementation of clinical staging for youth mental health may assist the early detection and treatment of mental disorders. We describe the development of a theory-based automated protocol to facilitate the initial clinical staging process, its intended use, and strategies for protocol validation and refinement. The automated clinical staging protocol leverages the clinical validation and evidence base of the staging model to improve its standardization, scalability, and utility by deploying it using Health Information Technologies (HIT). Its use has the potential to enhance clinical decision-making and transform existing care pathways, but further validation and evaluation of the tool in real-world settings is needed.
Highlights
Most mental disorders emerge before the age of 25 years and result in considerable burden of disease [1, 2]
The objective of this paper is to describe the translation of the clinical staging model into a decision support tool using Health Information Technologies (HIT)
This paper extends previous work on developing a clinical decision-making protocol based on the clinical staging framework [18, 23], by presenting the basic structure of an automated version of this protocol (Algorithms 1 and 2). This automated protocol was developed as a feature of a HIT that aimed to support mental health services by automating intake processes, including conducting a multidimensional initial assessment by collecting demographic data, administering psychometric scales, and providing real-time feedback about results
Summary
Most mental disorders emerge before the age of 25 years and result in considerable burden of disease [1, 2]. This paper extends previous work on developing a clinical decision-making protocol based on the clinical staging framework [18, 23], by presenting the basic structure of an automated version of this protocol (Algorithms 1 and 2) This automated protocol was developed as a feature of a HIT that aimed to support mental health services by (among other things) automating intake processes, including conducting a multidimensional initial assessment by collecting demographic data, administering psychometric scales, and providing real-time feedback about results. Algorithm 1: clinical staging algorithm //Apply formula to determine if young person meets conditions of being rated stage 2+ IF Social and occupational function rating indicates ongoing and major impact on functioning AND ( Clear manic syndrome (not just symptoms) OR Clear psychotic syndrome (not just symptoms) OR Clear severe depressive syndrome OR Clear severe anxiety syndrome OR Previous Hospitalization for mental ill-health OR Significant and ongoing comorbid syndromes (e.g., substance misuse, eating disorders, personality) ) assign ‘Stage 2+’. The dynamic use of this protocol ensures that an individual’s care plan is adjusted over time to match any changes in their clinical stage or care needs
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