Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study.
clinical staging
digital health solution
online diagnosis
service transformation
staged care
stratified care
youth mental health
Journal
JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394
Informations de publication
Date de publication:
08 Sep 2023
08 Sep 2023
Historique:
received:
18
12
2022
accepted:
26
06
2023
revised:
31
05
2023
medline:
8
9
2023
pubmed:
8
9
2023
entrez:
8
9
2023
Statut:
epublish
Résumé
As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals' risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services. The aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. We conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm's allocation based on a multidimensional self-report questionnaire. Of the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts' ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.
Sections du résumé
BACKGROUND
BACKGROUND
As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals' risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services.
OBJECTIVE
OBJECTIVE
The aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders.
METHODS
METHODS
We conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm's allocation based on a multidimensional self-report questionnaire.
RESULTS
RESULTS
Of the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts' ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F
CONCLUSIONS
CONCLUSIONS
This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.
Identifiants
pubmed: 37682588
pii: v7i1e45161
doi: 10.2196/45161
pmc: PMC10517388
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e45161Informations de copyright
©Min K Chong, Ian B Hickie, Shane P Cross, Sarah McKenna, Mathew Varidel, William Capon, Tracey A Davenport, Haley M LaMonica, Vilas Sawrikar, Adam Guastella, Sharon L Naismith, Elizabeth M Scott, Frank Iorfino. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.09.2023.
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