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Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness

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....

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Autores principales: Iorfino, Frank, Cheng, Vanessa Wan Sze, Cross, Shane P., Yee, Hannah F., Davenport, Tracey A., Scott, Elizabeth M., Hickie, Ian B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429786/
https://www.ncbi.nlm.nih.gov/pubmed/34513775
http://dx.doi.org/10.3389/fpubh.2021.621862
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author Iorfino, Frank
Cheng, Vanessa Wan Sze
Cross, Shane P.
Yee, Hannah F.
Davenport, Tracey A.
Scott, Elizabeth M.
Hickie, Ian B.
author_facet Iorfino, Frank
Cheng, Vanessa Wan Sze
Cross, Shane P.
Yee, Hannah F.
Davenport, Tracey A.
Scott, Elizabeth M.
Hickie, Ian B.
author_sort Iorfino, Frank
collection PubMed
description 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.
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spelling pubmed-84297862021-09-11 Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness Iorfino, Frank Cheng, Vanessa Wan Sze Cross, Shane P. Yee, Hannah F. Davenport, Tracey A. Scott, Elizabeth M. Hickie, Ian B. Front Public Health Public Health 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. Frontiers Media S.A. 2021-08-27 /pmc/articles/PMC8429786/ /pubmed/34513775 http://dx.doi.org/10.3389/fpubh.2021.621862 Text en Copyright © 2021 Iorfino, Cheng, Cross, Yee, Davenport, Scott and Hickie. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Iorfino, Frank
Cheng, Vanessa Wan Sze
Cross, Shane P.
Yee, Hannah F.
Davenport, Tracey A.
Scott, Elizabeth M.
Hickie, Ian B.
Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness
title Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness
title_full Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness
title_fullStr Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness
title_full_unstemmed Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness
title_short Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness
title_sort right care, first time: developing a theory-based automated protocol to help clinically stage young people based on severity and persistence of mental illness
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429786/
https://www.ncbi.nlm.nih.gov/pubmed/34513775
http://dx.doi.org/10.3389/fpubh.2021.621862
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