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dc.contributor.authorKoochakpour, Kaban
dc.contributor.authorNytrø, Øystein
dc.contributor.authorLeventhal, Bennett L.
dc.contributor.authorWestbye, Odd Sverre
dc.contributor.authorRøst, Thomas Brox
dc.contributor.authorKoposov, Roman Alexandriovich
dc.contributor.authorFrodl, Thomas
dc.contributor.authorClausen, Carolyn Elizabeth
dc.contributor.authorStien, Line Mærvoll
dc.contributor.authorSkokauskas, Norbert
dc.date.accessioned2024-10-11T10:41:15Z
dc.date.available2024-10-11T10:41:15Z
dc.date.issued2024-05-13
dc.description.abstractObjective: Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). Our secondary objective is to identify the potential for data analysis in different clinical services and functions in which data-driven decision aids can be useful. <p> <p>Materials and methods: We searched related studies in Science Direct and PubMed from 2018 to 2023(Jun), and also in ACM (Association for Computing Machinery) Digital Library, DBLP (Database systems and Logic Programming), and Google Scholar from 2018 to 2021. We have reviewed 39 studies and extracted types of analytical methods, information content, and information sources for decision-making. <p>Results: In order to compare studies, we developed a framework for characterizing health services, functions, and data features. Most data sets in reviewed studies were small, with a median of 1,176 patients and 46,503 record entries. Structured data was used for all studies except two that used textual clinical notes. Most studies used supervised classification and regression. Service and situation-specific data analysis dominated among the studies, only two studies used temporal, or process features from the patient data. This paper presents and summarizes the utility, but not quality, of the studies according to the care situations and care providers to identify service functions where data-driven decision aids may be relevant. <p>Conclusions: Frameworks identifying services, functions, and care processes are necessary for characterizing and comparing electronic health record (EHR) data analysis studies. The majority of studies use features related to diagnosis and assessment and correspondingly have utility for intervention planning and follow-up. Profiling the disease severity of referred patients is also an important application area.en_US
dc.identifier.citationKoochakpour, Nytrø, Leventhal, Westbye, Røst, Koposov, Frodl, Clausen, Stien, Skokauskas. A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services. International Journal of Medical Informatics. 2024;188en_US
dc.identifier.cristinIDFRIDAID 2270589
dc.identifier.doi10.1016/j.ijmedinf.2024.105479
dc.identifier.issn1386-5056
dc.identifier.issn1872-8243
dc.identifier.urihttps://hdl.handle.net/10037/35196
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalInternational Journal of Medical Informatics
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleA review of information sources and analysis methods for data driven decision aids in child and adolescent mental health servicesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)