Publication:
Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.

dc.contributor.authorChung, Younjin
dc.contributor.authorSalvador-Carulla, Luis
dc.contributor.authorSalinas-Pérez, José A
dc.contributor.authorUriarte-Uriarte, Jose J
dc.contributor.authorIruin-Sanz, Alvaro
dc.contributor.authorGarcía-Alonso, Carlos R
dc.date.accessioned2023-01-25T10:07:26Z
dc.date.available2023-01-25T10:07:26Z
dc.date.issued2018-04-25
dc.description.abstractDecision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.
dc.identifier.doi10.1186/s12961-018-0308-y
dc.identifier.essn1478-4505
dc.identifier.pmcPMC5922302
dc.identifier.pmid29695248
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5922302/pdf
dc.identifier.unpaywallURLhttps://doi.org/10.1186/s12961-018-0308-y
dc.identifier.urihttp://hdl.handle.net/10668/12393
dc.issue.number1
dc.journal.titleHealth research policy and systems
dc.journal.titleabbreviationHealth Res Policy Syst
dc.language.isoen
dc.organizationIMIBIC
dc.page.number35
dc.pubmedtypeJournal Article
dc.rightsAttribution 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDecision support systems
dc.subjectEvidence-informed policy planning
dc.subjectExpert knowledge
dc.subjectExpert-based collaborative analysis
dc.subjectHealth systems engineering
dc.subjectInteractive visual data mining
dc.subjectKey performance indicator
dc.subjectMental health system
dc.subjectSelf-organising map network
dc.subject.meshAlgorithms
dc.subject.meshDecision Making
dc.subject.meshDecision Support Techniques
dc.subject.meshEvidence-Based Practice
dc.subject.meshHealth Planning
dc.subject.meshHumans
dc.subject.meshKnowledge
dc.subject.meshMental Health
dc.subject.meshMental Health Services
dc.subject.meshNeural Networks, Computer
dc.subject.meshPolicy
dc.subject.meshRegional Health Planning
dc.subject.meshSpain
dc.subject.meshSystems Analysis
dc.subject.meshTechnology
dc.titleUse of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number16
dspace.entity.typePublication

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