RT Journal Article
T1 Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.
A1 Chung, Younjin
A1 Salvador-Carulla, Luis
A1 Salinas-Pérez, José A
A1 Uriarte-Uriarte, Jose J
A1 Iruin-Sanz, Alvaro
A1 García-Alonso, Carlos R
K1 Decision support systems
K1 Evidence-informed policy planning
K1 Expert knowledge
K1 Expert-based collaborative analysis
K1 Health systems engineering
K1 Interactive visual data mining
K1 Key performance indicator
K1 Mental health system
K1 Self-organising map network
AB Decision-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.
YR 2018
FD 2018-04-25
LK http://hdl.handle.net/10668/12393
UL http://hdl.handle.net/10668/12393
LA en
DS RISalud
RD Apr 18, 2025