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BackgroundDecision-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.MethodsWe 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.ResultsThe 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).ConclusionsThis 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.Electronic supplementary materialThe online version of this article (10.1186/s12961-018-0308-y) contains supplementary material, which is available to authorized users.

Objective To analyze the results of a 6‐month telephone follow‐up program for the prevention of suicidality in adult patients discharged from three general hospitals after a suicide attempt. Results are compared with traditional programs lasting 12 months or more. Methods This is a prospective, multicenter, study with the control group. Patients in the intervention group received five protocolized telephone calls which were added to their usual treatment. Those in the control group only received usual treatment. Each patient was followed up for 12 months. Results A total of 123 patients were included in the intervention group and 463 in the control group. 57.7% received at least three calls. Patients in the intervention group took longer to perform a reattempt (p = .05). The percentage of those who did a reattempt (p = .67) and the number of reattempts per patient (p = .66) did not differ between groups. Those in the intervention group showed higher percentages of adherence to the outpatient follow‐up (p < .001). Conclusion The intervention was well accepted and showed improved percentages of adherence to outpatient follow‐up; however, the results in the prevention of suicidality were worse than those obtained by programs lasting 12 months or more. It is advisable to maintain the telephone follow‐up for a minimum of 12 months.