Introduction A mental health (MH) assertive community treatment (ACT) is always designed expecting for a decrease in the pressure (visits and readmissions) in inpatient services and to increase care quality. An appropriate management of ACT provision can be crucial to develop a balanced community-based MH ecosystems. Objectives To assess the impact of the ACT on the performance of the MH ecosystem of Bizkaia (Basque Country, Spain). Methods The ecosystem is structured by 19 MH areas, supported by 5 ACT teams. Here ACT provides high intensity mobile outpatient care to people suffering from severe mental disorders. The impact of these teams on the ecosystem performance was assessed by Monte-Carlo simulation, the Data Envelopment Analysis (DEA) and fuzzy inference. The input variables were the availability, number of psychiatrics, nurses and total of professionals of ACT services in each area. The outputs were: frequentation, incidence and prevalence of ACT services in each MH area. Performance indicators were: relative technical efficiency (RTE), statistical stability and entropy. Results The global ecosystem performance was high (RTE on average=0.799 -input DEA orientation- and 0.825 -output orientation- up to 1, the maximum), the stability was medium-low (respectively 38,67% and 13.64% up to 100%, the maximum) and the entropy was medium-high (respectively 70,41% and 65.9% up to 100%, the maximum). Conclusions Results highlighted a positive impact of ACT in Bizkaia. Nevertheless, stability and entropy levels showed the existence of a high structural variability in ACT services due to the necessity of adjusting them to the user’s specific needs. Disclosure No significant relationships.
Introduction Decision Support Systems (DSS) are appropriate tools for guiding policymaking processes in Mental Health (MH) management, especially where a balanced and integrated care provision is required. Objectives To assess the performance of a MH ecosystem for identifying benchmark and target-for-improvement catchment areas according to the Balanced Care model. Methods The MH provision, distinguishing inpatient, day and outpatient main types of care, has been assessed in the Mental Health Network of Gipuzkoa (Basque Country, Spain) using a DSS, integrating Data Envelopment Analysis, Monte-Carlo Simulation and Artificial Intelligence. 13 catchment areas, defined by a reference MH centre, are the units (universe) for the analysis. The indicators for MH ecosystem performance were: relative technical efficiency, stability and entropy, for identifying both benchmarking and target-for-improvement areas. The analysis of the differences between the two groups can be used to design organizational interventions. Results The Mental Health Network of Gipuzkoa showed high global efficiency scores, but it can be considered statistically unstable (small changes in variable values can have relevant impacts on its performance). For a global performance improvement, it is recommended to reduce admissions and readmissions in inpatient care, increase workforce capacity and utilization of day care services and, finally, increase the availability of outpatient care services. Conclusions This research offers a guide for evidence-informed policy-making to improve MH care provision in the main types of care and provide aftercare. The characteristics of the area to be improved are critical to design interventions and assess their potential impact on the MH ecosystem. Disclosure No significant relationships.
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