ArticlePloS one2022
Use of a decision support system for benchmarking analysis and organizational improvement of regional mental health care: Efficiency, stability and entropy assessment of the mental health ecosystem of Gipuzkoa (Basque Country, Spain).
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Precision medicine in mental health: applications, challenges, and recommendations.European psychiatry : the journal of the Association of European Psychiatrists · 2026Review
- Multivariate Patterns in Mental Health Burden and Psychiatric Resource Allocation in Europe: A Principal Component Analysis.Healthcare (Basel, Switzerland) · 2025Article
- Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Assessment of care provision integration in a community-based mental health system: balanced care model implementation in Andalusia (Spain).BMC public health · 2024Article
- Impact of the workforce allocation on the technical performance of mental health services: the collective case of Helsinki-Uusimaa (Finland).Health research policy and systems · 2023Article
- Patterns of mental healthcare provision in rural areas: A demonstration study in Australia and Europe.Frontiers in psychiatry · 2023Article
- Co-Transformation of Digital Health and eSport in Metaverse: Moderating Effects of Digital Personality on Mental Health in Multiplayer Online Battle Arena (MOBA).International journal of environmental research and public health · 2022Article
- Analysis of the mental health service provision in Qatar: Progressing in community mental health in a Middle East country.Frontiers in psychiatry · 2022Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Decision support systems are appropriate tools for guiding policymaking processes, especially in mental health (MH), where care provision should be delivered in a balanced and integrated way. This study aims to develop an analytical process for (i) assessing the performance of an MH ecosystem and (ii) identifying benchmark and target-for-improvement catchment areas. MH provision (inpatient, day and outpatient types of care) was analysed in the Mental Health Network of Gipuzkoa (Osakidetza, Basque Country, Spain) using a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence. The unit of analysis was the 13 catchment areas defined by a reference MH centre. MH ecosystem performance was assessed by the following indicators: relative technical efficiency, stability and entropy to guide organizational interventions. Globally, the MH system of Gipuzkoa showed high efficiency scores in each main type of care (inpatient, day and outpatient), but it can be considered unstable (small changes can have relevant impacts on MH provision and performance). Both benchmark and target-for-improvement areas were identified and described. This article provides a guide for evidence-informed decision-making and policy design to improve the continuity of MH care after inpatient discharges. The findings show that it is crucial to design interventions and strategies (i) considering the characteristics of the area to be improved and (ii) assessing the potential impact on the performance of the global MH care ecosystem. For performance improvement, it is recommended to reduce admissions and readmissions for inpatient care, increase workforce capacity and utilization of day care services and increase the availability of outpatient care services.
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