Título 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)
Publicación externa No
Medio PLoS ONE
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 1
Impacto JCR 3.70000
Impacto SJR 0.88500
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126894996&doi=10.1371%2fjournal.pone.0265669&partnerID=40&md5=52ac76ba4095abeeadb18c718ab177ee
Fecha de publicacion 22/03/2022
ISI 000834238000033
Scopus Id 2-s2.0-85126894996
DOI 10.1371/journal.pone.0265669
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. Copyright: © 2022 García-Alonso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Palabras clave adult; article; artificial intelligence; Basque Country; benchmarking; catchment area; day care; decision making; decision support system; ecosystem; entropy; hospital patient; hospital readmission; h
Miembros de la Universidad Loyola

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