Title A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain)
Authors GARCÍA ALONSO, CARLOS, ALMEDA MARTÍNEZ, NEREA MARÍA, SALINAS PÉREZ, JOSÉ ALBERTO, RUIZ GUTIÉRREZ COLOSIA, MENCIA, Uriarte-Uriarte, Jose J., Salvador-Carulla, Luis, RUIZ GUTIÉRREZ COLOSIA, MENCIA, GARCÍA ALONSO, CARLOS, ALMEDA MARTÍNEZ, NEREA MARÍA, SALINAS PÉREZ, JOSÉ ALBERTO
External publication No
Means PLoS ONE
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 1
JCR Impact 2.74000
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061531881&doi=10.1371%2fjournal.pone.0212179&partnerID=40&md5=7d695f49f21ceb8430710508558181e2
Publication date 14/02/2019
ISI 000458763900048
Scopus Id 2-s2.0-85061531881
DOI 10.1371/journal.pone.0212179
Abstract Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.
Keywords article; Basque Country; community care; day care; decision support system; ecosystem health; entropy; health workforce; human; human experiment; mental health; Monte Carlo method; resource allocation
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