Título Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem
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-s2.0-85122620920&doi=10.1371%2fjournal.pone.0261621&partnerID=40&md5=f9cf5b300b70ad44938ff22907c0eeac
Fecha de publicacion 11/01/2022
ISI 000814820600004
Scopus Id 2-s2.0-85122620920
DOI 10.1371/journal.pone.0261621
Abstract Major efforts worldwide have been made to provide balanced Mental Health (MH) care. Any integrated MH ecosystem includes hospital and community-based care, highlighting the role of outpatient care in reducing relapses and readmissions. This study aimed (i) to identify potential expert-based causal relationships between inpatient and outpatient care variables, (ii) to assess them by using statistical procedures, and finally (iii) to assess the potential impact of a specific policy enhancing the MH care balance on real ecosystem performance. Causal relationships (Bayesian network) between inpatient and outpatient care variables were defined by expert knowledge and confirmed by using multivariate linear regression (generalized least squares). Based on the Bayesian network and regression results, a decision support system that combines data envelopment analysis, Monte Carlo simulation and fuzzy inference was used to assess the potential impact of the designed policy. As expected, there were strong statistical relationships between outpatient and inpatient care variables, which preliminarily confirmed their potential and a priori causal nature. The global impact of the proposed policy on the ecosystem was positive in terms of efficiency assessment, stability and entropy. To the best of our knowledge, this is the first study that formalized expert-based causal relationships between inpatient and outpatient care variables. These relationships, structured by a Bayesian network, can be used for designing evidence-informed policies trying to balance MH care provision. By integrating causal models and statistical analysis, decision support systems are useful tools to support evidence-informed planning and decision making, as they allow us to predict the potential impact of specific policies on the ecosystem prior to its real application, reducing the risk and considering the population’s needs and scientific findings. Copyright: © 2022 Almeda 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; Bayesian network; causal model; controlled study; decision support system; ecosystem; entropy; hospital patient; human; least square analysis; linear regression analysis; mental health
Miembros de la Universidad Loyola

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