Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem

• Published in: PloS one 2022-01, Vol.17 (1), p.e0261621-e0261621
• Main Authors: Almeda, Nerea, Garcia-Alonso, Carlos R, Gutierrez-Colosia, Mencia R, Salinas-Perez, Jose A, Iruin-Sanz, Alvaro, Salvador-Carulla, Luis
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Confidence intervals; Continuum of care; Decision analysis; Decision making; Decision support systems; Design; Ecology and Environmental Sciences; Ecosystems; Entropy; Health Policy; Humans; Inpatients; Integrated delivery systems; Knowledge; Length of Stay; Mathematical models; Medical care; Medical policy; Medicine and Health Sciences; Mental health; Mental Health Services - standards; Models, Theoretical; Monte Carlo simulation; Physical Sciences; Policies; Psychiatric services; Quality management; Research and analysis methods; Risk reduction; Social Sciences; Spain; Stability analysis; Statistical analysis; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0261621

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.