Propuesta de un modelo de apoyo a la gestión de la Salud Mental

Resumen

Background: In the European Union, more than 4% of the expenses of the Gross Domestic Product, are a consequence of mental disorders. The prevalence is constantly increasing and places a severe burden on society at health management level, at the socio-economic level and with regards to human rights. Aims: This doctoral thesis is aimed at improving MH (MH) care by using decision support systems for designing evidence-informed interventions and policies. Methods: The thesis is presented as a compendium of publications. It includes 6 studies structured in 10 chapters. Chapters 3-8 show the scientific, social and political impact of the doctoral student contributions. The thesis meets criteria for doctorate with international mention. Chapters 9 and 10 show the discussion and the main conclusions. Results: Chapter 1 introduces the empirical background of the thesis: prevalence and costs of mental disorders, community MH care model, deinstitutionalization process, decision support systems and related techniques (relative technical efficiency, Bayesian networks and international codification of MH services). Chapter 2 shows the objectives and hypotheses of the doctoral thesis. Chapters 3 and 4 include the studies 1 and 2, which aimed at reviewing systematically the empirical background on MH services planning and management by assessing relative technical efficiency and by using causal modelling respectively. The published articles are entitled: Relative Technical Efficiency Assessment of Mental Health Services: A Systematic Review (García-Alonso, Almeda, Salinas-Pérez, Gutiérrez-Colosía, & Salvador-Carulla, 2019) and Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review (Almeda, García-Alonso, Salinas-Pérez, Gutiérrez-Colosía, & Salvador-Carulla, 2019). Chapter 5 (study 3) aimed at evaluating the performance of a set of small health areas by using a decision support system prototype which integrates data envelopment analysis, Monte-Carlo simulation and a knowledge-base for formalizing expert knowledge. The associated publication is entitled: Assessment of Relative Technical Efficiency of Small Mental Health Areas in Bizkaia (Basque Country, Spain) (Almeda, García-Alonso, Alberto Salinas-Pérez, Gutiérrez-Colosía, & Salvador-Carulla, 2017). In Chapter 6 (study 4) the main objective was to assess the impact of organizational interventions on the performance of a real MH system (Bizkaia). The published article is entitled: A decision support system for assessing management interventions in a Mental Health ecosystem: The case of Bizkaia (Basque Country, Spain) (García-Alonso, Almeda, Salinas-Pérez, Gutiérrez-Colosía, Uriarte-Uriarte, et al., 2019). Chapter 7 (study 5) aimed at assessing relative technical efficiency and stability of MH supported accommodation services in England. Chapter 8 (study 6) aimed at developing a global, integrated and integral MH care model, Bayesian network, for assessing performance indicators based on the community MH care model. In Chapter 9 and 10 gather a general discussion based on the 6 studies and the main conclusions. The articles included in this research have been published in indexed journals and scientific conferences. Conclusion: In order to improve MH care, it is required to develop evidence-informed policy-making based on population needs, characteristics of the environment, resources available and scientific background. Decision support systems are appropriate tools for balancing care provision, assessing service performance and evaluating policy impact. According to the studies developed in this thesis, relative technical efficiency and Bayesian networks can be included in decision support systems for assessing service performance. In addition, these tools allow assessing the impact of health policies on MH systems prior their implementation in the real environment. This fact contributes to increase expert knowledge and let them to make evidence-informed policy to improve MH management, and in consequence, to provide better MH care.