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)

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

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning

Background: Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. Methods: We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. Results: The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). Conclusions: This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice

Psicothema

Resumen tomado de la publicaciónPrevención del deterioro cognitivo en la esquizofrenia crónica: eficacia a largo plazo de la terapia psicológica integrada y el entrenamiento en manejo de emociones. Antecedentes: los programas de tratamiento psicológico grupal eficaces para las fases crónicas de la esquizofrenia son escasos. Este artículo describe los resultados obtenidos tras la aplicación de un programa grupal, que incluye la terapia psicológica integrada (IPT) junto con una adaptación de la terapia de manejo emocional (EMT), en una muestra de pacientes ambulatorios con esquizofrenia crónica. Método: 42 pacientes recibieron el programa durante ocho meses y se evaluaron al inicio, en el post-tratamiento y en los seguimientos de 1, 3, 6 y 12 meses. Resultados: el programa fue bien aceptado ya que únicamente hubo un abandono durante los 8 meses de tratamiento y 2 ingresos hospitalarios durante los 20 meses de duración del estudio. Se obtuvieron mejorías en la cognición, el funcionamiento social y la calidad de vida tras recibir el tratamiento, y éstas se mantuvieron en el seguimiento a largo plazo. En resumen, los pacientes estaban mejor 12 meses después de recibir el tratamiento que en la evaluación inicial. Conclusión: el tratamiento resulta efectivo, ha sido bien aceptado y podría ser útil en los servicios de salud para reducir las hospitalizaciones, prevenir el deterioro cognitivo y ayudar a los pacientes a manejar sus preocupaciones diarias.Universidad de Oviedo. Biblioteca de Psicología; Plaza Feijoo, s/n.; 33003 Oviedo; Tel. +34985104146; Fax +34985104126; [email protected]

19th International Conference on Integrated Care, San Sebastian, 01-03 April 2019

Use of decision support systems may improve policy-making for management of mental health services and systems. System performance can be analyzed by using Relative Technical Efficiency (RTE), stability and entropy indicators. These indicators summarize resource availability, utilization and results as a balance between inputs (resources) and outputs (outcomes). Nevertheless, performance and stability assessment of mental health systems is complex because of difficulty related to data collection, results interpretation and translation of information into practice. The present mental health context requires better ways of planning for allocating resources and improving outcomes. The objective of this study is to assess the RTE, stability and entropy 2012-2015 variations as a consequence of a policy developed by the Mental Health Network of Gipuzkoa (Basque Country, Spain). The Mental Health Network of Gipuzkoa is structured by thirteen small health areas. In these catchment areas, mental health services were standardized by using the DESDE-LTC codification tool. Mental health services were classified according to the main type of care provided (outpatient, day and residential). In the analysis, 57 variables were included, which were classified in resources –inputs- (availability, placement and workforce capacity) and results –outputs- (service utilization, readmissions, discharges and length of stay). A hybrid decision support system, that integrates statistical, operational and artificial intelligence techniques, has been used to analyze the indicators. The main statistical procedure was a Monte- Carlo simulation engine to include the uncertainty of real contexts. The data envelopment analysis, an operational technique, was utilized to assess the RTE. In addition, a prototype of fuzzy inference engine was included for interpreting expert knowledge according to the basic community mental health care model. The stability was calculated by analyzing the frequency distributions of the RTE and, finally, the Shannon’s entropy to estimate system disorder. The main structure of the real policy was identified by developing structured interviews to senior managers and planners of Mental Health System of Gipuzkoa. Results provided information about the changes of the selected indicators throughout three years (2012-2015). The impact of the policy developed can be considered positive but the stability 2Almeda; Changes in mental health system performance: the case of Gipuzkoa (Basque Country, Spain) remains poor as new real interventions have to take into account that small changes in data values can result in a change, positive or negative, in the indicators´ value. The methodology presented can be considered appropriate for analyzing mental health services and systems performance. Variations in the indicator values can also be considered as a consequence of the policy impact and, because of that, the decision support system could analyze the evolution of the system. As future research, it is suggested to assess indicator variations throughout the time span and assess the impact of new organizational interventions and policies

Efectividad de una Intervención Psicoeducativa en la Reducción de la Sobrecarga del Cuidador Informal del Paciente con Esquizofrenia (EDUCA-III-OSA)

Los familiares de pacientes con esquizofrenia o trastorno esquizoafectivo frecuentemente padecen consecuencias negativas derivadas de su labor como cuidadores. El objetivo del estudio EDUCA-III-OSA es evaluar la efectividad de un programa de intervención psicoeducativa (PIP) en la reducción de la sobrecarga del cuidador informal tras la intervención a los 4 meses y 16 meses después. Se llevó a cabo un estudio multicéntrico con diseño cuasi-experimental de grupo único. La variable dependiente principal fue la sobrecarga, medida a través del Inventario de Sobrecarga de Zarit (ZBI) y el Cuestionario de Evaluación de Repercusión Familiar (IEQ). Las variables secundarias fueron la ansiedad (STAI-X), la salud mental del cuidador (GHQ-28) y la depresión (CES-D). 39 cuidadores de 5 centros diferentes participaron en el estudio. Tras la intervención (4 meses), las variables de sobrecarga (d de Cohen = 0.26), depresión (d = 0.42), salud mental (d = 0.76) y ansiedad-estado (d = 0.59) experimentaron una mejora moderada. Esta mejora se vio incrementada a los 16 meses en las variables de sobrecarga (d = 0.56) y ansiedad-estado (d = 0.89), mientras que la variable de salud mental experimentó un descenso (d = 0.39). Tras la aplicación de la intervención psicoeducativa manualizada se produjo una mejoría en el estado psicológico de los cuidadores informales. Estos cambios se mantuvieron un año después

Effectiveness of a psychoeducational intervention in reducing burden in informal caregivers of schizophrenic patients (EDUCA-III-OSA)

Families of patients with schizophrenia usually experience negative consequences. The aim of the EDUCA-III-OSA study is to test the effectiveness of a psychoeducational intervention program (PIP) to reduce the caregiver burden at post-intervention (4 months) and at follow-up (16 months). A multicentre quasi-experimental study design with a unique group of informal caregivers who received intervention was used. The intervention consisted of 12 weekly group sessions. The primary outcome variable was burden, measured through the Zarit Burden Interview (ZBI) and the Involvement Evaluation Questionnaire (IEQ). Secondary outcome variables were anxiety (STAI-X), mental health (GHQ-28), and depression (CES-D). 39 caregivers from 5 research sites participated in the study. After the intervention (4 months), the variables of burden (Cohen's d = 0.26), depression (d = 0.42), mental health (d = 0.76), and anxiety-state (d = 0.59) showed a moderate decrease. These improvements increased 16 months later in the variables of burden (d = 0.56) and anxiety-state (d = 0.89), while the mental health variable decreased (d = 0.39). After the application of the intervention program a marked improvement in the psychological status of informal caregivers was produced. These changes held one year later.Los familiares de pacientes con esquizofrenia o trastorno esquizoafectivo frecuentemente padecen consecuencias negativas derivadas de su labor como cuidadores. El objetivo del estudio EDUCA-III-OSA es evaluar la efectividad de un programa de intervención psicoeducativa (PIP) en la reducción de la sobrecarga del cuidador informal tras la intervención a los 4 meses y 16 meses después. Se llevó a cabo un estudio multicéntrico con diseño cuasi-experimental de grupo único. La variable dependiente principal fue la sobrecarga, medida a través del Inventario de Sobrecarga de Zarit (ZBI) y el Cuestionario de Evaluación de Repercusión Familiar (IEQ). Las variables secundarias fueron la ansiedad (STAI-X), la salud mental del cuidador (GHQ-28) y la depresión (CES-D). 39 cuidadores de 5 centros diferentes participaron en el estudio. Tras la intervención (4 meses), las variables de sobrecarga (d de Cohen = 0.26), depresión (d = 0.42), salud mental (d = 0.76) y ansiedad-estado (d = 0.59) experimentaron una mejora moderada. Esta mejora se vio incrementada a los 16 meses en las variables de sobrecarga (d = 0.56) y ansiedad-estado (d = 0.89), mientras que la variable de salud mental experimentó un descenso (d = 0.39). Tras la aplicación de la intervención psicoeducativa manualizada se produjo una mejoría en el estado psicológico de los cuidadores informales. Estos cambios se mantuvieron un año después

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

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 evidenceinformed 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