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.

[Everything is on the map: Integrated Mental Health Atlases as support tools for service planning. SESPAS Report 2020]

OBJECTIVE

This article reviews the usability of the Integrated Atlases of Mental Health as a decision support tool for service planning following a health ecosystem research approach.

METHOD

This study describes the types of atlases and the procedure for their development. Atlases carried out in Spain are presented and their impact in mental health service planning is assessed. Atlases comprise information on the local characteristics of the health care system, geographical availability of resources collected with the DESDE-LTC instrument and their use. Atlases use geographic information systems and other visualisation tools. Atlases follow a bottom-up collaborative approach involving decision-makers from planning agencies for their development and external validation.

RESULTS

Since 2005, Integrated Atlases of Mental Health have been developed for nine regions in Spain comprising over 65% of the Spanish inhabitants. The impact on service planning has been unequal for the different regions. Catalonia, Biscay and Gipuzkoa, and Andalusia reach the highest impact. In these areas, health advisors have been actively involved in their co-design and implementation in service planning.

CONCLUSIONS

Atlases allow detecting care gaps and duplications in care provision; monitoring changes of the system over time, and carrying out national and international comparisons, efficiency modelling and benchmarking. The knowledge provided by atlases could be incorporated to decision support systems in order to support an efficient mental health service planning based on evidence-informed policy.

Standardised description of health and social care: A systematic review of use of the ESMS/DESDE (European Service Mapping Schedule/Description and Evaluation of Services and DirectoriEs)

BACKGROUND

Evidence-informed planning and interpretation of research results both require standardised description of local care delivery context. Such context analysis descriptions should be comparable across regions and countries to allow benchmarking and organizational learning, and for research findings to be interpreted in context. The European Service Mapping Schedule (ESMS) is a classification of adult mental health services that was later adapted for the assessment of health and social systems research (Description and Evaluation of Services and DirectoriEs - DESDE). The aim of the study was to review the diffusion and use of the ESMS/DESDE system in health and social care and its impact in health policy and decision-making.

METHOD

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (1997-2018).

RESULTS

Out of 155 papers mentioning ESMS/DESDE, 71 have used it for service research and planning. The classification has been translated into eight languages and has been used by seven international research networks. Since 2000, it has originated 11 instruments for health system research with extensive analysis of their metric properties. The ESMS/DESDE coding system has been used in 585 catchment areas in 34 countries for description of services delivery at local, regional and national levels.

CONCLUSIONS

The ESMS/DESDE system provides a common terminology, a classification of care services, and a set of tools allowing a variety of aims to be addressed in healthcare and health systems research. It facilitates comparisons across and within countries for evidence-informed planning.

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain)

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.

Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review

Mental health services and systems (MHSS) are characterized by their complexity. Causal modelling is a tool for decision-making based on identifying critical variables and their causal relationships. In the last two decades, great efforts have been made to provide integrated and balanced mental health care, but there is no a clear systematization of causal links among MHSS variables. This study aims to review the empirical background of causal modelling applications (Bayesian networks and structural equation modelling) for MHSS management. The study followed the PRISMA guidelines (PROSPERO: CRD42018102518). The quality of the studies was assessed by using a new checklist based on MHSS structure, target population, resources, outcomes, and methodology. Seven out of 1847 studies fulfilled the inclusion criteria. After the review, the selected papers showed very different objectives and subjects of study. This finding seems to indicate that causal modelling has potential to be relevant for decision-making. The main findings provided information about the complexity of the analyzed systems, distinguishing whether they analyzed a single MHSS or a group of MHSSs. The discriminative power of the checklist for quality assessment was evaluated, with positive results. This review identified relevant strategies for policy-making. Causal modelling can be used for better understanding the MHSS behavior, identifying service performance factors, and improving evidence-informed policy-making.

Gender Differences in Psychological Well-Being and Health Problems among European Health Professionals: Analysis of Psychological Basic Needs and Job Satisfaction

Background: The aim was to examine the mediating role of basic psychological needs and job satisfaction in the relationship between the gender effect on health problems and psychological well-being for health professionals in Europe in 2015. Methods: Two multiple partial mediation analyses were conducted in order to test the partial mediation of both basic needs and job satisfaction, with gender as the independent variable and health problems or well-being, respectively, as the dependent variables, with a sample of health professionals. Results: Women reported lower psychological well-being and more health problems than men. The total effect of gender on both well-being and health problems was found to be significant. Regarding multiple mediation analyses: (a) the effect of gender on well-being was fully mediated by global basic need satisfaction and job satisfaction, such that gender did not present a significant direct effect and (b) the effect of gender on health problems was partially mediated by global basic need satisfaction and job satisfaction, such that the direct effect remained significant. Conclusions: The fulfillment of basic needs for autonomy, competence, and relatedness, as postulated within self-determination theory, was hypothesized to play a mediating role in the relationship between gender and well-being. Since significant gender differences in basic need satisfaction were observed, such a mediator should be controlled in order to achieve a significant relationship between gender and well-being when basic needs comes into play. The current study adds to the research emphasizing the need for satisfaction as a promising mechanism underlying for female health professionals' well-being.

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.

Electronic supplementary material

The online version of this article (10.1186/s12961-018-0308-y) contains supplementary material, which is available to authorized users.

Identification and location of hot and cold spots of treated prevalence of depression in Catalonia (Spain)

Background

Spatial analysis is a relevant set of tools for studying the geographical distribution of diseases, although its methods and techniques for analysis may yield very different results. A new hybrid approach has been applied to the spatial analysis of treated prevalence of depression in Catalonia (Spain) according to the following descriptive hypotheses: 1) spatial clusters of treated prevalence of depression (hot and cold spots) exist and, 2) these clusters are related to the administrative divisions of mental health care (catchment areas) in this region.

Methods

In this ecological study, morbidity data per municipality have been extracted from the regional outpatient mental health database (CMBD-SMA) for the year 2009. The second level of analysis mapped small mental health catchment areas or groups of municipalities covered by a single mental health community centre. Spatial analysis has been performed using a Multi-Objective Evolutionary Algorithm (MOEA) which identified geographical clusters (hot spots and cold spots) of depression through the optimization of its treated prevalence. Catchment areas, where hot and cold spots are located, have been described by four domains: urbanicity, availability, accessibility and adequacy of provision of mental health care.

Results

MOEA has identified 6 hot spots and 4 cold spots of depression in Catalonia. Our results show a clear spatial pattern where one cold spot contributed to define the exact location, shape and borders of three hot spots. Analysing the corresponding domain values for the identified hot and cold spots no common pattern has been detected.

Conclusions

MOEA has effectively identified hot/cold spots of depression in Catalonia. However these hot/cold spots comprised municipalities from different catchment areas and we could not relate them to the administrative distribution of mental care in the region. By combining the analysis of hot/cold spots, a better statistical and operational-based visual representation of the geographical distribution is obtained. This technology may be incorporated into Decision Support Systems to enhance local evidence-informed policy in health system research.