How is evaluated mental health recovery?: A systematic review.

There is an increasingly recognition of the concept of personal recovery in the treatment of mental illness. Recovery defined as living a fulfilling, rewarding life, even in the ongoing presence of a mental illness. Consequently, a number of different instruments have been designed to assess recovery-oriented outcomes. The objective of the study was to conduct a systematic revision of the domains and the instruments used to assess personal recovery and mental health services orientation to recovery. After the systematic review, it has been carried out a selection process of the most adequate instruments taking into account different criteria of adequacy, psychometric properties and the validation to the Spanish population. In the results have been obtained 35 instruments for measuring personal recovery and 18 for assessing the orientation of recovery in mental health services. However, many of them have been dismissed for not reaching the adequacy criteria. This review makes clear the lack of consensus on the concept of recovery, as a consequence of the high number of instruments that evaluate the same concept through different domains. In addition, few instruments offer data related to the psychometric properties and only one instrument to assess personal recovery is validated to the Spanish population.

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.

Oral health and the symptoms of schizophrenia.

The objectives of this study were to evaluate the oral health of a group of schizophrenic outpatients and a control group without psychiatric illness. The study also aimed to assess the influence of positive and negative symptomatology on oral health among outpatients with schizophrenia. The DMF-T Index (sum of decayed, missing and filled teeth) and the Community Periodontal Index of Treatment Needs (CPITN) were assessed in both groups. We evaluated the psychopathological state of the patient group using the Positive and Negative Syndrome Scale (PANSS). The schizophrenic patients had higher scores than the control group with respect to decayed teeth (4.39 vs. 0.72), missing teeth (5.66 vs. 1.50), the DMF-T index (13.51 vs. 7.8) and CPITN (2.32 vs. 1.04); and lower scores for filled teeth (3.53 vs. 5.54). The PANSS negative subscale score correlated positively with the oral health variables studied, whereas the PANSS positive subscale score correlated negatively and exclusively with the number of missing teeth. Age and smoking status affected oral health in both groups, but even when the influence of these factors was considered, the oral health of the patients was poorer than that of the control group.

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.