A framework for evidence-based mental health care and policy

Impact of the workforce allocation on the technical performance of mental health services: the collective case of Helsinki-Uusimaa (Finland)

BACKGROUND

Long-term mental health (MH) policies in Finland aimed at investing in community care and promoting reforms have led to a reduction in the number of psychiatric hospital beds. However, most resources are still allocated to hospital and community residential services due to various social, economic and political factors. Despite previous research focussing on the number and cost of these services, no study has evaluated the emerging patterns of use, their technical performance and the relationship with the workforce structure.

OBJECTIVE

The purpose of this study was to observe the patterns of use and their technical performance (efficiency) of the main types of care of MH services in the Helsinki-Uusimaa region (Finland), and to analyse the potential relationship between technical performance and the corresponding workforce structure.

METHODS

The sample included acute hospital residential care, non-hospital residential care and outpatient care services. The analysis was conducted using regression analysis, Monte Carlo simulation, fuzzy inference and data envelopment analysis.

RESULTS

The analysis showed a statistically significant linear relationship between the number of service users and the length of stay, number of beds in non-hospital residential care and number of contacts in outpatient care services. The three service types displayed a similar pattern of technical performance, with high relative technical efficiency on average and a low probability of being efficient. The most efficient acute hospital and outpatient care services integrated multidisciplinary teams, while psychiatrists and nurses characterized non-hospital residential care.

CONCLUSIONS

The results indicated that the number of resources and utilization variables were linearly related to the number of users and that the relative technical efficiency of the services was similar across all types. This suggests homogenous MH management with small variations based on workforce allocation. Therefore, the distribution of workforce capacity should be considered in the development of effective policies and interventions in the southern Finnish MH system.

Magnitude of terminological bias in international health services research: a disambiguation analysis in mental health

AIMS

Health services research (HSR) is affected by a widespread problem related to service terminology including non-commensurability (using different units of analysis for comparisons) and terminological unclarity due to ambiguity and vagueness of terms. The aim of this study was to identify the magnitude of the terminological bias in health and social services research and health economics by applying an international classification system.

METHODS

This study, that was part of the PECUNIA project, followed an ontoterminology approach (disambiguation of technical and scientific terms using a taxonomy and a glossary of terms). A listing of 56 types of health and social services relevant for mental health was compiled from a systematic review of the literature and feedback provided by 29 experts in six European countries. The disambiguation of terms was performed using an ontology-based classification of services (Description and Evaluation of Services and DirectoriEs - DESDE), and its glossary of terms. The analysis focused on the commensurability and the clarity of definitions according to the reference classification system. Interrater reliability was analysed using κ.

RESULTS

The disambiguation revealed that only 13 terms (23%) of the 56 services selected were accurate. Six terms (11%) were confusing as they did not correspond to services as defined in the reference classification system (non-commensurability bias), 27 (48%) did not include a clear definition of the target population for which the service was intended, and the definition of types of services was unclear in 59% of the terms: 15 were ambiguous and 11 vague. The κ analyses were significant for agreements in unit of analysis and assignment of DESDE codes and very high in definition of target population.

CONCLUSIONS

Service terminology is a source of systematic bias in health service research, and certainly in mental healthcare. The magnitude of the problem is substantial. This finding has major implications for the international comparability of resource use in health economics, quality and equality research. The approach presented in this paper contributes to minimise differentiation between services by taking into account key features such as target population, care setting, main activities and type and number of professionals among others. This approach also contributes to support financial incentives for effective health promotion and disease prevention. A detailed analysis of services in terms of cost measurement for economic evaluations reveals the necessity and usefulness of defining services using a coding system and taxonomical criteria rather than by 'text-based descriptions'.

Rapid response to crisis: Health system lessons from the active period of COVID-19

BACKGROUND

This paper outlines the need for a health systems approach and rapid response strategy for gathering information necessary for policy decisions during pandemics and similar crises. It suggests a new framework for assessing the phases of the pandemic.

METHOD

The paper draws its information and conclusions from a rapid synthesis and translation process (RSTP) of a series of webinars and online discussions from the Pandemic-Mental Health International Network (Pan-MHIN) - policy experts from across 16 locations in Australia, Denmark, Italy, Spain, Taiwan, the UK and the USA. While the initial focus of this research was on mental health, COVID-19 has raised much broader issues and questions for health planners.

RESULTS

We identified gaps affecting the capacity to respond effectively and quickly, including in relation to system indicators, the inadequacy of the prior classification of the phases of the pandemic, the absences of a healthcare ecosystem approach, and the quick shift to digital technologies. The strengths and weaknesses of COVID-19 responses across different systems, services, sites and countries been identified and compared, including both low and high impacted areas.

CONCLUSIONS

There is an urgent need for managerial epidemiology based on healthcare ecosystem research encompassing multidisciplinary teams, visualization tools and decision analytics for rapid response. Policy and healthcare context played a key role in the response to COVID-19. Its severity, the containment measures and the societal response varied greatly across sites and countries. Understanding this variation is vital to assess the impact of COVID-19 in specific areas such as ageing or mental health.

Experts' perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study

BACKGROUND

Health experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making.

METHODS

Health expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts' responses were analysed using statistical and text mining approaches.

RESULTS

The forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses.

CONCLUSIONS

This exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare.

Regional planning for meaningful person-centred care in mental health: context is the signal not the noise

Person-centred care is at the core of a value-based health system. Its transformative potential is to enable and support key policy, planning and service developments across the system even when these go against the self-interest of individual major players. It offers a potent test for decision makers at all levels. It demands responses that are multi-level, empirically grounded, expert-informed and data-driven that must converge on the singularity of individuals in the places that they live. This requires different approaches that recognise, respect and reconcile two necessary but constitutionally disparate perspectives: the bureaucratic, overtly decontextualised, top-down, policy and planning objectives of central governments and the formally complex, dynamic and contextualised experience of individuals in the system. Conflating the latter with the former can lead unwittingly to a pervasive and reductive form of quasi-Taylorism that nearly always creates waste at the expense of value. This has parallel application in the treatment domain where outcomes are non-randomly clustered and partitioned by socioeconomic status, amplifying unwarranted variation by place that is striking in its magnitude and heterogeneity. In this paper, we propose that a combination of (1) relevant, local and sophisticated data planning, collection and analysis systems, (2) more detailed person-centred service planning and delivery and (3) system accountability through co-design and transparent public reporting of health system performance in a manner that is understandable, relevant, and locally applicable are all essential in ensuring planned and provided care is most appropriate to more than merely the 'average' person for whom the current system is built. We argue that only through a greater appreciation of healthcare as a complex adaptive (eco)system, where context is everything, and then utilising planning, analysis and management methodologies that reflect this reality is the way to achieve genuine person-centred care.

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.

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.

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

Background

The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS).

Methods

The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or “Basic Stable Input of Care” (BSIC), coded by its principal function or “Main Type of Care” (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal).

Results

DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making.

Conclusion

DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

The mental health system in Lombardy, Italy: access to services and patterns of care

PURPOSE

The psychiatric reform in Italy devolved to the regions the responsibility of implementing community psychiatric care. The aim of this paper is to evaluate the mental health system in Lombardy by assessing changes in accessibility and patterns of care occurred between 1999 and 2009.

METHODS

Data on mental health services were collected through the regional mental health information system and analyzed in terms of treated prevalence, treated incidence, continuity of care and packages of care.

RESULTS

Both treated incidence and treated prevalence in Lombardy increased between 1999 and 2009. There was an increasing access to psychiatric services of people with a better social integration. Incidence of schizophrenic and personality disorders decreased and that of affective and neurotic disorders increased dramatically, while increase in prevalence concerned all diagnostic groups. The percentage of patients in continuous care remained stable and was generally low. The majority of cases, even those with schizophrenia, are cared for on outpatient basis. The percentage of patients receiving integrated multiprofessional care declined. Rates of admission to inpatient services remained low and within the inpatient sector a shift from hospital towards residential care emerged, with decreasing hospital utilization and an increase in size of patient population entering community residences. Treatment gap is still a problem in schizophrenic disorders.

CONCLUSIONS

The Lombardy mental health system is strongly based on community care. However, it is reaching a turning point and it needs to be improved in some key areas: the shifting balance towards the care of common mental disorders, in the absence of resource allocations targeted to severely mentally ill, may hinder the system ability to deal with more disabled people. A focus on early intervention and an improvement of continuity of care for people with severe mental disorder, by strengthening community teams, is a priority.

Economic context analysis in mental health care. Usability of health financing and cost of illness studies for international comparisons

This paper discusses an integrated approach to mental health studies on Financing of Illness (FoI) and health accounting, Cost of Illness (CoI) and Burden of Disease (BoD). In order to expand the mental health policies, the following are suggested: (a) an international consensus on the standard scope, methods to collect and to analyse mental health data, as well as to report comparative information; (b) mathematical models are also to be validated and tested in an integrated approach, (c) a better knowledge transfer between clinicians and knowledge engineers, and between researchers and policy makers to translate economic analysis into practice and health planning.

A preliminary taxonomy and a standard knowledge base for mental-health system indicators in Spain

Background

There are many sources of information for mental health indicators but we lack a comprehensive classification and hierarchy to improve their use in mental health planning. This study aims at developing a preliminary taxonomy and its related knowledge base of mental health indicators usable in Spain.

Methods

A qualitative method with two experts panels was used to develop a framing document, a preliminary taxonomy with a conceptual map of health indicators, and a knowledge base consisting of key documents, glossary and database of indicators with an evaluation of their relevance for Spain.

Results

A total of 661 indicators were identified and organised hierarchically in 4 domains (Context, Resources, Use and Results), 12 subdomains and 56 types. Among these the expert panels identified 200 indicators of relevance for the Spanish system.

Conclusions

The classification and hierarchical ordering of the mental health indicators, the evaluation according to their level of relevance and their incorporation into a knowledge base are crucial for the development of a basic list of indicators for use in mental health planning.

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

BACKGROUND

Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved.

METHOD

This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases.

RESULTS

EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases.

DISCUSSION

This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.

Comparison of therapeutic action, style and content in cognitive-behavioural and psychodynamic group therapy under clinically representative conditions

It is still an open question whether psychotherapists adhere to their therapeutic conceptions in routine practice (clinician's treatment adherence) and thus to what extent the two most common approaches, cognitive-behavioural (CBT) and psychodynamic therapy (PDT), differ from each other as theoretically expected (treatment differentiation). This holds true especially in case of group therapy.The study compares essential process components of CBT and PDT group treatments under clinically representative conditions using non-participating observer ratings. Results demonstrate that CBT group therapists use more cognitive, behavioural and psychoeducational strategies, foster self-efficacy to a larger extent and are more supporting and empathetic. PDT group therapists use more interpretative and confrontative interventions and focus on interactional and dynamic aspects. The results strongly support that not only in individual psychotherapy-as shown in prior research-but also in the group setting do CBT and PDT reveal very distinct profiles and that therapists primarily abide by their theoretical training also in clinical practice. They allow one to identify differential process components of the group setting and to trace back parameters of outcome to the process of CBT and PDT for clinical routines.

Development and spatial representation of synthetic indexes of outpatient mental health care in Andalusia (Spain)

INTRODUCTION

There is a need to develop composite indicators to monitor mental health care in countries such as Spain, where there is wide variability of care systems in 17 different regions. The aim of this study is to generate and to test the usability of synthetic indexes in Andalusia (Southern Spain).

METHOD

Seven mental health indicators were selected by expert opinion from a previous list of simple indicators used to compare mental health care systems across Spain (Psicost-74). A Geographical Information Systems (GIS) was used to delineate 71 sectors based on the catchment areas of the mental health centers in Andalusia. Synthetic indexes were obtained through linear combinations of simple indicators via Principal Components Analysis (PCA), using activity data from the Mental Health Information System of Andalusia (SISMA). Maps of these indexes were drawn for 71 catchment areas.

RESULTS

Two synthetic indexes were obtained and showed high consistency in the PCA. The Care Load Index (component 1) related to population size and total outpatient care provided within the area. The Case Load Index (component 2) related to assisted morbidity in relation to the population size. The care load index was higher in populated urban areas, whereas the case load was higher in rural areas.

DISCUSSION

Care and case load indexes show a different pattern in urban and rural areas. This may be related to a different underlying model of care related to the degree of urbanisation. Geographical Information Systems (GIS) improved recognition and assessment of the spatial phenomena related to the mental health care system, and support policy decision making process in mental health.

Assessing the outcome of community-based psychiatric care: building a feedback loop from 'real world' health services research into clinical practice

OBJECTIVE

To describe the main characteristics of the South-Verona Outcome Project (SVOP) and to focus on its overall conceptual framework, with specific reference to the following perspectives: i) integrating evidence-based and practice-based approaches; ii) involving service professionals in routine outcome assessment; and iii) involving service users in mental health outcome assessment.

METHOD

A selective literature review of methodological and empirical papers addressing the relevance and usefulness of outcome research to routine clinical practice was performed.

RESULTS

Reviewed literature shows the need to integrate evidence-based and practice-based approaches and to involve service professionals in routine outcome assessment, by adopting a multiple perspective paradigm. Studies conducted in 'real world' health services indicate that the outcome of care is multifaceted and it can be perceived differently when different perspectives are taken into account. Such a complex picture can provide more comprehensive information on the effectiveness of care provided, to feed back positively into clinical practice.

CONCLUSION

The SVOP design and its methodological background were demonstrated to be appropriate for a detailed and routine assessment of outcome in the 'real world' of mental health services.