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

Abstract

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.

Service availability and capacity in rural mental health in Australia: Analysing gaps using an Integrated Mental Health Atlas

“…This study is intended to improve information on the local healthcare ecosystem, defined as a network of interrelated sub-systems such as natural and socioeconomic contexts, providers, organisations, health suppliers, regulations and consumers in a specific region (Serbanati et al, 2011). Comparison across jurisdictions and countries using the same method, terminology and tools (Chung et al, 2018; Gutiérrez-Colosía et al, 2019; Sadeniemi et al, 2018; Salinas-Perez et al, 2018) has favoured the modelling of local efficiency and resource allocation tailored to the particular needs of every area in urban planning (García-Alonso et al, 2019). In future, the extension of comparative research on local patterns of service delivery and resource utilisation in rural areas should facilitate the development of specific models of rural mental health care, replace the traditional ‘ one size fits all ’ approach to rural health by tailored local planning and enable a better analysis of factors related to geographical accessibility such as out-of-pocket costs of transportation, equity of access, the impact of distance on help-seeking behaviour, and social adherence and fragmentation.…”

Patterns of Service Provision in Older People’s Mental Health Care in Australia

“…In any case the use of this information requires incorporating experts to improve, interpret and refine the results and provide better estimates [ 51 ]. This involves a multistep process where the standard description of the context, the patterns of care provision and utilisation, and the patterns of interventions and connections constitute the key components of the prior knowledge base for designing scenarios and running the model [ 18 ]. Furthermore, funding this type of research is even more complicated when we compare health systems instead of services or organisations.…”

“…National and international comparisons are hindered by ambiguity and inconsistency in service definition and description; differences in organisational structure and complexity of service networks; and differences in the definition of the target population [ 17 ]. Hence using a common assessment and coding system allows harmonisation of service data, and can inform equitable allocation of care resources, programmes, and treatments across different health districts, as well as facilitating linkages of health networks [ 18 , 19 ].…”

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

“…This approach involves applying systems thinking to better understand public health challenges and identifying desirable collective actions. This kind of approach has been adopted for health planning in both Scotland [25] and the Basque Country in Spain [26] . It uses healthcare ecosystem methods to analyse, monitor and guide decision making.…”