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.
Highlights
Decision Analysis (DA) is a technique to support decision making avoiding pitfalls
The recommendations proposed by the World Health Organization (2018) [2] for Mental Health (MH) policy development highlight the need to help policy makers to reach deeper knowledge on service planning, but traditional approaches focusing on systematic reviews of evidence, policy briefs and better accessibility to data [3] do not suffice to guide decision making
(3) On average calculated taking into consideration the Relative Technical Efficiency (RTE) average
Summary
Decision Analysis (DA) is a technique to support decision making avoiding pitfalls. DA aims to understand the decision-making processes, the factors involved in the way people make decisions and the procedures involved in choosing alternatives for maximizing the expected utility, the probability of achieving some specific goals or minimizing the decisional uncertainty [1].The recommendations proposed by the World Health Organization (2018) [2] for Mental Health (MH) policy development highlight the need to help policy makers to reach deeper knowledge on service planning, but traditional approaches focusing on systematic reviews of evidence, policy briefs and better accessibility to data [3] do not suffice to guide decision making. Decision Analysis (DA) is a technique to support decision making avoiding pitfalls. Decision makers might approximate the consequences of a policy plan or specific management interventions (e.g., reallocation of workforce capacity or beds) in a very limited way. The complexity, uncertainty, non-linearity, dimensionality and multiscalarity of the questions posed in mental healthcare planning [4] make it necessary to integrate an entire array of different disciplines, research fields and analysis techniques to develop usable and interoperable Decision Support Systems (DSS) in the real world. DSS are interactive computer-based tools for improving decision-making processes and guiding decision makers in semi-structured or, sometimes, unstructured problem solving [5,6]. The capacitation processes for guiding users to make decisions based on DSS results are always complex in dynamic environments [8,9]
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