scholarly journals Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261621
Author(s):  
Nerea Almeda ◽  
Carlos R. Garcia-Alonso ◽  
Mencia R. Gutierrez-Colosia ◽  
Jose A. Salinas-Perez ◽  
Alvaro Iruin-Sanz ◽  
...  
Keyword(s):  
Mental Health ◽  
Decision Support ◽  
Bayesian Network ◽  
Outpatient Care ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Generalized Least Squares ◽  
Causal Relationships ◽  
Potential Impact ◽  
Inpatient And Outpatient Care

Major efforts worldwide have been made to provide balanced Mental Health (MH) care. Any integrated MH ecosystem includes hospital and community-based care, highlighting the role of outpatient care in reducing relapses and readmissions. This study aimed (i) to identify potential expert-based causal relationships between inpatient and outpatient care variables, (ii) to assess them by using statistical procedures, and finally (iii) to assess the potential impact of a specific policy enhancing the MH care balance on real ecosystem performance. Causal relationships (Bayesian network) between inpatient and outpatient care variables were defined by expert knowledge and confirmed by using multivariate linear regression (generalized least squares). Based on the Bayesian network and regression results, a decision support system that combines data envelopment analysis, Monte Carlo simulation and fuzzy inference was used to assess the potential impact of the designed policy. As expected, there were strong statistical relationships between outpatient and inpatient care variables, which preliminarily confirmed their potential and a priori causal nature. The global impact of the proposed policy on the ecosystem was positive in terms of efficiency assessment, stability and entropy. To the best of our knowledge, this is the first study that formalized expert-based causal relationships between inpatient and outpatient care variables. These relationships, structured by a Bayesian network, can be used for designing evidence-informed policies trying to balance MH care provision. By integrating causal models and statistical analysis, decision support systems are useful tools to support evidence-informed planning and decision making, as they allow us to predict the potential impact of specific policies on the ecosystem prior to its real application, reducing the risk and considering the population’s needs and scientific findings.

scholarly journals A review of the applications for involuntary admissions made to the Mental Health Review Boards by institutions in Gauteng in 2008

2010 ◽  
Vol 16 (4) ◽  
pp. 6
Author(s):  
M Y H Moosa ◽  
F Y Jeenah
Keyword(s):  
Mental Health ◽  
Health Care Professionals ◽  
Outpatient Care ◽  
Inpatient Care ◽  
Psychiatric Hospitals ◽  
Department Of Health ◽  
Massive Scale ◽  
Inpatient And Outpatient Care ◽  
Health Review ◽  
Review Boards

<p><strong>Aim.</strong> To review applications for involuntary admissions made to the Mental Health Review Boards (MHRBs) by institutions in Gauteng.</p><p><strong>Method.</strong> A retrospective review of the register/database of the two review boards in Gauteng for the period January - December 2008. All applications for admissions (involuntary and assisted inpatient) and outpatient care (involuntary and assisted), and periodic reports for continued care (inpatient or outpatient care) were included.</p><p><strong>Results.</strong> During the study period the two MHRBs received a total of 3 803 applications for inpatient care, of which 2 526 were for assisted inpatient care (48.1% regional hospitals, 29.6% specialised psychiatric hospitals, 22.2% tertiary academic hospitals). Of the applications for involuntary inpatient care, 73.1% were from the specialised psychiatric hospitals (65.2% from Sterkfontein Hospital). Applications for outpatient care, treatment and rehabilitation (CTR) numbered 1 226 (92% assisted outpatient CTR). Although the health establishments in northern Gauteng applied for more outpatient CTR compared with those in southern Gauteng (879 v. 347, respectively), the ratios of assisted to involuntary outpatient applications for CTR for each region were similar (approximately 12:1 and 9:1, respectively). The boards received 3 805 periodic reports for prolonged CTR (93.5% inpatient, 6.5% outpatient), in the majority of cases for assisted CTR.</p><p><strong>Conclusion.</strong> The study suggests that in the 4 years since the promulgation of the MHCA in 2004 , there have been significant strides towards implementation of the procedures relating to involuntary admission and CTR by all stakeholders. Differences in levels of implementation by the various stakeholders may result from differences in knowledge, perceptions, attitudes and understanding of their roles and therefore indicate the need for education of mental health care professionals and the public on a massive scale. The Department of Health also needs to invest more funds to improve mental health human resources and infrastructure at all health establishments.</p>

Evaluating complex mental health care in outpatient care in rural Lower Saxony: an ecologic study

2011 ◽  
Vol 38 (S 01) ◽  
Author(s):  
A Bramesfeld ◽  
K Kopke ◽  
M Walle ◽  
J Radisch ◽  
D Büchtemann ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Outpatient Care ◽  
Lower Saxony ◽  
Ecologic Study

Childhood adversity studies as an antidote to the predominance of neo-liberal thinking in the field of mental health

2020 ◽  
Vol 29 (4) ◽  
pp. 556-563
Author(s):  
Adam Burley
Keyword(s):  
Mental Health ◽  
Childhood Adversity ◽  
Sole Source ◽  
Point Of View ◽  
Early Adversity ◽  
Potential Impact ◽  
The Individual

This is a personal and reflective piece written from a clinician's point of view on the influence that the developing awareness around the consequences of childhood adversity has had upon the discussions, thinking and practice across the areas in which they are working. It seeks to argue that the increased understanding and recognition of the potential impact of early adversity can not only enhance and deepen the understanding of an individual's difficulties, but can serve to inform how services respond in a way that takes account of this. It suggests that the research and literature on childhood adversity can offer a route map away from a model of mental health that focuses predominantly on the individual as the sole source of interest.

Elicitation of expert knowledge to develop decision support tools for use in water demand management planning

2007 ◽  
Vol 7 (5-6) ◽  
pp. 53-60
Author(s):  
D. Inman ◽  
D. Simidchiev ◽  
P. Jeffrey
Keyword(s):  
Decision Support ◽  
Water Demand ◽  
Expert Knowledge ◽  
Demand Management ◽  
Decision Processes ◽  
Conservation Strategy ◽  
Influence Diagrams ◽  
Decision Support Tools ◽  
Water Demand Management ◽  
Support Tools

This paper examines the use of influence diagrams (IDs) in water demand management (WDM) strategy planning with the specific objective of exploring how IDs can be used in developing computer-based decision support tools (DSTs) to complement and support existing WDM decision processes. We report the results of an expert consultation carried out in collaboration with water industry specialists in Sofia, Bulgaria. The elicited information is presented as influence diagrams and the discussion looks at their usefulness in WDM strategy design and the specification of suitable modelling techniques. The paper concludes that IDs themselves are useful in developing model structures for use in evidence-based reasoning models such as Bayesian Networks, and this is in keeping with the objectives set out in the introduction of integrating DSTs into existing decision processes. The paper will be of interest to modellers, decision-makers and scientists involved in designing tools to support resource conservation strategy implementation.

scholarly journals Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jo-An Occhipinti ◽  
Adam Skinner ◽  
Frank Iorfino ◽  
Kenny Lawson ◽  
Julie Sturgess ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Determinants ◽  
Suicide Attempts ◽  
Expert Knowledge ◽  
Population Level ◽  
Modelling And Simulation ◽  
Decision Makers ◽  
Public Health Issue ◽  
Coordinated Care ◽  
Systems Modelling

Abstract Background Reducing suicidal behaviour (SB) is a critical public health issue globally. The complex interplay of social determinants, service system factors, population demographics, and behavioural dynamics makes it extraordinarily difficult for decision makers to determine the nature and balance of investments required to have the greatest impacts on SB. Real-world experimentation to establish the optimal targeting, timing, scale, frequency, and intensity of investments required across the determinants is unfeasible. Therefore, this study harnesses systems modelling and simulation to guide population-level decision making that represent best strategic allocation of limited resources. Methods Using a participatory approach, and informed by a range of national, state, and local datasets, a system dynamics model was developed, tested, and validated for a regional population catchment. The model incorporated defined pathways from social determinants of mental health to psychological distress, mental health care, and SB. Intervention scenarios were investigated to forecast their impact on SB over a 20-year period. Results A combination of social connectedness programs, technology-enabled coordinated care, post-attempt assertive aftercare, reductions in childhood adversity, and increasing youth employment projected the greatest impacts on SB, particularly in a youth population, reducing self-harm hospitalisations (suicide attempts) by 28.5% (95% interval 26.3–30.8%) and suicide deaths by 29.3% (95% interval 27.1–31.5%). Introducing additional interventions beyond the best performing suite of interventions produced only marginal improvement in population level impacts, highlighting that ‘more is not necessarily better.’ Conclusion Results indicate that targeted investments in addressing the social determinants and in mental health services provides the best opportunity to reduce SB and suicide. Systems modelling and simulation offers a robust approach to leveraging best available research, data, and expert knowledge in a way that helps decision makers respond to the unique characteristics and drivers of SB in their catchments and more effectively focus limited health resources.

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