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We describe a new Bayesian estimation algorithm for fitting a binary treatment, ordered outcome selection model in a potential outcomes framework. We show how recent advances in simulation methods, namely data augmentation, the Gibbs sampler and the Metropolis-Hastings algorithm can be used to fit this model efficiently, and also introduce a reparameterization to help accelerate the convergence of our posterior simulator. Conventional “treatment effects” such as the Average Treatment Effect (ATE), the effect of treatment on the treated (TT) and the Local Average Treatment Effect (LATE) are adapted for this specific model, and Bayesian strategies for calculating these treatment effects are introduced. Finally, we review how one can potentially learn (or at least bound) the non-identified cross-regime correlation parameter and use this learning to calculate (or bound) parameters of interest beyond mean treatment effects.

The purpose of this paper is to introduce a method of the bottleneck detection for Emergency Department (ED) improvement using benchmarking and design of experiments (DOE) in simulation model.

Four procedures of treatments are used to represent ED activities of the patient flow. Simulation modeling is applied as a cost-effective tool to analyze the ED operation. Benchmarking provides the achievable goal for the improvement. DOE speeds up the process of bottleneck search.

It is identified that the long waiting time is accumulated by previous arrival patients waiting for treatment in the ED. Comparing the processing time of each treatment procedure with the benchmark reveals that increasing the treatment time mainly happens in treatment in progress and emergency room holding (ERH) procedures. It also indicates that the to be admitted time caused by the transfer delay is a common case.

The current research is conducted in the ED only. Activities in the ERH require a close cooperation of several medical teams to complete patients’ condition evaluations. The current model may be extended to the related medical units to improve the model detail.

ED overcrowding is an increasingly significant public healthcare problem. Bottlenecks that affect ED overcrowding have to be detected to improve the patient flow.

Integration of benchmarking and DOE in simulation modeling proposed in this research shows the promise in time-saving for bottleneck detection of ED operations.

This article explores the use of the Good Lives Model and its relevance to people with a learning disability and forensic needs. The article presents the rationale for using the model; arguing that it has the potential to address the complexities of meeting both the person‐centred agenda in learning disabilities services and the public protection agenda in relation to the management of mentally disordered offenders, including those detained under the Mental Health Act (2007). The model is compared with other treatment models, such as the Risk‐Need‐Responsivity Model (RNR). The paper briefly explores how the model may be practically applied in a service for people with learning disabilities who have committed, or who are at risk of committing, sexual offences.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.

Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).

The MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.

For both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.

The objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

The purpose of this paper is to improve the accuracy of evaluation efficiency by constructing parallel structures considering the main components of industrial pollutants, and then to consider some external influence factors to eliminate random errors.

In this paper, data transformation has been used to deal with undesirable output, and a model with a parallel structure based on the three-stage data envelopment analysis model to calculate the efficiency scores of different division in pollution treatment has been composed.

The analysis shows that the external environmental factors and random factors of the economy and society greatly affect the efficiency of industrial pollutant treatment; moreover, there is an imbalance between regions in China in the treatment of industrial pollutants.

Optimal improvement requires each province to take targeted measures to improve its efficiency of pollutant treatment measures, which are tailored to specific situations and determined by efficiency analysis in this paper.

The purpose of this paper is to present insights into how and why the Sanctuary and SELF models are effective in decreasing trauma symptoms with a population of court-committed male adolescents in a residential treatment program. The Sanctuary model is a trauma-focussed, trauma-sensitive, organizational change model, and treatment protocol approach to working with clients who have experienced trauma, loss, and toxic stress to the degree that they interfere with social and personal functioning. The SELF model within Sanctuary is a treatment protocol that is an acronym for the organizing categories of safety, emotion management, loss, and the future. In essence, Sanctuary’s purpose is to create therapeutic community.

Qualitative research methods of observation of groups and meetings, content analysis of existing quantitative data and agency documents, focus groups with staff and residents, and individual interviews with staff were utilized.

Data show that the Sanctuary model ameliorates the symptoms of complex trauma. The substantive theory that emerges is that relational and neurological integration and recovery occur in the lives of residents as shaped first by the therapeutic community that supports the level of interpersonal relationships experienced with staff within a therapeutic milieu, along with shaping the organizational culture.

As a complex intervention, it is evident that reducing the Sanctuary model into its component parts cannot capture fully the essence of the intervention. A complex system can never be understood fully by observing it at single points in time.

It is suggested that future research and programmatic planning within this therapeutic community need to demonstrate how to continue enhancing staff-resident relational integration vis-à-vis staff training and vehicles that offer residents more of a representative voice while in placement.

It is suggested that future research and programmatic planning within this agency need to demonstrate how to continue enhancing staff-resident trauma-informed therapeutic milieus and relational integration vis-à-vis staff training and vehicles that offer residents more of a representative voice while in placement.

This is a unique study in that it employs qualitative methods to explore how and why the Sanctuary model contributes to its working in a residential treatment facility. The Sanctuary model is the only trauma-informed organizational intervention of its kind, with limited published evaluations in the current literature (Esaki et al., 2013). This study used focus groups with residents and staff that allowed them to influence the research and its processes. The residents expressed their views about the experience of being placed outside of their homes and of living in a therapeutic community within the Sanctuary Network. Staff spoke of aspects of working in a trauma-informed milieu and its effect on clients, colleagues, and the organization as a whole.

I propose a general framework for instrumental variables estimation of the average treatment effect in the correlated random coefficient model, focusing on the case where the treatment variable has some discreteness. The approach involves adding a particular function of the exogenous variables to a linear model containing interactions in observables, and then using instrumental variables for the endogenous explanatory variable. I show how the general approach applies to binary and Tobit treatment variables, including the case of multiple treatments.