A qualitative system dynamics model for effects of workplace violence and clinician burnout on agitation management in the emergency department

Background Over 1.7 million episodes of agitation occur annually across the United States in emergency departments (EDs), some of which lead to workplace assaults on clinicians and require invasive methods like physical restraints to maintain staff and patient safety. Recent studies demonstrated that experiences of workplace violence contribute to symptoms of burnout, which may impact future decisions regarding use of physical restraints on agitated patients. To capture the dynamic interactions between clinicians and agitated patients under their care, we applied qualitative system dynamics methods to develop a model that describes feedback mechanisms of clinician burnout and the use of physical restraints to manage agitation. Methods We convened an interprofessional panel of clinician stakeholders and agitation experts for a series of model building sessions to develop the current model. The panel derived the final version of our model over ten sessions of iterative refinement and modification, each lasting approximately three to four hours. We incorporated findings from prior studies on agitation and burnout related to workplace violence, identifying interpersonal and psychological factors likely to influence our outcomes of interest to form the basis of our model. Results The final model resulted in five main sets of feedback loops that describe key narratives regarding the relationship between clinician burnout and agitated patients becoming physically restrained: (1) use of restraints decreases agitation and risk of assault, leading to increased perceptions of safety and decreasing use of restraints in a balancing feedback loop which stabilizes the system; (2) clinician stress leads to a perception of decreased safety and lower threshold to restrain, causing more stress in a negatively reinforcing loop; (3) clinician burnout leads to a decreased perception of colleague support which leads to more burnout in a negatively reinforcing loop; (4) clinician burnout leads to negative perceptions of patient intent during agitation, thus lowering threshold to restrain and leading to higher task load, more likelihood of workplace assaults, and higher burnout in a negatively reinforcing loop; and (5) mutual trust between clinicians causes increased perceptions of safety and improved team control, leading to decreased clinician stress and further increased mutual trust in a positively reinforcing loop. Conclusions Our system dynamics approach led to the development of a robust qualitative model that illustrates a number of important feedback cycles that underly the relationships between clinician experiences of workplace violence, stress and burnout, and impact on decisions to physically restrain agitated patients. This work identifies potential opportunities at multiple targets to break negatively reinforcing cycles and support positive influences on safety for both clinicians and patients in the face of physical danger.

Identifying stakeholders’ perspectives on the success factors of halal tourism in the city of Makassar: a group model building approach

Purpose Islamic attributes and activities need to be developed in compliance with the halal concept to attract potential Muslim tourists and ensure the success of halal tourism. Although the literature shows that many factors can influence the success of halal tourism, a complete picture of the success factors of halal tourism in a city is still very limited. As such, this explorative study aims to examine stakeholders’ perspectives regarding the antecedents of halal tourism success and the benefits of halal tourism for the city. Design/methodology/approach Qualitative system dynamics modeling was used for this study, and Makassar (a successful halal tourism city) was considered as the basis for the study. A causal loop diagram (CLD) of halal tourism was developed using the group model building technique to elicit stakeholders’ knowledge and assumptions. Network analysis and feedback loop analysis were used to identify the driving factors of successful halal tourism. Findings Two factors need to be taken into account by halal tourism stakeholders in the city: support from the central and local government and improving and maintaining potential tourists’ perceptions of the city. There are four benefits of halal tourism success for the city: an increase in the number of micro-, small- and medium-sized halal businesses in the city, increased support from the central and local government to further develop halal tourism infrastructure in the city, increased word-of-mouth promotion of Makassar as a tourism destination and a decrease in the price of halal tourism components (e.g. food and accommodation). Originality/value The resulting CLD shows the interlinkage between political, societal and economical factors that could influence the success of halal tourism development. In particular, the findings show how governments and tourism stakeholders need to promote halal tourism socialization in the community and improve the public perception of this type of tourism. Therefore, the findings can help destination stakeholders and tourism developers in other cities develop halal tourism potential.

Artificial Intelligence Techniques for Power System Transient Stability Assessment

The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.

Supervised Learning Predictive Models for Automated Fracturing Treatment Design: A Workflow Based on Algorithm Comparison and Multiphysics Model Validation

Diagnostic pumping techniques are used routinely in proppant fracturing design. The pumping process can be time consuming; however, it yields technical confidence in treatment and productivity optimization. Recent developments in data analytics and machine learning can aid in shortening operational workflows and enhance project economics. Supervised learning was applied to an existing database to streamline the process and affect the design framework. Five classification algorithms were used for this study. The database was constructed through heterogeneous reservoir plays from the injection/falloff outputs. The algorithms used were support vector machine, decision tree, random forest, multinomial, and XGBoost. The number of classes was sensitized to establish a balance between model accuracy and prediction granularity. Fifteen cases were developed for a comprehensive comparison. A complete machine learning framework was constructed to work through each case set along with hyperparameter tuning to maximize accuracy. After the model was finalized, an extensive field validation workflow was deployed. The target outputs selected for the model were crosslinked fluid efficiency, total proppant mass, and maximum proppant concentration. The unsupervised clustering technique with t-SNE algorithm that was used first lacked accuracy. Supervised classification models showed better predictions. Cross-validation techniques showed an increasing trend of prediction accuracy. Feature selection was done using one-variable-at-a-time (OVAT) and a simple feature correlation study. Because the number of features and the dataset size were small, no features were eliminated from the final model building. Accuracy and F1 score calculations were used from the confusion matrix for evaluation, XGBoost showed excellent results with an accuracy of 74 to 95% for the output parameters. Fluid efficiency was categorized into three classes and yielded an accuracy of 96%. Proppant concentration and proppant mass predictions showed 77% and 86% accuracy, respectively, for the six-class case. The combination of high accuracy and fine granularity confirmed the potential application of machine learning models. The ratio of training to testing (holdout) across all cases ranged from 80:20 to 70:30. Model validations were done through an inverse problem of predicting and matching the fracture geometry and treatment pressures from the machine learning model design and the actual net pressure match. The simulations were conducted using advanced multiphysics simulations. The advantages of this innovative design approach showed four areas of improvement: reduction in polymer consumption by 30%, reduction of the flowback time by 25%, reduction of water usage by 30%, and enhanced operational efficiency by 60 to 65%.

Innovative application of full-waveform inversion applied to extended wide-azimuth marine streamer seismic in a complex salt environment

We demonstrated the application of full-waveform inversion (FWI) guided velocity model building to an extended wide-azimuth towed streamer (EWATS) seismic data set in the Gulf of Mexico. Field data were collected over a historically challenging imaging area, colloquially called the “grunge zone” due to the formation of a compressional allosuture emplaced between two colliding salt sheets. These data had a poor subsalt image below the suture with conventional narrow-azimuth data. Additional geologic complexities were observed including high-velocity carbonate carapace near the top of salt and multiple intrasalt sedimentary inclusions. As such, improved seismic imaging was required to plan and execute wells targeting subsalt strata. Significant improvements to the velocity model and subsalt image were evident with wide-azimuth towed streamer and later EWATS data using conventional top-down velocity model building approaches. Then, high-impact improvements were made using EWATS data with an FWI velocity model building workflow; this study represented an early successful application of FWI used to update salt body geometries from streamer seismic data, in which many past applications were limited to improving sedimentary velocities. Later petrophysical data verified the new FWI-derived model, which had significantly increased confidence in the structural and stratigraphic interpretation of subsalt reservoir systems below the grunge zone.

An Introduction to the Federated Learning Standard

With the growing concern on data privacy and security, it is undesirable to collect data from all users to perform machine learning tasks. Federated learning, a decentralized learning framework, was proposed to construct a shared prediction model while keeping owners' data on their own devices. This paper presents an introduction to the emerging federated learning standard and discusses its various aspects, including i) an overview of federated learning, ii) types of federated learning, iii) major concerns and the performance evaluation criteria of federated learning, and iv) associated regulatory requirements. The purpose of this paper is to provide an understanding of the standard and facilitate its usage in model building across organizations while meeting privacy and security concerns.

USING SYSTEM DYNAMICS MODELING TO ASSESS THE IMPACT OF CONNECTICUT GOOD SAMARITAN LAWS: A PROTOCOL PAPER

Background We applied a participatory system dynamics (SD) modeling approach to evaluate the effectiveness and impact of Connecticut Good Samaritan Laws (GSLs) that are designed to promote bystander intervention during an opioid overdose event and reduce opioid overdose-related adverse outcomes. Our SD model can be used to predict whether additional revisions of the statutes might make GSLs more effective. SD modeling is a novel approach for assessing the impact of GSLs; and, in this protocol paper, we describe its applicability to our policy question, as well as expected outcomes of this approach. Methods This project began in February 2021 and is expected to conclude by March 2022. During this time, a total of six group model-building (GMB) sessions will have been held with key stakeholders to elicit feedback that will, in turn, contribute to the development of a more robust SD model. Session participants include bystanders who witness an overdose, law enforcement personnel, first responders, pharmacists, physicians, and other health care professionals who work in at least two major metropolitan areas of Connecticut (New Haven and Hartford). Due to the restrictions imposed by the COVID-19 pandemic, the sessions are being held virtually via Zoom. The information obtained during these sessions will be integrated with a draft SD model that has already been developed by the modeling team as part of a previous CDC-funded project. Model calibration and policy simulations will then be performed to assess the impact of the current GSLs and to make recommendations for future public policy changes. Discussion An SD modeling approach enables capture of complex interrelationships among multiple health outcomes to better assess the drivers of the opioid epidemic in Connecticut. The model simulation results are expected not only to align with current real-world data but also to recreate historical trends and infer future trends in a situationally relevant fashion. This will facilitate the work of policy makers who are devising and implementing time-sensitive changes to address opioid overdose-related deaths at the state level. Replicating our approach as described can be applied to make similar improvements in other jurisdictions. CONTRIBUTIONS TO THE LITERATURE - System dynamics (SD) modeling and group model-building (GMB) approaches enable the group to start with a simple concept model and apply the collective knowledge of the group to finish the session with a much more developed model that can produce impressively accurate simulation results. - The model will be used to understand the impact of Connecticut Good Samaritan Laws (GSLs), as well as their limitations, and to deduce factors to further improve public health laws to counter opioid overdose-related deaths. - The approach can be applied to other jurisdictions, taking into account local conditions and existing Good Samaritan legislation. KEYWORDS: System dynamics modeling, group model building, opioid overdose deaths, opioid use disorder, Good Samaritan laws

Sequence assignment validation in cryo-EM models with checkMySequence

The availability of new AI-based protein structure prediction tools radically changed the way cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register-shifts remain one of the most difficult to identify and correct. Here we introduce checkMySequence; a fast, fully automated and parameter-free method for detecting register-shifts in protein models built into cryo-EM maps. We show that the method can assist model building in cases where poorer map resolution hinders visual interpretation. We also show that checkMySequence could have helped avoid a widely discussed sequence register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community.