Decision-making support: the impact of training on disability support workers who work with adults with cognitive disability

There is growing recognition that people with disability have the right to be involved in making decisions that affect their lives. Decision-making support has emerged as one way to support people with cognitive disability to make decisions, however, there is a paucity of research that explores how disability support workers can be upskilled to provide decision-making support to this group. The aim of the research was to explore the impact of an evidence-based online training course on disability support workers of adults with cognitive disability. Changes in knowledge about decision-making support and confidence in providing decision-making support were explored, attitudes towards decision-making support, and facilitators and barriers. Participants completed an online training course and responded to a survey on three occasions: baseline, post-training, and at 2-month follow-up. Ninety-nine disability support workers across Australia participated in the online training and completed the baseline and post-training surveys. Thirty-six participants completed the training and all three surveys. The results revealed that there were statistically significant improvements in knowledge, confidence, and attitudes from baseline to post-training, which were maintained at 2-month follow-up. Barriers to decision-making support included service providers or other supporters, including the family of the person with cognitive disability, whilst a key enabler was knowing about the decision-making support principles. This research demonstrates that an evidence-based online training course about decision-making support can be effective in building capacity in disability support workers. There are, however, several barriers that must be addressed to facilitate the implementation of decision-making support.

Measuring the worldwide spread of COVID-19 using a comprehensive modeling method

BACKGROUND

With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models.

METHODS

We collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model.

RESULTS

All 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9-12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10-20% of the countries' populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner.

CONCLUSIONS

We demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management.

Three-level model for opinion aggregation under hesitance

Valuable information for decision-making can be obtained by collecting and analyzing opinions from diverse stakeholder or respondent groups, which usually have different backgrounds and are variously affected by the topics under survey. For this to succeed, it is necessary to manage the uncertainty of respondents' opinions, different number of filled questionnaires among groups, different number of questions for each stakeholder group, and relevance of subsets of respondent groups. This work proposes handling the hesitance of respondents' opinions for the rating scale questions. To evaluate the collected opinions, a three-level aggregation model is developed. In the first level, the overall opinion of each respondent is computed as a mean of fuzzy numbers covering uncertain answers and their respective hesitance. In the second level, stakeholder groups are considered as a whole. Aggregation by a relative quantifier is applied to calculate the validity of a proposition the majority of respondents have a positive or negative opinion. At the third level, the consensus among diverse subsets of stakeholder groups is calculated considering the relevance of each group independently as well as their so-called coalitions by Choquet integral. Finally, the proposed model is illustrated by a real-life case study.

Post-consumer plastic sorting infrastructure improvements planning: Scenario-based modeling of greenhouse gas savings with sustainable costs

With the increasing share of waste material recovery, household plastic waste is one of the biggest problems. In most countries, mainly manual sorting is used. Meanwhile, new automated technologies are being developed to expand the range of classifiable types to increase material recovery. The overall automation of the sorting process can help the EU's established recycling targets to be effectively met. However, the new technologies are feasible only in the case of large-capacity centers, which must be conveniently located in the existing infrastructure. This paper presents a two-stage model aiming to modernize the current sorting infrastructure for plastic waste. The approach uses multi-criteria optimization to minimize environmental impact at a reasonable price. The result is the optimal location of new automatic sorting centers, and waste stream flows using existing manual sorting facilities. The model is applied through an initial case study inspired by the Czech Republic data. Optimization output proposes four new automatic sorting lines with a total capacity of 158 kt per year. In most cases, manual sorting is used to reduce the transported weight of plastic waste, while automatic sorting lines separate the remaining, hardly recognized part. More than 60% of separately collected plastic is sorted and determined for material recovery.

Development and Evaluation of Options for Action to Progress on the SDG 6 Targets in Austria

The Agenda 2030 of the United Nations stipulates an ambitious set of 17 Sustainable Development Goals (SDGs). They were globally agreed upon and demand coherent, context-specific implementation at the national level. To address the complexity of challenges therein, the Agenda is designed to be integrated, indivisible, and universal. The numerous multifaceted interactions in-between the SDGs and with corresponding measures pose a complex challenge for decision-makers implementing them worldwide that requires support for a comprehensive discourse in the science-society-policy arena. Research on the interactions between the SDGs has been flourishing and can help to understand where policy options might be most successfully located. A catalytic effect on several other goals is, e.g., often attributed to SDG 6 on water and sanitation. However, beyond the where to locate policy options, it is similarly important to understand how potential policy options would affect the SDGs and their targets. We developed eleven options and 85 measures as context-specific pathways to advance the SDG 6 Targets in Austria. As a country in the Global North and with a generally far-established water and sanitation infrastructure and management, this responds to the Agenda's demand for universal applicability and can serve as an example to illustrate potential challenges beyond basic infrastructure provision and management. The proposed options cover resources-oriented sanitation, blue-green-brown infrastructure, efficient use and integrated management of water resources, maintenance and restoration of ecological functions of inland waters, reduction of diffuse discharge of nutrients and problematic substances as well as trace substances, water, sanitation and hygiene in public spaces, groundwater protection, development cooperation as well as co-design and co-creation. Their effects on the SDG 6 Targets are evaluated using a 7-point-scale. The evaluation method is simple and practicable, and fosters discourse on the entire water cycle amongst the expert group applying the method. The evaluated effects on the targets are found to be unanimously positive or neutral, but trade-offs might arise when including other SDGs in the assessment, making an expansion of the evaluation necessary for coherent implementation. The results can be used as a baseline to support follow-up discussions with stakeholders and decision-makers.

Machine learning-based triage to identify low-severity patients with a short discharge length of stay in emergency department

BACKGROUND

Overcrowding in emergency departments (ED) is a critical problem worldwide, and streaming can alleviate crowding to improve patient flows. Among triage scales, patients labeled as "triage level 3" or "urgent" generally comprise the majority, but there is no uniform criterion for classifying low-severity patients in this diverse population. Our aim is to establish a machine learning model for prediction of low-severity patients with short discharge length of stay (DLOS) in ED.

METHODS

This was a retrospective study in the ED of China Medical University Hospital (CMUH) and Asia University Hospital (AUH) in Taiwan. Adult patients (aged over 20 years) with Taiwan Triage Acuity Scale level 3 were enrolled between 2018 and 2019. We used available information during triage to establish a machine learning model that can predict low-severity patients with short DLOS. To achieve this goal, we trained five models-CatBoost, XGBoost, decision tree, random forest, and logistic regression-by using large ED visit data and examined their performance in internal and external validation.

RESULTS

For internal validation in CMUH, 33,986 patients (75.9%) had a short DLOS (shorter than 4 h), and for external validation in AUH, there were 13,269 (82.7%) patients with short DLOS. The best prediction model was CatBoost in internal validation, and area under the receiver operating cha racteristic curve (AUC) was 0.755 (95% confidence interval (CI): 0.743-0.767). Under the same threshold, XGBoost yielded the best performance, with an AUC value of 0.761 (95% CI: 0.742- 0.765) in external validation.

CONCLUSIONS

This is the first study to establish a machine learning model by applying triage information alone for prediction of short DLOS in ED with both internal and external validation. In future work, the models could be developed as an assisting tool in real-time triage to identify low-severity patients as fast track candidates.

Midwifery scale to support shared decision-making for unplanned pregnancies: A cross-sectional study

Midwives significantly support women with unplanned pregnancies-promoting a shared perspective on the decision-making process. This study aimed to develop a scale to support midwives self-assess their practice of this vital role. Following the derivation of scale items and pilot testing, the final version of the scale was administered to 531 midwives to establish internal consistency and construct criterion-related validity. Through exploratory factor analysis, 35 items with a five-factor structure were retained to form the midwifery practice self-assessment scale to promote shared decision-making in women with unplanned pregnancies. These factors illustrate midwives' general aptitude and competencies in understanding environmental factors, collaborating with significant others and the interprofessional group, forming rapport and problem sharing, focusing on consultation content, and promoting autonomous decision-making. There were high and low scores on the scales after attending the workshops to support the decision-making of women with unplanned pregnancies. The reliability analysis showed acceptable Cronbach's alpha values for the five factors, from .85-.87. The scale was demonstrated to be a reliable and valid measure that would help improve the quality of midwives' practice. This article is protected by copyright. All rights reserved.

[Artificial intelligence in cardiology : Relevance, current applications, and future developments]

Big data and applications of artificial intelligence (AI), such as machine learning or deep learning, will enrich healthcare in the future and become increasingly important. Among other things, they have the potential to avoid unnecessary examinations as well as diagnostic and therapeutic errors. They could enable improved, early and accelerated decision-making. In the article, the authors provide an overview of current AI-based applications in cardiology. The examples describe innovative solutions for risk assessment, diagnosis and therapy support up to patient self-management. Big data and AI serve as a basis for efficient, predictive, preventive and personalised medicine. However, the examples also show that research is needed to further develop the solutions for the benefit of the patient and the medical profession, to demonstrate the effectiveness and benefits in health care and to establish legal and ethical standards.

Optimization of water resource dispatching for Huanghua Port under uncertain water usage scenario

In recent years, large-scale coal bulk cargo ports have been vigorously promoting the green and intelligent construction, it is an important problem for them to manage water resources scientifically and effectively to realize energy conservation and environmental protection under uncertain circumstances. Taking Huanghua Port of Shenhua Group in China as an example, firstly, through a systematic review of the water resource dispatching infrastructures and production operations, the four-level water resource dispatching framework of Huanghua Port was obtained. Secondly, an uncertain multi-objective programming model is constructed to comprehensively consider the cost of water purchase, the energy consumption of water diversion, and the uncertainty of water usage. Then, an algorithm is designed according to the characteristics of the model. Finally, the applicability and effectiveness of the water resource dispatching framework, optimization model, and solution algorithm are verified by the analysis of 8 typical production water scenarios. This study not only provides Huanghua Port with a water resource dispatching optimization solution but also provides a decision-making reference for the green and intelligent transformation and upgrading of other large-scale coal bulk ports.

[Protecting dignity: Reviewing the decision-supporting process for an isolated elderly individual living in a large housing complex]

AIM

To clarify the conditions under which dignity is maintained by reviewing the decision-supporting process for a case.

METHODS

We conducted both a longitudinal epidemiological survey and action research in parallel in a large housing complex district in Tokyo, Japan, using the community-based participatory research framework. Through collaboration with community professionals, we supported an isolated elderly man who refused medical intervention for three years until his death. After his passing, we re-examined all of his records, conducted in-depth interviews with the community professionals, and held a conference to review the process of managing this individual.

RESULTS

Concerning support for the decision-making, three conclusions were obtained from the data: 1) a decision is not always stated explicitly; 2) a decision should be supported by the team, because mind sometimes changes; and 3) supporting decision-making is a process in itself. For the maintenance of dignity in the medical setting, the following were kept in mind: medical context is not all that is important; supporters should wait for the right moment to intervene, and support should be provided to help the patient keep in touch with other people and the community.

CONCLUSIONS

While precisely defining dignity can be difficult, we explored the conditions under which dignity could be maintained by reviewing the decision-supporting process for a single case. Geriatricians may encounter difficult and complex cases such as this in the clinical setting, but guidelines cannot cover such diverse cases.