Evaluation of the Accuracy of the Smart Work Injury Management (SWIM) System to Assist Case Managers in Predicting the Work Disability of Injured Workers.

PURPOSE: Many countries have developed clinical decision-making support tools, such as the smart work injury management (SWIM) system in Hong Kong, to predict rehabilitation paths and address global issues related to work injury disability. This study aims to evaluate the accuracy of SWIM by comparing its predictions on real work injury cases to those made by human case managers, specifically with regard to the duration of sick leave and the percentage of permanent disability. METHODS: The study analyzed a total of 442 work injury cases covering the period from 2012 to 2020, dividing them into non-litigated and litigated cases. The Kruskal-Wallis post hoc test with Bonferroni adjustment was used to evaluate the differences between the actual data, the SWIM predictions, and the estimations made by three case managers. The intra-class correlation coefficient was used to assess the inter-rater reliability of the case managers. RESULTS: The study discovered that the predictions made by the SWIM model and a case manager possessing approximately 4 years of experience in case management exhibited moderate reliability in non-litigated cases. Nevertheless, there was no resemblance between SWIM's predictions regarding the percentage of permanent disability and those made by case managers. CONCLUSION: The findings indicate that SWIM is capable of replicating the sick leave estimations made by a case manager with an estimated 4 years of case management experience, albeit with limitations in generalizability owing to the small sample size of case managers involved in the study. IMPLICATIONS: These findings represent a significant advancement in enhancing the accuracy of CDMS for work injury cases in Hong Kong, signaling progress in the field.

Evaluating ChatGPT-4's Accuracy in Identifying Final Diagnoses Within Differential Diagnoses Compared With Those of Physicians: Experimental Study for Diagnostic Cases.

BACKGROUND: The potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists. OBJECTIVE: This study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series. METHODS: We used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4's evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician. RESULTS: The 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4's evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians' evaluations. CONCLUSIONS: GPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.

Evaluating the effectiveness of mitigation measures in environmental impact assessments: A comprehensive review of development projects in Korea.

Rapid urbanization and development projects in Korea have posed significant threats to biodiversity; thus, effective mitigation measures are required to preserve natural habitats. Nevertheless, the factors underlying variations in mitigation measure effectiveness according to the disturbance level and surrounding environmental conditions have not been clarified. This study evaluated the effectiveness of mitigation measures implemented in environmental impact assessments (EIAs) of development projects in Korea, with a focus on their effectiveness with respect to the disturbance level and surrounding environmental conditions. A review of 288 EIA reports from selected projects that implemented all 10 mitigation measures classified according to the Wildlife Conservation Comprehensive Plan was conducted. Using the biodiversity tipping point framework, the effects of mitigation measures on biodiversity were categorized into four levels and analyzed. Analysis of variance and redundancy analysis were then performed to discern the variance in mitigation measure effectiveness in terms of the disturbance level, surrounding environment, and species. The results revealed significant variations in the effectiveness of mitigation measures depending on the surrounding environment and disturbance level. Linear projects exhibited a clear impact on various species as the disturbance level increased, whereas area-based projects did not exhibit such pronounced effects. All species demonstrated a negative relationship with development duration, development area, and distance from urban centers. Notably, avian and amphibian species showed a strong negative correlation with the digital elevation model while reptiles and mammals exhibited a strong positive relationship with pre-development biodiversity and distance from protected areas, respectively. Mitigation measures play a key role in alleviating the adverse effects of development projects; therefore, our findings indicate the need for spatially tailored mitigation plans to augment their effectiveness.

Enhancing Diagnostic Support for Chiari Malformation and Syringomyelia: A Comparative Study of Contextualized ChatGPT Models.

OBJECTIVES: The rapidly increasing adoption of large language models in medicine has drawn attention to potential applications within the field of neurosurgery. This study evaluates the effects of various contextualization methods on ChatGPT's ability to provide expert-consensus aligned recommendations on the diagnosis and management of Chiari Malformation and Syringomyelia. METHODS: Native GPT4 and GPT4 models contextualized using various strategies were asked questions revised from the 2022 Chiari and Syringomyelia Consortium International Consensus Document. ChatGPT-provided responses were then compared to consensus statements using reviewer assessments of 1) responding to the prompt, 2) agreement of ChatGPT response with consensus statements, 3) recommendation to consult with a medical professional, and 4) presence of supplementary information. Flesch-Kincaid, SMOG, word count, and Gunning-Fog readability scores were calculated for each model using the quanteda package in R. RESULTS: Relative to GPT4, all contextualized GPTs demonstrated increased agreement with consensus statements. PDF+Prompting and Prompting models provided the most elevated agreement scores of 19 of 24 and 23 of 24, respectively, versus 9 of 24 for GPT4 (p=.021, p=.001). A trend toward improved readability was observed when comparing contextualized models at large to ChatGPT4, with significant decreases in average word count (180.7 vs 382.3, p<.001) and Flesch-Kincaid Reading Ease score (11.7 vs 17.2, p=.033). CONCLUSIONS: The enhanced performance observed in response to ChatGPT4 contextualization suggests broader applications of large language models in neurosurgery than what the current literature indicates. This study provides proof of concept for the use of contextualized GPT models in neurosurgical contexts and showcases the easy accessibility of improved model performance.

Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?

OBJECTIVE: To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations. MATERIALS AND METHODS: In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. The methodology included preprocessing queries, executing them multiple times, and evaluating ChatGPT responses against the databases and dermatologists. The ChatGPT-generated responses were analyzed statistically in a disease-drug matrix, considering disease-medication associations (Q-value) and expert evaluation. RESULTS: ChatGPT achieved a high 98.87% dermatologist approval rate for common dermatological medication recommendations. We evaluated its drug suggestions using the Q-value, showing that human expert validation agreement surpassed Q-value cutoff-based agreement. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. While ChatGPT offered accurate drug advice, it occasionally included incorrect ATC codes, leading to issues like incorrect drug use and type, nonexistent codes, repeated errors, and incomplete medication codes. CONCLUSION: ChatGPT provides medication recommendations as a second opinion in dermatology treatment, but its reliability and comprehensiveness need refinement for greater accuracy. In the future, integrating a medical domain-specific knowledge base for training and ongoing optimization will enhance the precision of ChatGPT's results.

Electronic Integrated Management of Childhood Illness (eIMCI): a randomized controlled trial to evaluate an electronic clinical decision-making support system for management of sick children in primary health care facilities in South Africa.

BACKGROUND: Electronic clinical decision-making support systems (eCDSS) aim to assist clinicians making complex patient management decisions and improve adherence to evidence-based guidelines. Integrated management of Childhood Illness (IMCI) provides guidelines for management of sick children attending primary health care clinics and is widely implemented globally. An electronic version of IMCI (eIMCI) was developed in South Africa. METHODS: We conducted a cluster randomized controlled trial comparing management of sick children with eIMCI to the management when using paper-based IMCI (pIMCI) in one district in KwaZulu-Natal. From 31 clinics in the district, 15 were randomly assigned to intervention (eIMCI) or control (pIMCI) groups. Computers were deployed in eIMCI clinics, and one IMCI trained nurse was randomly selected to participate from each clinic. eIMCI participants received a one-day computer training, and all participants received a similar three-day IMCI update and two mentoring visits. A quantitative survey was conducted among mothers and sick children attending participating clinics to assess the quality of care provided by IMCI practitioners. Sick child assessments by participants in eIMCI and pIMCI groups were compared to assessment by an IMCI expert. RESULTS: Self-reported computer skills were poor among all nurse participants. IMCI knowledge was similar in both groups. Among 291 enrolled children: 152 were in the eIMCI group; 139 in the pIMCI group. The mean number of enrolled children was 9.7 per clinic (range 7-12). IMCI implementation was sub-optimal in both eIMCI and pIMCI groups. eIMCI consultations took longer than pIMCI consultations (median duration 28 minutes vs 25 minutes; p = 0.02). eIMCI participants were less likely than pIMCI participants to correctly classify children for presenting symptoms, but were more likely to correctly classify for screening conditions, particularly malnutrition. eIMCI participants were less likely to provide all required medications (124/152; 81.6% vs 126/139; 91.6%, p= 0.026), and more likely to prescribe unnecessary medication (48/152; 31.6% vs 20/139; 14.4%, p = 0.004) compared to pIMCI participants. CONCLUSIONS: Implementation of eIMCI failed to improve management of sick children, with poor IMCI implementation in both groups. Further research is needed to understand barriers to comprehensive implementation of both pIMCI and eIMCI. (349) CLINICAL TRIALS REGISTRATION: Clinicaltrials.gov ID: BFC157/19, August 2019.

Problems Experienced by Health Care Professionals with Do not Attempt Resuscitation (DNAR) Orders - A Qualitative Study.

A 'Do Not Attempt Resuscitation' (DNAR) order is one of the most important yet difficult medical decisions. Despite the recent European guidelines, health care professionals (HCPs) in general perceive challenges in making a DNAR order. We aimed to evaluate the types of problems related to DNAR order making. A link to a web-based multiple-choice questionnaire including open-ended questions was sent by e-mail to all physicians and nurses working in the Tampere University Hospital special responsibility area covering a catchment area of 900,000 Finns. The questionnaire covered issues on DNAR order making, its meaning and documentation. Here we report the analysis of the open-ended questions, examined based on the Ottawa Decision Support Framework with expanded individual decisional needs categories. Qualitative data describing respondents' opinions (N=648) regarding problems related to DNAR order decision making were analysed using Atlas.ti 23.12 software. In total, 599 statements (phrases) dealing with inadequate advice, information, emotional support, and instrumental help were identified. Our results show that HCPs experience lack of support in DNAR decision making on multiple levels. Digital decision-making support integrated into electronic patient records (EPR) to assure timely and clearly visible DNAR orders could be beneficial.

Actual situation of decision-making support from medical staff when cancer patients make treatment choices.

Background: We investigated factors involved in decision-making support provided by physicians, nurses, pharmacists and medical and psychiatric social workers involved in cancer care. Materials & methods: A questionnaire survey on decision-making support was conducted. The level of clinician support was classified as 'supporting patients' 'decision-making process regarding cancer treatment', 'no support for patients' 'decision-making process regarding cancer treatment' or 'team-based support for patients' 'decision-making process regarding cancer treatment'. Results: Physicians estimated that 83.7% of patients made a cancer treatment decision within 1 week, but 45.4% of patients had difficulty making a decision. Conclusion: Medical personnel should support patients who have difficulty making decisions, establish a screening method to identify those needing support and develop a system providing decision-making support through interprofessional work.

We conducted a survey to investigate issues related to the level of decision-making support provided by physicians, nurses, pharmacists medical social workers and psychiatric social workers involved in cancer care. The physicians reported that 83.7% of patients with cancer chose a treatment plan within 1 week, although 45.4% of patients had difficulty making a decision. These decision-making difficulties arose at the time of diagnosis, when having difficulty controlling adverse events and when cancer metastasis or recurrence occurred. Some medical providers supported patients who had particular difficulty in choosing their cancer treatment, others provided no support, while a third group orchestrated a team to support them in their decision-making. To improve the quality of decision-making support, interprofessional work should be promoted and screening tools to identify those who need support should be established.

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.

Transformer-Based Approach to Melanoma Detection.

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.

Increasing clinical medical service satisfaction: An investigation into the impacts of Physicians' use of clinical decision-making support AI on patients' service satisfaction.

BACKGROUND: The medical industry is one of the key industries for the application of artificial intelligence (AI). Although it is believed that the combination of CDSS and physicians could improve the medical service, there are still many concerns about the usage of CDSS. Based on these concerns, limited studies have answered the question that when a physician makes decision independently or with AI's help, will there be any differences in patients' satisfaction with the medical service? METHODS: This study uses the service fairness theory as a theoretical lens and employs three vignette experiments to address this research gap. There are totally 740 subjects recruited to participate into the three experiments. Group comparison methods and structural equation model are used to verify the hypotheses. RESULTS: The experimental results reveal that: (1) physicians using AI can reduce patients' service satisfaction (Mdifference=0.404,p=0.004); (2) the negative relationship between AI usage and service satisfaction can partially be mediated through distributive fairness and procedural fairness; (3) physicians actively informing their patients about the usage of AI can help mitigate the reduction in service satisfaction (Mdifference=0.400,p=0.003) and three types of fairness Mdifferencedistributive=0.307,p=0.042;Mdifferenceprocedural=0.483,p<0.001;Mdifferenceinteractional=0.253,p=0.027. CONCLUSION: This study investigates the effect of physicians using decision-making support AI on their patients' service satisfaction. These results contribute to the existing literature pertaining to AI and fairness theory, and also help in formulating some practical suggestions for medical staff and AI development companies.

On the importance of interpretable machine learning predictions to inform clinical decision making in oncology.

Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient's future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions. In this paper, we describe how different algorithms can be interpreted and introduce some techniques for interpreting complex nonlinear algorithms.

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.

Challenges of using e-health technologies to support clinical care in rural Africa: a longitudinal mixed methods study exploring primary health care nurses' experiences of using an electronic clinical decision support system (CDSS) in South Africa.

BACKGROUND: Electronic decision-making support systems (CDSSs) can support clinicians to make evidence-based, rational clinical decisions about patient management and have been effectively implemented in high-income settings. Integrated Management of Childhood Illness (IMCI) uses clinical algorithms to provide guidelines for management of sick children in primary health care clinics and is widely implemented in low income countries. A CDSS based on IMCI (eIMCI) was developed in South Africa. METHODS: We undertook a mixed methods study to prospectively explore experiences of implementation from the perspective of newly-trained eIMCI practitioners. eIMCI uptake was monitored throughout implementation. In-depth interviews (IDIs) were conducted with selected participants before and after training, after mentoring, and after 6 months implementation. Participants were then invited to participate in focus group discussions (FGDs) to provide further insights into barriers to eIMCI implementation. RESULTS: We conducted 36 IDIs with 9 participants between October 2020 and May 2021, and three FGDs with 11 participants in October 2021. Most participants spoke positively about eIMCI reporting that it was well received in the clinics, was simple to use, and improved the quality of clinical assessments. However, uptake of eIMCI across participating clinics was poor. Challenges reported included lack of computer skills which made simple tasks, like logging in or entering patient details, time consuming. Technical support was provided, but was time consuming to access so that eIMCI was sometimes unavailable. Other challenges included heavy workloads, and the perception that eIMCI took longer and disrupted participant's work. Poor alignment between recording requirements of eIMCI and other clinic programmes increased participant's administrative workload. All these factors were a disincentive to eIMCI uptake, frequently leading participants to revert to paper IMCI which was quicker and where they felt more confident. CONCLUSION: Despite the potential of CDSSs to increase adherence to guidelines and improve clinical management and prescribing practices in resource constrained settings where clinical support is scarce, they have not been widely implemented. Careful attention should be paid to the work environment, work flow and skills of health workers prior to implementation, and ongoing health system support is required if health workers are to adopt these approaches (350).

Patient-Facing Clinical Decision Support for High Blood Pressure Control: Patient Survey.

BACKGROUND: High blood pressure (HBP) affects nearly half of adults in the United States and is a major factor in heart attacks, strokes, kidney disease, and other morbidities. To reduce risk, guidelines for HBP contain more than 70 recommendations, including many related to patient behaviors, such as home monitoring and lifestyle changes. Thus, the patient's role in controlling HBP is crucial. Patient-facing clinical decision support (CDS) tools may help patients adhere to evidence-based care, but customization is required. OBJECTIVE: Our objective was to understand how to adapt CDS to best engage patients in controlling HBP. METHODS: We conducted a mixed methods study with two phases: (1) survey-guided interviews with a limited cohort and (2) a nationwide web-based survey. Participation in each phase was limited to adults aged between 18 and 85 years who had been diagnosed with hypertension. The survey included general questions that assessed goal setting, treatment priorities, medication load, comorbid conditions, satisfaction with blood pressure (BP) management, and attitudes toward CDS, and also a series of questions regarding A/B preferences using paired information displays to assess perceived trustworthiness of potential CDS user interface options. RESULTS: We conducted 17 survey-guided interviews to gather patient needs from CDS, then analyzed results and created a second survey of 519 adults with clinically diagnosed HBP. A large majority of participants reported that BP control was a high priority (83%), had monitored BP at home (82%), and felt comfortable using technology (88%). Survey respondents found displays with more detailed recommendations more trustworthy (56%-77% of them preferred simpler displays), especially when incorporating social trust and priorities from providers and patients like them, but had no differences in action taken. CONCLUSIONS: Respondents to the survey felt that CDS capabilities could help them with HBP control. The more detailed design options for BP display and recommendations messaging were considered the most trustworthy yet did not differentiate perceived actions.

[Decision-making process and evaluation of public health interventions]. / Démarche décisionnelle et évaluation des interventions de santé publique.

OBJECTIVE: We have designed a methodological framework for experts involved in the support of decision-making in public health interventions. METHODS: The methodological framework consists of four elements: 1) A series of nine questions, formulated in non-technical terms, relevant to assessment of the usefulness of an intervention, at a given time in a given context; 2) Translation of these questions into concepts related to the evaluation of interventions (definition of the intervention, its target and objective, potential and actual effectiveness, safety, efficiency, and equity); 3) Logical organization of the information needed to address and answer the questions; and 4) An algorithm to translate the available information into recommendations on the real usefulness of the intervention in the context in which the questions were raised. RESULTS: Each step is illustrated by questions raised about road safety interventions, screening, blood transfusion and measures proposed during the COVID-19 pandemic. CONCLUSION: Decision-making can be facilitated if experts provide decision-makers with a formal summary of the strengths and weaknesses of existing knowledge, based on an analysis of all facets of an intervention's potential usefulness.

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.

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.

Effectiveness of a Decision Support Training Program for Health Care Professionals

Purpose: In today’s medical field, it is an essential quality competency for staff to not only recognize the importance of patient decision-making and the skills to support it, but also implement it. This study aimed to establish a training program on decision support for healthcare professionals and examine its effectiveness. Method: We conducted the training at a medical institution and conducted a questionnaire survey at two points before and after the training. A total of 88 nurses and doctors participated in the survey. Result: We developed a two-hour training on the knowledge and skills needed for decision support. Questionnaire results showed improvements in literacy and efficacy before and after the training. Discussion: It was confirmed that the training led to an in-depth understanding of the participants’ decision support, and increased the sense of efficacy in their daily work, particularly through responding to patients according to patients’ cognitive and physical assessments, and in actively supporting those who have difficulty in making decisions. There were references to the significance of re-learning and the possibility of applying the training to difficult situations in participants comments. In the future, it is necessary to study decision support with reference toco-operation in the medical field where collaboration among multiple professions is indispensable.