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Parents who receive a diagnosis of a severe, life-threatening CHD for their foetus or neonate face a complex and stressful decision between termination, palliative care, or surgery. Understanding how parents make this initial treatment decision is critical for developing interventions to improve counselling for these families.
We conducted focus groups in four academic medical centres across the United States of America with a purposive sample of parents who chose termination, palliative care, or surgery for their foetus or neonate diagnosed with severe CHD.
Ten focus groups were conducted with 56 parents (Mage = 34 years; 80% female; 89% White). Results were constructed around three domains: decision-making approaches; values and beliefs; and decision-making challenges. Parents discussed varying approaches to making the decision, ranging from relying on their “gut feeling” to desiring statistics and probabilities. Religious and spiritual beliefs often guided the decision to not terminate the pregnancy. Quality of life was an important consideration, including how each option would impact the child (e.g., pain or discomfort, cognitive and physical abilities) and their family (e.g., care for other children, marriage, and career). Parents reported inconsistent communication of options by clinicians and challenges related to time constraints for making a decision and difficulty in processing information when distressed.
This study offers important insights that can be used to design interventions to improve decision support and family-centred care in clinical practice.
This paper presents a methodology to support the decision-making process during the planning of ship operations. The methodology is designed with the aim of identifying and providing the operator with the best Estimated Time of Departure (ETD)–Estimated Time of Arrival (ETA) window of opportunity to execute the journey/operation between two predefined locations. To achieve this purpose, the International Maritime Organization (IMO) stability criteria are exploited in the process to formulate an operational safety criterion based on fuzzy reasoning as a function of the METeorological and OCeanographic (METOC) and sailing conditions. This allows for the analysis of the set of Pareto routes computed by a weather routing systems relying on a multi-objective set-up. The proposed methodology is tested in an operational scenario in the Mediterranean Sea.
Key to bridging knowing–doing gaps is analysis of the constraints binding interactions between decision-makers and conservation biologists to clarify the problems they address. We apply this analysis to decision situations in the Northern Vosges (France), which illustrate three kinds of constraints: governance, framework and initiative. We explore how conservation biologists can mitigate constraints so as to foster more ambitious conservation actions in each case. The first case explores attempts at reintroducing the lynx (Lynx lynx). In this case, we show that governance plays a key role, in the sense that conservation actions should focus on improving the acceptability of reintroductions to key stakeholders. The second case refers to water monitoring schemes. Here we show that framing is the dominant constraint. This means that conservation actions are tightly limited by the use of a restrictive scientific apparatus. The last case study, fish stock protection, is constrained by initiative. Here, decision-makers have too much leverage to implement solutions they favour, even if they are not the best options in conservation terms. Exploring how our framework relates to the existing literature allows us to highlight its usefulness for rationalizing conservation problem framing and for strengthening the ambitions of conservation actions.
Rough sleeping is a chronic experience faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link (HL), a UK-based charity, in developing a data-driven approach to better connect people sleeping rough on the streets with outreach service providers. HL's platform has grown exponentially in recent years, leading to thousands of alerts per day during extreme weather events; this overwhelms the volunteer-based system they currently rely upon for the processing of alerts. In order to solve this problem, we propose a human-centered machine learning system to augment the volunteers' efforts by prioritizing alerts based on the likelihood of making a successful connection with a rough sleeper. This addresses capacity and resource limitations whilst allowing HL to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation using historical data shows that our approach increases the rate at which rough sleepers are found following a referral by at least 15% based on labeled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modeling process is done with careful considerations of ethics, transparency, and explainability due to the sensitive nature of the data involved and the vulnerability of the people that are affected.
When designing complex systems, multiple people contribute to the process of information collection in support of decision making. In this paper, we study information collection in the Issue Resolution Decision Support (IRDS) framework. We assess the difficulties associated with uncertainty in the often scarce data when implementing the framework in a company and map out how the data sources are scattered across the organization. We study the elicitation process and propose to leverage sensitivity analysis to better allocate data collection efforts.
This third edition capitalizes on the success of the previous editions and leverages the important advancements in visualization, data analysis, and sharing capabilities that have emerged in recent years. It serves as an accelerated guide to decision support designs for consultants, service professionals and students. This 'fast track' enables a ramping up of skills in Excel for those who may have never used it to reach a level of mastery that will allow them to integrate Excel with widely available associated applications, make use of intelligent data visualization and analysis techniques, automate activity through basic VBA designs, and develop easy-to-use interfaces for customizing use. The content of this edition has been completely restructured and revised, with updates that correspond with the latest versions of software and references to contemporary add-in development across platforms. It also features best practices in design and analytical consideration, including methodical discussions of problem structuring and evaluation, as well as numerous case examples from practice.
The anesthesia record is more than just a historic snapshot of clinical care. It also serves as a clinical monitor in itself. In electronic form, and as a component of an electronic health record (EHR), its utility is extended to provide data to drive clinical decision support, compliance, research, administrative, and human resource functions with an overall goal of performance improvement.
The volume of evidence from scientific research and wider observation is greater than ever before, but much is inconsistent and scattered in fragments over increasingly diverse sources, making it hard for decision-makers to find, access and interpret all the relevant information on a particular topic, resolve seemingly contradictory results or simply identify where there is a lack of evidence. Evidence synthesis is the process of searching for and summarising a body of research on a specific topic in order to inform decisions, but is often poorly conducted and susceptible to bias. In response to these problems, more rigorous methodologies have been developed and subsequently made available to the conservation and environmental management community by the Collaboration for Environmental Evidence. We explain when and why these methods are appropriate, and how evidence can be synthesised, shared, used as a public good and benefit wider society. We discuss new developments with potential to address barriers to evidence synthesis and communication and how these practices might be mainstreamed in the process of decision-making in conservation.
Shared patient–clinician decision-making is central to choosing between medical treatments. Decision support tools can have an important role to play in these decisions. We developed a decision support tool for deciding between nonsurgical treatment and surgical total knee replacement for patients with severe knee osteoarthritis. The tool aims to provide likely outcomes of alternative treatments based on predictive models using patient-specific characteristics. To make those models relevant to patients with knee osteoarthritis and their clinicians, we involved patients, family members, patient advocates, clinicians, and researchers as stakeholders in creating the models.
Stakeholders were recruited through local arthritis research, advocacy, and clinical organizations. After being provided with brief methodological education sessions, stakeholder views were solicited through quarterly patient or clinician stakeholder panel meetings and incorporated into all aspects of the project.
Participating in each aspect of the research from determining the outcomes of interest to providing input on the design of the user interface displaying outcome predications, 86% (12/14) of stakeholders remained engaged throughout the project. Stakeholder engagement ensured that the prediction models that form the basis of the Knee Osteoarthritis Mathematical Equipoise Tool and its user interface were relevant for patient–clinician shared decision-making.
Methodological research has the opportunity to benefit from stakeholder engagement by ensuring that the perspectives of those most impacted by the results are involved in study design and conduct. While additional planning and investments in maintaining stakeholder knowledge and trust may be needed, they are offset by the valuable insights gained.
Product development, especially in aerospace, has become more and more interconnected with its operational environment. In a constant changing world, the operational environment will be subjected to changes during the life cycle of the product. The operational environment will be affected by not only technical and non-technical perturbations, but also economical, managerial and regulatory decisions, thus requiring a more global product development approach. One way to try tackling such complex and intertwined problem advocates studying the envisioned product or system in the context of system of systems (SoS) engineering. SoSs are all around us, probably in any field of engineering, ranging from integrated transport systems, public infrastructure systems to modern homes equipped with sensors and smart appliances; from cities filling with autonomous vehicle to defence systems.
Since also aerospace systems are certainly affected, this work will present a holistic approach to aerospace product development that tries spanning from needs to technology assessment. The proposed approach will be presented and analysed and key enablers and future research directions will be highlighted from an interdisciplinary point of view. Consideration of the surrounding world will require to look beyond classical engineering disciplines.
Quantifying reasonable crop yield gaps and determining potential regions for yield improvement can facilitate regional plant structure adjustment and promote crop production. The current study attempted to evaluate the yield gap in a region at multi-scales through model simulation and farmer investigation. Taking the winter wheat yield gap in the Huang-Huai-Hai farming region (HFR) for the case study, 241 farmers’ fields in four typical high-yield demonstration areas were surveyed to determine the yield limitation index and attainable yield. In addition, the theoretical and realizable yield gap of winter wheat in 386 counties of the HFR was assessed. Results showed that the average field yield of the demonstration plots was 8282 kg/ha, accounting for 0.72 of the potential yield, which represented the highest production in the region. The HFR consists of seven sub-regions designated 2.1–2.7: the largest attainable yield gap existed in the 2.6 sub-region, in the southwest of the HFR, while the smallest was in the 2.2 sub-region, in the northwest of the HFR. With a high irrigated area rate, the yield gap in the 2.2 sub-region could hardly be reduced by increasing irrigation, while a lack of irrigation remained an important limiting factor for narrowing the yield gap in 2.3 sub-region, in the middle of the HFR. Therefore, a multi-scale yield gap evaluation framework integrated with typical field survey and crop model analysis could provide valuable information for narrowing the yield gap.
Farmers, who have to decide which pesticide to use against a particular crop-damaging pest, need to take into account country-specific regulations (e.g. permitted levels of pesticide residues), application instructions and financial considerations. The fact that these data are stored in different locations, sometimes using different terminology or different languages, makes it difficult to gather these data and requires that farmers are familiar with the variety of terms used, which consequently hampers the efficiency and effectiveness of the decision process. To overcome these challenges, a Web application for pest control is proposed to facilitate the integration of information coming from different Internet sources and representing different terminologies by using an ontology. The application is based on a pest-control ontology (formal representations of domain knowledge that can be interpreted by computers) that accounts for various pesticide regulations of different countries to which the crop is exported. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision support systems and other intelligent systems, inter alia, in the context of agriculture. The pest-control ontology developed in the current research includes pest-control concepts that have yet to be covered by existing ontologies. It is demonstrated in the specific case of pepper in Israel. The ontology is expressed using Web Ontology Language (OWL) and thus can be shared on the Web and reused by other ontologies and systems. In addition, a comprehensive method for developing and evaluating agricultural ontologies is presented.
Weeds remain the most commonly cited concern of organic farmers. Without the benefit of synthetic herbicides, organic farmers must rely on a host of ecological weed management (EWM) practices to control weeds. Despite EWM’s ability to improve soil quality, the perceived rate of integrated EWM strategy adoption remains low. This low adoption is likely a result of the complexity in designing and evaluating EWM strategies, the tendency for outreach to focus on the risks of EWM strategies rather than their benefits, and a lack of quantitative measures linking the performance of EWM strategies to farmers’ on-farm objectives and practices. Here we report on the development and deployment of an easy-to-use online decision support tool (DST) that aids organic farmers in identifying their on-farm objectives, characterizing the performance of their practices, and evaluating EWM strategies recommended by an expert advisory panel. Informed by the principles of structured decision making, the DST uses multiple choice tasks to help farmers evaluate the short- and long-term trade-offs of EWM strategies, while also focusing their attention on their most important objectives. We then invited organic farmers across the United States, in particular those whose email addresses were registered on the USDA’s Organic Research Integrity Database, to engage the DST online. Results show considerable movement in participants’ (n = 45) preferences from practices focused on reducing weeding costs and labor in the short term to EWM strategies focused on improving soil quality in the long term. Indeed, nearly half of those farmers (48%) who initially ranked a strategy composed of their current practices highest ultimately preferred a better-performing EWM strategy focused on eliminating the weed seedbank over 5 yr.
To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient–clinician shared decision-making about care and RCT enrollment, based on “mathematical equipoise.”
As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis.
With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making.
The KOMET predictive regression model for knee pain had four patient-specific variables, and an r2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received.
Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.
In multi-domain product development organizations, there is a continuous need to transfer captured knowledge between engineers to enable better design decisions in the future. The objective of this paper is to evaluate how engineering knowledge can be captured, disseminated and (re)used by applying a knowledge reuse tool entitled Engineering Checksheet (ECS). The tool was introduced in 2012 and this evaluation has been performed over the 2017–2018 period. This case study focused on codified knowledge in incremental product development with a high reuse potential both in and over time. The evaluation draws conclusions from the perspectives of the knowledge workers (the engineers), knowledge owners and knowledge managers. The study concludes that the ECS has been found to be valuable in enabling a timely understanding of technological concepts related to low level engineering tasks in the product development process. Hence, this enables knowledge flow and, in particular, reuse among inexperienced engineers, as well as providing quick and accurate quality control for experienced engineers. The findings regarding knowledge ownership and management relate to the need for clearly defining a knowledge owner structure in which communities of practice take responsibility for empowering engineers to use ECS and as knowledge evolves managing updates to the ECS.
Precision technologies and data have had relatively modest impacts in grass-based livestock ruminant production systems compared with other agricultural sectors such as arable. Precision technologies promise increased efficiency, reduced environmental impact, improved animal health, welfare and product quality. The benefits of precision technologies have, however, been relatively slow to be realised on pasture based farms. Though there is significant overlap with indoor systems, implementing technology in grass-based dairying brings unique opportunities and challenges. The large areas animals roam and graze in pasture based systems and the associated connectivity challenges may, in part at least, explain the comparatively lower adoption of such technologies in pasture based systems. With the exception of sensor and Bluetooth-enabled plate metres, there are thus few technologies designed specifically to increase pasture utilisation. Terrestrial and satellite-based spectral analysis of pasture biomass and quality is still in the development phase. One of the key drivers of efficiency in pasture based systems has thus only been marginally impacted by precision technologies. In contrast, technological development in the area of fertility and heat detection has been significant and offers significant potential value to dairy farmers, including those in pasture based systems. A past review of sensors in health management for dairy farms concluded that although the collection of accurate data was generally achieved, the processing, integration and presentation of the resulting information and decision-support applications were inadequate. These technologies’ value to farming systems is thus unclear. As a result, it is not certain that farm management is being sufficiently improved to justify widespread adoption of precision technologies currently. We argue for a user need-driven development of technologies and for a focus on how outputs arising from precision technologies and associated decision support applications are delivered to users to maximise their value. Further cost/benefit analysis is required to determine the efficacy of investing in specific precision technologies, potentially taking account of several yet to ascertained farm specific variables.
Outcomes of adverse events in home care are varied and multifactorial. This study tested a framework combining two health measures to identify home care recipients at higher risk of long-term care placement or death within one year. Both measures come from the Resident Assessment Instrument-Home Care (RAI-HC), a standardized comprehensive clinical assessment. Persons scoring high in the Method for Assigning Priority Levels (MAPLe) algorithm and Changes in Health, End-stage disease, Signs and Symptoms (CHESS) scale were at the greatest risk of placement or death and more than twice as likely to experience either outcome earlier than others. The target group was more likely to trigger mood, social relationship, and caregiver distress issues, suggesting mental health and psychosocial interventions might help in addition to medical care and/or personal support services. Home care agencies can use this framework to identify home care patients who may require a more intensive care coordinator approach.
Precision means being exact and accurate and is an important management component for cropping systems. However, precision does not mean integration, which encompasses spatial and temporal dimensions and is a necessary practice rivaling precision. True IWM merges precision and integration by incorporating advanced technology that allows for greater flexibility of inputs and enhanced responsiveness to field conditions. Examples of this approach are non-existent due to a lack of suitable technological tools and a need for a paradigm shift. Herein a potential model startup company is offered as a guide to advance beyond precision weed control to true integration. The critical components of such a company include grower connections, investor support, proven and reliable technology, and adaptability and innovation in the agricultural technology market. The company with the vision and incentive to make true IWM a reality will be the first to more fully integrate available tools using technology, thus helping many growers overcome ongoing challenges associated with resistance, soil erosion, drift, and weed seedbanks.
This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.
Introduction/Innovation Concept: Utilization of CT imaging has increased dramatically over the past two decades, but has not necessarily improved patient outcomes. As healthcare spending grows unsustainably and evidence of harms from unnecessary testing accrues, there is pressure to improve imaging appropriateness. However, prior attempts to reduce unnecessary imaging using evidence-based guidelines have met with limited success, with common barriers cited including a lack of confidence in patient outcomes, medicolegal risk, and patient expectations. This project attempts to address these barriers through the development of an electronic clinical decision support (CDS) tool embedded in clinical practice. Methods: An interactive web-based point-of-care CDS tool was incorporated into computerized physician order entry software to provide real-time evidence-based guidance to emergency physicians for select clinical indications. For patients with mild traumatic brain injury (MTBI), decision support for the Canadian CT Head Rule pops up when a CT head is ordered. For patients with suspected pulmonary embolism (PE), the tool is triggered when a CT pulmonary angiogram is ordered and provides CDS for the Pulmonary Embolism Rule-out Criteria (PERC), Wells Score, age-adjusted D-dimer and CT imaging. To study the impact of the tool, all emergency physicians in the Calgary zone were randomized to receive voluntary decision support for either MTBI or PE. Curriculum, Tool, or Material: The tool uses a multifaceted approach to inform physician decision making, including visualization of risk and quantitative outcomes data and links to primary literature. The CDS tool simultaneously documents guideline compliance in the health record, generates printable patient education materials, and populates a REDCap™ database, enabling the creation of confidential physician report cards on CT utilization, appropriateness and diagnostic yield for both audit and feedback and research purposes. Preliminary data show that physicians are using the MTBI CDS approximately 30% of the time, and the PE CDS approximately 40% of the time. Evaluation of CDS impact on imaging utilization and appropriateness is ongoing. Conclusion: A voluntary web-based point-of-care decision support tool embedded in workflow has the potential to address many of the factors typically cited as barriers to use of evidence-based guidelines in practice. However, high rates of adherence to CDS will likely require physician incentives and appropriateness measures.