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Highlights • H&N radiation treatment plan directives are typically not patient-specific. • Patient-specific directives may facilitate the best-achievable dose distribution. • Use of an AI-guided tool significantly improved achieved dose for nearly all OARs., Background and purpose Volumetric modulated arc therapy (VMAT) planning for head and neck cancer is a complex process. While the lowest achievable dose for each individual organ-at-risk (OAR) is unknown a priori, artificial intelligence (AI) holds promise as a tool to accurately estimate the expected dose distribution for OARs. We prospectively investigated the benefits of incorporating an AI-based decision support tool (DST) into the clinical workflow to improve OAR sparing. Materials and methods The DST dose prediction model was based on 276 institutional VMAT plans. Under an IRB-approved prospective trial, the physician first generated a custom OAR directive for 50 consecutive patients (physician directive, PD). The DST then estimated OAR doses (AI directive, AD). For each OAR, the treating physician used the lower directive to form a hybrid directive (HD). The final plan metrics were compared to each directive. A dose difference of 3 Gray (Gy) was considered clinically significant. Results Compared to the AD and PD, the HD reduced OAR dose objectives by more than 3 Gy in 22% to 75% of cases, depending on OAR. The resulting clinical plan typically met these lower constraints and achieved mean dose reductions between 4.3 and 16 Gy over the PD, and 5.6 to 9.1 Gy over the AD alone. Dose metrics achieved using the HD were significantly better than institutional historical plans for most OARs and NRG constraints for all OARs. Conclusions The DST facilitated a significantly improved treatment directive across all OARs for this generalized H&N patient cohort, with neither the AD nor PD alone sufficient to optimally direct planning.

Peer support specialists (i.e., lay interventionists representing one of the fastest-growing mental health workforce) are increasingly using technologies to support individuals with mental health challenges between clinical encounters. The use of technology by peers has been significantly increased During COVID-19. Despite the wide array of technologies available, there is no framework designed specifically for peer support specialists and service users to select technologies to support their personal recovery. The objective of the study was to develop a Decision-Support Tool for Peer Support Specialists and Service Users to facilitate shared decision-making when choosing technologies to support personal recovery. The study used an iterative co-production process, including item formulation and a series of group cognitive interviews with peer support specialists and service users (n=9; n=9, n=4). The total sample included 22 participants: peer support specialists (n=18, 81.8%) and service users (n=4, 18.2%). The final version of the Decision-Support Tool for Peer Support Specialists and Service Users (D-SPSS), includes 8 domains: (1) privacy and security; (2) cost; (3) usability; (4) accessibility; (5) inclusion and equity; (6) recovery principles; (7) personalized for service users’ needs; and (8) device set-up. Our study found that involving peer support specialists and service users in the design and co-production phase of a decision-support tool is feasible and has the potential to empower both peer support specialists and service users, and potentially increase engagement in the use of technologies that support individuals’ recovery from traditional clinical encounters. Experience Framework This article is associated with the Innovation & Technology lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens

There have been several pleas for more inclusive ecosystem services assessments in recent years. This is partially due to a growing consensus about the importance of incorporating value pluralism into ecosystem services assessments. While there is increasingly attention for such value pluralism in academia and at the IPBES science policy interface, this concern barely reached many conservation practitioners, as indicated in studies reviewing decision support tools for ecosystem services. We examined six review studies of such tools and this revealed there is indeed little attention for value pluralism and only a part of the tools provides the conceptual space to incorporate value pluralism. The growing scientific and science-policy consensus that recognizing value pluralism is key for inclusive assessments can only be translated in actions if there is a connection to the field. Users of ecosystem services assessment tools should have the opportunity to select value-inclusive tools, as this can lead to a stronger support base for conservation actions, prevent conflict, and lead to more comprehensive ecosystem services assessments. This does not imply that all tools need to focus on values; the actual goal of a specific ecosystem services assessment tool is context-specific. If scientists want the plural valuation debate to have an impact on conservation practices, three conditions need to be fulfilled: (i) developing value-inclusive decision-support tools; (ii) clarifying when to use value inclusive tools; and (iii) learning from and sharing of value-inclusive tools.

PurposeTo develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy.MethodsFrom a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relative importance of each feature in the applicator selection process was determined.ResultsFeature selection indicated that the mean and maximum lateral and vertical extent, volume, and axis offset of the HR-CTV were the most informative features and were thus provided to the ML models. Relative feature importances indicated that the HR-CTV volume and mean lateral extent were most important for applicator selection. From the comparison of the individual classification algorithms, it was found that the highest performing algorithms were tree-based ensemble methods – AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). The accuracy of the individual models was compared to the voting model for 100 iterations (ABC = 91.6 ± 3.1%, GBC = 90.4 ± 4.1%, RFC = 89.5 ± 4.0%, Voting Model = 92.2 ± 1.8%) and the voting model was found to have superior accuracy. Over the final 1,000 evaluation iterations, the final voting model demonstrated a high predictive accuracy (91.5 ± 0.9%) and F1 Score (90.6 ± 1.1%).ConclusionThe presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation.

The contemporary ‘digital age’ prompts the need for a re-assessment of urban planning principles and practices. Against the background of current data-rich urban planning, this study seeks to address the question whether an appropriate methodological underpinning can be provided for smart city governance based on a data-driven planning perspective. It posits that the current digital technology age has a drastic impact on city strategies and calls for a multi-faceted perspective on future urban development, termed here the ‘XXQ-principle’ (which seeks to attain the highest possible level of quality for urban life). Heterogeneity in urban objectives and data embodied in the XXQ-principle can be systematically addressed by a process of data decomposition (based on a ‘cascade principle’), so that first, higher-level urban policy domains are equipped with the necessary (‘big’) data provisions, followed by lower-ranking urban governance levels. The conceptual decomposition principle can then be translated into a comprehensive hierarchical model architecture for urban intelligence based on the ‘flying disc’ model, including key performance indicators (KPIs). This new model maps out the socio-economic arena of a complex urban system according to the above cascade system. The design of this urban system architecture and the complex mutual connections between its subsystems is based on the ‘blowing-up’ principle that originates from a methodological deconstruction-reconstruction paradigm in the social sciences. The paper advocates the systematic application of this principle to enhance the performance of smart cities, called the XXQ performance value. This study is not empirical, although it is inspired by a wealth of previous empirical research. It aims to advance conceptual and methodological thinking on principles of smart urban planning.

The purpose of analyzing data is to transform it into useful knowledge. Descriptive analytics renders factual information about research and events that can be used to relate an organization's environment to its activities. However, descriptive analytics alone is not enough to gain understanding and possibly predict the future. Minding only the output of such an analysis can mislead the researcher and decisionmaker. Because many factors influence results, it is essential to advance the prediction of future challenges through statistical analytics and factual patterns that dictate the environment with scientifically tested models. The data patterns, types of analysis, and attributes the prediction will be based on are all important. Data influenced by unforeseen variables make for poor predictions, such as the evening capacity report data in this study.

Background Ministries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative. Methods Three electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making. Results Data from 300 health facilities in four health districts was collected and analyzed (November 2014—June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7—100% of all health facilities achieved all the required items for each indicator. Conclusions Our experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.

Given the marked heterogeneous conditions in smallholder agriculture in Sub-Saharan Africa, there is a growing policy interest in site-specific extension advice and the use of digital extension tools to provide site-specific information. Empirical ex-ante studies on the design of digital extension tools and their use are rare. Using data from a choice experiment in Nigeria, we elicit and analyze the preferences of extension agents for major design features of ICT-enabled decision support tools (DSTs) aimed at site-specific nutrient management extension advice. We estimate different models, including mixed logit, latent class and attribute non-attendance models. We find that extension agents are generally willing to use such DSTs and prefer a DST with a more user-friendly interface that requires less time to generate results. We also find that preferences are heterogeneous: some extension agents care more about the effectiveness-related features of DSTs, such as information accuracy and level of detail, while others prioritise practical features, such as tool platform, language and interface ease-of-use. Recognising and accommodating such preference differences may facilitate the adoption of DSTs by extension agents and thus enhance the scope for such tools to impact the agricultural production decisions of farmers. ispartof: JOURNAL OF AGRICULTURAL ECONOMICS vol:71 issue:3 pages:798-815 ispartof: location:England status: published

Background The vision of transforming health systems into learning health systems (LHSs) that rapidly and continuously transform knowledge into improved health outcomes at lower cost is generating increased interest in government agencies, health organizations, and health research communities. While existing initiatives demonstrate that different approaches can succeed in making the LHS vision a reality, they are too varied in their goals, focus, and scale to be reproduced without undue effort. Indeed, the structures necessary to effectively design and implement LHSs on a larger scale are lacking. In this paper, we propose the use of architectural frameworks to develop LHSs that adhere to a recognized vision while being adapted to their specific organizational context. Architectural frameworks are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution. Because these frameworks support the analysis of LHSs and allow their outcomes to be simulated, they act as pre-implementation decision-support tools that identify potential barriers and enablers of system development. They thus increase the chances of successful LHS deployment. Discussion We present an architectural framework for LHSs that incorporates five dimensions—goals, scientific, social, technical, and ethical—commonly found in the LHS literature. The proposed architectural framework is comprised of six decision layers that model these dimensions. The performance layer models goals, the scientific layer models the scientific dimension, the organizational layer models the social dimension, the data layer and information technology layer model the technical dimension, and the ethics and security layer models the ethical dimension. We describe the types of decisions that must be made within each layer and identify methods to support decision-making. Conclusion In this paper, we outline a high-level architectural framework grounded in conceptual and empirical LHS literature. Applying this architectural framework can guide the development and implementation of new LHSs and the evolution of existing ones, as it allows for clear and critical understanding of the types of decisions that underlie LHS operations. Further research is required to assess and refine its generalizability and methods.

We modeled and combined outputs for 125 tree species for the eastern United States, using habitat suitability and colonization potential models along with an evaluation of adaptation traits. These outputs allowed, for the first time, the compilation of tree species&rsquo, current and future potential for each unit of 55 national forests and grasslands and 469 1 &times, 1 degree grids across the eastern United States. A habitat suitability model, a migration simulation model, and an assessment based on biological and disturbance factors were used with United States Forest Service Forest Inventory and Analysis data to evaluate species potential to migrate or infill naturally into suitable habitats over the next 100 years. We describe a suite of variables, by species, for each unique geographic unit, packaged as summary tables describing current abundance, potential future change in suitable habitat, adaptability, and capability to cope with the changing climate, and colonization likelihood over 100 years. This resulting synthesis and summation effort, culminating over two decades of work, provides a detailed data set that incorporates habitat quality, land cover, and dispersal potential, spatially constrained, for nearly all the tree species of the eastern United States. These tables and maps provide an estimate of potential species trends out 100 years, intended to deliver managers and publics with practical tools to reduce the vast set of decisions before them as they proactively manage tree species in the face of climate change.

Biodiversity offsetting, or compensatory mitigation, is increasingly being used in temperate grassland ecosystems to compensate for unavoidable environmental damage from anthropogenic developments such as transportation infrastructure, urbanization, and energy development. Pursuit of energy independence in the United States will expand domestic energy production. Concurrent with this increased growth is increased disruption to wildlife habitats, including avian displacement from suitable breeding habitat. Recent studies at energy‐extraction and energy‐generation facilities have provided evidence for behavioral avoidance and thus reduced use of habitat by breeding waterfowl and grassland birds in the vicinity of energy infrastructure. To quantify and compensate for this loss in value of avian breeding habitat, it is necessary to determine a biologically based currency so that the sufficiency of offsets in terms of biological equivalent value can be obtained. We describe a method for quantifying the amount of habitat needed to provide equivalent biological value for avifauna displaced by energy and transportation infrastructure, based on the ability to define five metrics: impact distance, impact area, pre‐impact density, percent displacement, and offset density. We calculate percent displacement values for breeding waterfowl and grassland birds and demonstrate the applicability of our avian‐impact offset method using examples for wind and oil infrastructure. We also apply our method to an example in which the biological value of the offset habitat is similar to the impacted habitat, based on similarity in habitat type (e.g., native prairie), geographical location, land use, and landscape composition, as well as to an example in which the biological value of the offset habitat is dissimilar to the impacted habitat. We provide a worksheet that informs potential users how to apply our method to their specific developments and a framework for developing decision‐support tools aimed at achieving landscape‐level conservation goals.

Modern infrastructure systems form complex networks that are organized hierarchically in communities of tightly integrated elements. This paper presents three new community-based metrics to identify the critical elements of a network system. Two of these metrics assess intracommunity and intercommunity behavior for any community structure, and the third metric accounts for the multiple levels of community structure. First, these metrics are studied to establish their characteristics with different community structures, and then the Great Britain Railway Network is used as a case study to demonstrate the usefulness of these new metrics. The results show that an assessment of the system using these metrics leads to the identification of not only those elements that are critical at the global level, but also those that greatly affect the local performance of the communities. Such identification of the critical components at the community and global levels would enable a better understanding of system behavior by stakeholders with competing demands.

Introduction: This paper aims to develop a data warehouse (AD-SISCOLO) in order to support the management of the cervical cancer screening program in the municipality of Rio de Janeiro/Brazil. As a part of the management process, the program managers of the municipality perform tedious manual work in order to calculate a series of performance indicators and then take decisions based on them. Methods AD-SISCOLO was implemented using the Pentaho BI Suite Business Intelligence Platform and the MySQL database management system. The indicators to be calculated and visualized in the tool were based on the municipal data of the cytopathology and histopathology tests from January 2012 until December 2014, which was obtained from the Information System of Cervical Cancer (SISCOLO) after a record linkage process. The follow-up indicators were based on a simplified version of the Brazilian guidelines for the cervical cancer screening. Results AD-SISCOLO allows the visualization of a set of test-based and follow-up indicators from different views and dimensions, which enable managers to monitor all the phases of the screening process and to identify the process’ failures. Conclusions Compared with the current available environments in Brazil, AD-SISCOLO is unique in its visualization of the follow-up indicators of groups of women, according to their test results and age. Thereby it provides presentation flexibility to suit the program manager's needs.

Sewers, water pipes, and streets are elements of our civil infrastructure, the supporting structure of society. Infrastructure is a complex technical system that provides us with a varied range of essential services; a storehouse of resources and wealth that each generation inherits, uses, and passes on to succeeding generations. The asset management has a big influence on infrastructure development and use: undertaken and executed without fully recognizing the complexity, diversity, and social and technological evolution of the system almost inevitably squander economic, environmental, social, and cultural resources. The challenges of managing these assets most effectively are substantial: the inefficiencies are widespread and really easy to see: jammed traffic on roads designed to carry only a fraction of the current demand, newly-resurfaced city streets open to repair aged subsurface pipes, basements flooded in case of insistent heavy rain, etc. In existing asset management systems often information is not efficiently used in decisional process, which results in much waste in time and effort. It is necessary to develop life-cycle management systems of infrastructure to overcome this problem. The system must integrate geographic information, design data, inspection and maintenance data. Emphasis is placed on development of decision-support tools for municipal infrastructure management. The study identifies the challenges for maintenance, repair and renewal planning faced by asset owners and managers. Integration with existing systems such as Computerized Maintenance Management Systems, Geographic Information Systems, is seen as the largest challenge for developing and using decisionsupport tools in the area of asset management.

Background In many countries, the responsibility for planning and delivery of health services is devolved to the subnational level. Health programs, however, often fall short of efficient use of data to inform decisions. As a result, programs are not as effective as they can be at meeting the health needs of the populations they serve. In Kenya, a decision-support tool, the District Health Profile (DHP) tool was developed to integrate data from health programs, primarily HIV, at the district level and to enable district health management teams to review and monitor program progress for specific health issues to make informed service delivery decisions. Methods Thirteen in-depth interviews were conducted with ten tool users and three non-users in six districts to qualitatively assess the process of implementing the tool and its effect on data-informed decision making at the district level. The factors that affected use or non-use of the tool were also investigated. Respondents were selected via convenience sample from among those that had been trained to use the DHP tool except for one user who was self-taught to use the tool. Selection criteria also included respondents from urban districts with significant resources as well as respondents from more remote, under-resourced districts. Results Findings from the in-depth interviews suggest that among those who used it, the DHP tool had a positive effect on data analysis, review, interpretation, and sharing at the district level. The automated function of the tool allowed for faster data sharing and immediate observation of trends that facilitated data-informed decision making. All respondents stated that the DHP tool assisted them to better target existing services in need of improvement and to plan future services, thus positively influencing program improvement. Conclusions This paper stresses the central role that a targeted decision-support tool can play in making data aggregation, analysis, and presentation easier and faster. The visual synthesis of data facilitates the use of information in health decision making at the district level of a health system and promotes program improvement. The experience in Kenya can be applied to other countries that face challenges making district-level, data-informed decisions with data from fragmented information systems.

International audience; Indicators are widely promoted as means to monitor ecosystem status or to evaluate fisheries management performance. "Which indicators are most relevant as decision-support tools in fisheries management?" still remains a topical question. Indicators should be metrics related to fish populations and fleets and should be sensitive to management strategies. However, given the complexity of the processes involved, it is often difficult to unequivocally interpret variations in metrics. A simulation approach was used to study metric properties and to identify robust and relevant fishery indicators. By applying sensitivity analysis methods, simulation designs were built that cross a variety of management scenarios and uncertainty hypotheses. Bio-economic outputs were simulated using a mechanistic model (ISIS-Fish), and their properties were statistically analyzed. This approach was applied to the pelagic fishery of the Bay of Biscay. The analysis of metric properties highlighted the major factors driving variations in each metric and identified the important sources of uncertainty that need to be reduced to allow the use of metrics as indicators. Although very few metrics gave robust indications of management performance, sensitivity indices evidenced how management performances could be improved, and spatially disaggregated metrics provided insights into the mechanisms underlying management performance.

Debris generated by disasters can hinder relief efforts and result in devastating economic, environmental and health problems. In this paper, we present a decision-support tool to assist disaster and waste management officials with the collection, transportation, reduction, recycling, and disposal of debris. The tool enables optimizing and balancing the financial and environmental costs, duration of the removal operations, landfill usage, and the amount of recycled materials generated. It can support post-disaster operational decisions as well as the challenging task of developing strategic plans for disaster preparedness.

Different vessel types for transferring technicians for maintenance and inspection of offshore wind farms are often evaluated and compared by their limiting significant wave height for accessing the wind turbines. The limiting significant wave height is also the parameter that is often used as the access criteria in strategic decision support tools for maintenance and logistics for offshore wind farms. In practice, however, other wave parameters, such as the peak wave period and wave heading, have major influence on the accessibility to a wind turbine for a given vessel. We compare the use of single-parameter and multi-parameter wave criteria for access to wind turbines in two strategic maintenance and logistics models for offshore wind farms: one simulation model and one optimization model. Multi-parameter wave criteria in the form of limiting significant wave heights as functions of peak wave period and wave heading are obtained by numerical analysis of the vessel docking operation. Results for availability, operation and maintenance costs and the optimal vessel fleet size and mix are found using both these multi-parameter wave criteria and using a corresponding single-parameter limiting significant wave height. The comparison indicates that the use of a single limiting significant wave height can give similar results as when using more complex multi-parameter wave criteria. An important precondition is that the single limiting significant wave height is carefully chosen to represent the vessel and the wave conditions. (c) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/)