Integrated Decision Support Research Articles

Air Quality Warning and Integrated decision Support system for Emissions (AIRWISE): Enhancing Air Quality management in Megacities

Air Quality Warning and Integrated decision Support system for Emissions (AIRWISE): Enhancing Air Quality management in Megacities

Extending multi-criteria coastal vulnerability assessment to low-lying inland areas: Examples from Estonia, eastern Baltic Sea

The assessment of vulnerability to coastal hazards is a significant coastal management problem in regions with complicated shoreline, such as Estonia. This study implements the vulnerability assessment based on the multi-criteria decision analysis using fuzzy logic, analytical hierarchy process, and weighted linear combination (including input from experts) integrated with a geographical information system, to map the coastal vulnerability index (CVI) of the Estonian coasts at high resolution based on 16 parameters. The novelty of our approach is that we expand this assessment to a 2 km wide inland area that is an intrinsic but often overlooked part of coastal vulnerability estimates. The Estonian shores have mostly low and moderate vulnerability. Short segments with high vulnerability are impacted by severe waves and highly elevated water levels. The CVI also characterizes low-lying areas, such as large river valleys, reasonably well. Estimates of coastal vulnerability based on the three most important parameters according to experts’ judgements provide a reasonable approximation of the 16-parameter CVI in mostly homogeneous coastal regions, but less so elsewhere where its value is questioned. The results show that the application of the developed integrated decision support system, applied to a 2 km wide coastal strip, provides more information than single tools to assist coastal managers and stakeholders in planning, preparing for and responding to hazards.

Modelling an Integrated Fuzzy Based Decision Support System for an Anaesthetist during Pre-Operative Clinical Assessment

Pre-operative clinical assessment is a crucial responsibility for every anaesthetist before any major medical procedure. This assessment evaluates a patient’s suitability for anaesthesia and their overall physical condition to endure the stresses of surgery. This is especially vital given recent data linking many surgical deaths to improper anaesthesia administration, with a mortality rate of 20 deaths per 10,000 anaesthetics in developed countries. This paper proposes an integrated fuzzy-based decision support system to assist anaesthetists during pre-operative clinical assessments. The approach combines neural network algorithms to classify and normalize input data, which then feeds into a fuzzy logic system for multi-variable decision-making analysis. To evaluate this decision support system, patient anaesthesia assessment datasheets were sourced from Ibom Multi Specialist Hospital in Akwa Ibom State, along with an online dataset from electronic anaesthesia records (MetaVision, 1MDsoft) of adult patients undergoing surgery at a major academic medical center. The study focused on patients over 18 years old undergoing non-cardiac surgeries, taking into account factors such as urgency and type of anaesthesia. The next step involves a decision-making process that assesses patient suitability for surgical anaesthesia based on established rules and membership functions developed through the fuzzy inference system. The results from this integrated decision support system were found to be reliable and consistent with anaesthetists’ assessments. The system achieved an accuracy of 91.46%, as indicated by a ROC curve comparing sensitivity and specificity, demonstrating its effectiveness in evaluating patients for medical procedures. To enhance the training of future anaesthesiologists, the introduction of artificial intelligence into the curriculum is recommended, along with the application of evolutionary algorithms like genetic algorithms and programming. These strategies aim to significantly enrich the decision-making processes employed by anaesthetists. Keywords: Fuzzy logic, neural network, anaesthesia, artificial intelligence

The Impact of International Classification of Disease-Triggered Prescription Support on Telemedicine: Observational Analysis of Efficiency and Guideline Adherence.

Integrating decision support systems into telemedicine may optimize consultation efficiency and adherence to clinical guidelines; however, the extent of such effects remains underexplored. This study aims to evaluate the use of ICD (International Classification of Disease)-coded prescription decision support systems (PDSSs) and the effects of these systems on consultation duration and guideline adherence during telemedicine encounters. In this retrospective, single-center, observational study conducted from October 2021 to March 2022, adult patients who sought urgent digital care via direct-to-consumer video consultations were included. Physicians had access to current guidelines and could use an ICD-triggered PDSS (which was introduced in January 2022 after a preliminary test in the preceding month) for 26 guideline-based conditions. This study analyzed the impact of implementing automated prescription systems and compared these systems to manual prescription processes in terms of consultation duration and guideline adherence. This study included 10,485 telemedicine encounters involving 9644 patients, with 12,346 prescriptions issued by 290 physicians. Automated prescriptions were used in 5022 (40.67%) of the consultations following system integration. Before introducing decision support, 4497 (36.42%) prescriptions were issued, which increased to 7849 (63.57%) postimplementation. The physician's average consultation time decreased significantly to 9.5 (SD 5.5) minutes from 11.2 (SD 5.9) minutes after PDSS implementation (P<.001). Of the 12,346 prescriptions, 8683 (70.34%) were aligned with disease-specific international guidelines tailored for telemedicine encounters. Primary medication adherence in accordance with existing guidelines was significantly greater in the decision support group than in the manual group (n=4697, 93.53% vs n=1389, 49.14%; P<.001). Most of the physicians adopted the PDSS, and the results demonstrated the use of the ICD-code system in reducing consultation times and increasing guideline adherence. These systems appear to be valuable for enhancing the efficiency and quality of telemedicine consultations by supporting evidence-based clinical decision-making.

An integrated decision support system for ship collision avoidance

The ship encounter process is divided into the stages of free action, risk of collision, close-quarter, and immediate danger according to the four-stage theory of ship encounters. Current research on ship collision avoidance primarily focuses on the risk of collision stage, aiming to prevent ships from entering the close-quarter stage. However, various navigational equipment and environmental factors may cause ships to enter the close-quarter or immediate danger stages. Therefore, this paper establishes an integrated decision support system for the above stages to ensure that ships can handle all situations. First, the system includes a new collision risk detection module based on the ship domain model and the four-stage theory. Subsequently, in the collision avoidance strategy formulation module, two sets of avoidance strategies are established based on the convention on the COLREGs and the most effective avoidance principle for ships in different stages. Finally, in the avoidance action optimization module, a new fitness function is constructed based on ship maneuverability, and the sparrow search algorithm is introduced to solve for the optimal avoidance actions. The results demonstrate the capability of the decision support system presented in this paper to effectively avoid other ships in different stages of the encounter process.

Electronic health record system use and documentation burden of acute and critical care nurse clinicians: a mixed-methods study.

Examine electronic health record (EHR) use and factors contributing to documentation burden in acute and critical care nurses. A mixed-methods design was used guided by Unified Theory of Acceptance and Use of Technology. Key EHR components included, Flowsheets, Medication Administration Records (MAR), Care Plan, Notes, and Navigators. We first identified 5 units with the highest documentation burden in 1 university hospital through EHR log file analyses. Four nurses per unit were recruited and engaged in interviews and surveys designed to examine their perceptions of ease of use and usefulness of the 5 EHR components. A combination of inductive/deductive coding was used for qualitative data analysis. Nurses acknowledged the importance of documentation for patient care, yet perceived the required documentation as burdensome with levels varying across the 5 components. Factors contributing to burden included non-EHR issues (patient-to-nurse staffing ratios; patient acuity; suboptimal time management) and EHR usability issues related to design/features. Flowsheets, Care Plan, and Navigators were found to be below acceptable usability and contributed to more burden compared to MAR and Notes. The most troublesome EHR usability issues were data redundancy, poor workflow navigation, and cumbersome data entry based on unit type. Overall, we used quantitative and qualitative data to highlight challenges with current nursing documentation features in the EHR that contribute to documentation burden. Differences in perceived usability across the EHR documentation components were driven by multiple factors, such as non-alignment with workflows and amount of duplication of prior data entries. Nurses offered several recommendations for improving the EHR, including minimizing redundant or excessive data entry requirements, providing visual cues (eg, clear error messages, highlighting areas where missing or incorrect information are), and integrating decision support. Our study generated evidence for nurse EHR use and specific documentation usability issues contributing to burden. Findings can inform the development of solutions for enhancing multi-component EHR usability that accommodates the unique workflow of nurses. Documentation strategies designed to improve nurse working conditions should include non-EHR factors as they also contribute to documentation burden.

Integrated Geospatial and Geostatistical Multi-Criteria Evaluation of Urban Groundwater Quality Using Water Quality Indices

Groundwater contamination poses a severe public health risk in Lahore, Pakistan’s second-largest city, where over-exploited aquifers are the primary municipal and domestic water supply source. This study presents the first comprehensive district-wide assessment of groundwater quality across Lahore using an innovative integrated approach combining geographic information systems (GIS), multi-criteria decision analysis (MCDA), and water quality indexing techniques. The core objectives were to map the spatial distributions of critical pollutants like arsenic, model their impacts on overall potability, and evaluate targeted remediation scenarios. The analytic hierarchy process (AHP) methodology was applied to derive weights for the relative importance of diverse water quality parameters based on expert judgments. Arsenic received the highest priority weight (0.28), followed by total dissolved solids (0.22) and hardness (0.15), reflecting their significance as health hazards. Weighted overlay analysis in GIS delineated localized quality hotspots, unveiling severely degraded areas with very poor index values (&gt;150) in urban industrial zones like Lahore Cantt, Model Town, and parts of Lahore City. This corroborates reports of unregulated industrial effluent discharges contributing to aquifer pollution. Prospective improvement scenarios projected that reducing heavy metals like arsenic by 30% could enhance quality indices by up to 20.71% in critically degraded localities like Shalimar. Simulating advanced multi-barrier water treatment processes showcased an over 95% potential reduction in arsenic levels, indicating the requirement for deploying advanced oxidation and filtration infrastructure aligned with local contaminant profiles. The integrated decision support tool enables the visualization of complex contamination patterns, evaluation of remediation options, and prioritizing risk-mitigation investments based on the spatial distribution of hazard exposures. This framework equips urban planners and utilities with critical insights for developing targeted groundwater quality restoration policies through strategic interventions encompassing treatment facilities, drainage infrastructure improvements, and pollutant discharge regulations. Its replicability across other regions allows for tackling widespread groundwater contamination challenges through robust data synthesis and quantitative scenario modeling capabilities.

SIGEPS: A System for Managing the Health Internship Program.

The healthcare sector's demand for a skilled workforce necessitates effective management of internship and residency programs. This study introduces the System for Managing the Health Internship Program (SIGEPS), aimed at addressing internship and residency management challenges in Joinville's municipal health network. The method of this study focused on developing a computational solution with applied and exploratory objectives. The research adopted a User-Centered Development approach, involving seven stages from scope determination to system implementation. Key stages included selecting Google's free technological tools, developing forms for information input, integrating these into a Google Sheets spreadsheet, and creating a website for schedule display. The primary result was the successful development of SIGEPS, which automated the process from initial requests to scheduling and analysis of healthcare institutions and displayed schedules online. The system offers several advantages over previous models, such as concurrent scheduling by multiple employees, process automation, enhanced information transparency, and improved efficiency with significant time savings. Future enhancements include integrating decision support systems and enabling data analysis reports through dashboards to further reduce scheduling efforts and enhance professional efficiency. This study demonstrates the feasibility of developing and implementing a free computational solution using the Google platform for managing internships and residencies in the healthcare sector.

A hybrid approach based on magnitude-based fuzzy analytic hierarchy process for estimating sustainable urban transport solutions

Transport systems are pivotal in sustainable development, profoundly impacting social, environmental, and economic sustainability in urban settings. Integrating decision support systems becomes imperative to underscore the necessity for change and facilitate informed policy decisions based on current conditions. This study evaluates Budapest, Hungary's urban public transport system to foster sustainability. Experts in the transportation field in Budapest were engaged as evaluators. The study employs the newly developed Magnitude Based Fuzzy Analytic Hierarchy Process, chosen for its accuracy and computational efficiency compared to existing methods. The obtained results align with those from the Modified Fuzzy Logarithmic Least Squares method, affirming its reliability. Comparative and sensitivity analyses validate, ensure consistency, and test the robustness of the proposed model findings. The study concludes that introducing new buses emerges as the most viable solution for enhancing the service quality of the existing transport system. The adopted results on a real dataset, its application in transportation evaluation, and the recommendation of new buses as an optimal solution offer insights into sustainable urban transport development.

Selection of track form in railway tunnel from a life cycle analysis perspective

AbstractThe use of greenhouse gas (GHG) emissions as a criterion for decision-making within the rail industry is increasing. The demand for considering this criterion affects the type of decision models acceptable by railway infrastructure managers in the planning, construction, and maintenance of railway assets. The total amount of GHG emitted from a track solution in tunnels during its service life depends on the track form (i.e., ballasted track or ballastless track), the type of construction, maintenance machines used, current traffic profile, and tunnel length. However, the development in the design of ballastless track systems during recent decades to make them environmentally friendly motivates infrastructure managers to rethink and consider the use of the system. This study examines the effect of several design and maintenance factors not adequately addressed in previous research. These factors are (i) the modulus of elasticity of track support affecting the design of track forms, (ii) differences in maintenance and renewal required for track forms in the corresponding line condition, and (iii) recent developments in optimizing the environmental impact of ballastless tracks. The GHG emissions, represented by life cycle carbon dioxide equivalent (CO2e) emissions, are calculated using the climate impact software developed by the Swedish Transport Administration Trafikverket. The result is compared with the estimated emission from the conventional ballasted tracks. The method proposed in this paper is applied in a case study to study the effect of applying the optimized ballastless track system Rheda 2000® in a railway tunnel (the Hallsberg-Stenkumla tunnel) as part of a new line project in Sweden. The model applied in the study is an integral part of an integrated decision support system for effectively selecting track solutions from a lifecycle perspective. The study´s findings are: (i) the life cycle CO2 equivalent emissions by a ballastless track during its life cycle are 10% lower than that of the ballasted track, (ii) the primary total emission driver for both track form solutions is the emissions generated at the manufacturing of rails. (iii) the second important emission factor for the ballasted track solution is the emission from the renewal of the track form during its life cycle, and (iv) the second important emission factor for the ballastless track solution is concrete manufacturing.

Integrated decision support framework for construction land reduction projects prioritization in China: a multi-criteria decision analysis approach

Integrated decision support framework for construction land reduction projects prioritization in China: a multi-criteria decision analysis approach

Advancing sustainability: An integrated decision support framework for fleet selection in open pit mining construction

The process of selecting and configuring equipment fleets for open-pit mining is complex, requiring consideration of both quantitative and qualitative criteria. This paper presents a novel comprehensive framework for selecting loading/hauling fleets in open-pit mining projects. Through an extensive review of the literature, a total of 21 selection criteria are initially identified. With guidance from local experts, these criteria are shortlisted into five primary groups: site conditions, managerial, social, and environmental criteria. The ranking of the identified selection criteria is carried out by employing the analytical hierarchy process (AHP) and multi-attribute utility theory (MAUT) methods, utilizing a questionnaire survey. The most crucial criteria in selecting loading/hauling equipment are identified as the equipment's operating cost, productivity, repair and maintenance costs, and soil condition. An optimization module is developed to optimize the selected fleet, while adhering to sustainability principles and project constraints. The optimization module considers social, economic, and environmental sustainability variables to determine the optimal number, size, type, and capacity of fleets needed to complete the work within the allotted time. As a case study, the proposed framework is validated by experts in these types of projects. The framework's ability to effectively select fleet units for sustainable loading/hauling fleet is confirmed through this validation method.

Model‐Based Decision Support using Test and Evaluation: A Lightweight Architecture Approach

AbstractA standardized decision support tool to support test prioritization and decision‐making is a prerequisite to achieving on‐time delivery of weapon systems that are adequately tested and vetted by decision makers within distributed organizations such as the defense industrial base. However, to achieve standardization, the core principles of systems thinking and systems engineering must be utilized in order to realize a holistic and evolvable process and product that integrates Test &amp; Evaluation (T&amp;E) data and information with decision‐making in a consistent format to support program offices. To address this need, a model‐based reference architecture for the standardization of the Integrated Decision Support Key (IDSK) data and decision formats is presented in this paper. The IDSK is a DoD‐mandated decision support tool (artifact) for capturing a program's decisions and the T&amp;E information necessary to support the decisions. The digital‐IDSK reference architecture proposed is developed using a model‐based systems engineering approach and facilitates the generation of standardized data and decision formats that are tailorable, adaptable to incremental future changes, and easy to integrate with other digital engineering artifacts all within a digital engineering ecosystem. In addition, this approach is also applicable not only to defense‐based organizations, but also finds utility within organizations pivoting to a digital engineering strategy for informing product T&amp;E decision‐making.

Do Decision Support Tools Decrease the Prevalence of Hospital-Acquired Venous Thromboembolisms When Compared to Clinical Judgement? A Single-Center Pre-Post Study.

Introduction: Hospital-acquired venous thromboembolisms (HA-VTEs) carry a significant health burden on patients and a financial burden on hospitals due to reimbursement penalties. VTE prophylaxis at our institute was performed through utilizing an order set based on healthcare professionals' perceived level of risk. However, the use of standardized risk assessment models is recommended by multiple professional societies. Furthermore, integrating decision support tools (DST) based on the standardized risk assessment models has been shown to increase the administration of appropriate deep vein thrombosis (DVT) prophylaxis. Nonetheless, such scoring systems are not inherently flawless and their integration into EMR as a mandatory step can come at the risk of healthcare professional fatigue and burnout. We conducted a study to evaluate the incidence of HA-VTE and length of stay pre- and post implementation of a DST. Methods: We conducted a retrospective, pre-post-implementation observational study at a tertiary medical center after implementing a mandatory DST. The DST used Padua scores for medical patients and Caprini scores for surgical patients. Patients were identified through ICD-10 codes and outcomes were collected from electronic charts. Healthcare professionals were surveyed through an anonymous survey and stored securely. Statistical analysis was conducted by using R (version 3.4.3). Results: A total of 343 patients developed HA-VTE during the study period. Of these, 170 patients developed HA-VTE in the 9 months following the implementation of the DST, while 173 patients were identified in the 9 months preceding the implementation. There was no statistically significant difference in mean HA-VTE/1000 discharge/month pre- and post implementation (4.4 (SD 1.6) compared to 4.6 (SD 1.2), confidence interval [CI] -1.6 to 1.2, p = 0.8). The DST was used in 73% of all HA-VTE cases over the first 6 months of implementation. The hospital length of stay (LOS) was 14.2 (SD 1.9) days prior to implementation and 14.1 (SD 1.6) days afterwards. No statistically significant change in readmission rates was noted (8.8% (SD 2.6) prior to implementation and 15.53% (SD 9.6) afterwards, CI -14.27 to 0.74, p = 0.07). Of the 56 healthcare professionals who answered the survey, 84% (n = 47) reported to be dissatisfied or extremely dissatisfied with the DST, while 91% (n = 51) reported that it slowed them down. Conclusions: There were no apparent changes in the prevalence of HA-VTE, length of stay, or readmission rates when VTE prophylaxis was mandated through DST compared to a prior model which used order sets based on perceived risk. Further studies are needed to further evaluate the current risk assessment models and improve healthcare professionals' satisfaction with DST.

An Integrated Multi-Criteria Decision Support Model for Sustainable Ship Queuing Policy Application via Vessel Traffic Service (VTS)

An Integrated Multi-Criteria Decision Support Model for Sustainable Ship Queuing Policy Application via Vessel Traffic Service (VTS)

Enhanced Network Security Protection through Data Analysis and Machine Learning: An Application of GraphSAGE for Anomaly Detection and Operational Intelligence

With the Internet's rapid expansion, network security challenges have become increasingly complex and prominent. Traditional protection methods, largely dependent on predefined rules and patterns, demonstrate limited effectiveness against sophisticated and unknown network attacks, failing to harness the full potential of extensive network data. This study addresses the challenges faced by modern cybersecurity, particularly the limitations of traditional defense methods in countering unknown and complex attacks, by proposing a solution that integrates data analysis and machine learning technologies. The focus of this research is placed on network security anomaly detection as well as on intelligent network operations and maintenance exception management based on graph network algorithms, aiming to enhance security defense capabilities and operational efficiency. Specifically, the main contributions and innovations of this paper include: 1. Innovations in sampling, aggregation, and loss functions within the Graph Sample and Aggregation (GraphSAGE) model to improve the accuracy and robustness of the model for network anomaly detection; 2. The introduction of a novel network anomaly root cause analysis and localization model, which, combined with an optimized root cause likelihood assessment method and search scheme, significantly enhances the speed and accuracy of anomaly localization; 3. The design of an integrated decision support system that can automatically adjust protection strategies as network conditions change, achieving a high level of automation and intelligence in cybersecurity management. This work not only provides effective technical support for network security protection but also opens new avenues for future cybersecurity research.

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

The EU Green Deal requires the reduction in pesticides and fertilisers in food crop production, whilst the sustainable development goals require reductions in food loss and food waste. In a complex and interacting system like the food system, these goals are difficult to coordinate. Here, we show an approach using Bayesian network modelling for decision support. Bayesian networks are important tools for modelling complex systems which may develop emergent behaviour and for providing quantitative comparisons for different candidate policies, approaches or interventions under the Integrating Decision Support System paradigm. Using lettuce as an exemplar crop, we demonstrate that expected food loss changes under different agricultural input reduction and integrated pest management combinations can be quantified to aid decision making for growers.

Integrated Decision Support for Disaster Risk Management: Aiding Preparedness and Response Decisions in Wildfire Management

A central challenge in disaster risk management (DRM) is that there are key dependencies and uncertainty between the decisions made at the mitigation, preparedness, response, and recovery stages. Decision support systems for disaster management require information systems that allow timely and reliable integration of data sources from different domains, including information on hazards and vulnerabilities for risk analysis, as well as organizational and logistical information for decision analysis. We propose an analytics-centered framework that integrates predictive and prescriptive models responding to unique characteristics of DRM. The framework relies on probabilistic risk assessment and uses optimization-based simulation of the response phase as a means to inform decisions at the preparedness stage. This paper presents a case study regarding the analysis of preparedness and response decisions for wildfire control in Uruguay. Numerical results illustrate insights from the risk-informed analyses. For instance, slight reductions in the preparedness budget can lead to disproportionate losses during the response stage, whereas slight increases have little effect unless explicitly directed to control high-consequence scenarios. Motivated by a real-world problem, this case study emphasizes the challenges for integrated information systems that enable the potential of analytical decision support frameworks for DRM.

Advancing Efficiency Sustainability in Poultry Farms through Data Envelopment Analysis in a Brazilian Production System.

The production efficiency factor is widely used to measure the zootechnical performance of a batch of broilers. The unit cost of production brings new elements to improve efficiency evaluation and financial sustainability for this activity in agriculture. This research aims to evaluate the production efficiency level of the crop to maximize the return on investment. This study uses Data Envelopment Analysis (DEA) with the computational processing of the SIAD software (Integrated Decision Support System). The variables selected were poultry housing, age at slaughter, feed consumed, mortality, and unit cost. The chosen output variable was the total available weight. The analysis spans 31 decision-making units (DMUs) composed of integrated producers, unveiling a frontier of efficiency delineated by the most exemplary DMUs. Notably, only two DMUs, specifically DMU 4 and DMU 23, approached the threshold of maximum relative efficiency. This research illuminates the critical role of unit cost in enhancing the assessment of production efficiency and financial sustainability within the agriculture environment. By setting benchmarks for efficient management and operational protocols, our findings serve as a cornerstone for improving practices among less efficient DMUs, contributing significantly to the literature on agricultural efficiency and offering actionable insights for the poultry farming sector.

An Integrated Decision Support System for Low-Disturbance Surface Mining

Low-disturbance mining in surface mining (LDM) can transform traditional surface mine production systems into a more sustainable model by reducing the disturbance of surface mining, minimizing pollutant emissions, and reducing ecological impacts. The purpose of this paper is to explore the LDM evaluation method by applying multi-criteria decision-making to provide technical support for LDM implementation. Therefore, an evaluation method based on the combination of the fuzzy analytical hierarchy process (F-AHP) and grey clustering was proposed. Analyzed in terms of the current status of the evaluation indicators (reality) and the significance of the development of the LDM (desirability). Determined the weights and low-disturbance (LD) levels of the evaluation indicators. Combined with the fuzzy technique for order preference by similarity to an ideal solution (F-TOPSIS), the low-disturbance open pit mining paths are ranked, and finally, the decision support system for low-disturbance mining in surface mining is constructed. This study not only enriches the existing literature on related technologies but also lays the foundation for further research on LDM and provides exploratory insights for deeper improvement of LD level in surface mining.