A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm.

The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline's ability to be retrained and used for other datasets collected using different measurements and with different distributions.

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges.

(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing.

Rational utilization of water resources is one of the major methods of water conservation. There are significant differences in the irrigation needs of different agricultural fields because of their spatial variability. Therefore, a decision support system for variable rate irrigation (DSS-VRI) by center pivot was developed. This system can process multi-spectral images taken by unmanned aerial vehicles (UAVs) and obtain the vegetation index (VI). The crop evapotranspiration model (ETc) and crop water stress index (CWSI) were obtained from their established relationships with the VIs. The inputs to the fuzzy inference system were constituted with ETc, CWSI and precipitation. To provide guidance for users, the duty-cycle control map was outputted using ambiguity resolution. The control command contained in the map adjusted the duty cycle of the solenoid valve, and then changed the irrigation amount. A water stress experiment was designed to verify the rationality of the DSS-VRI. The results showed that the more severe water stress is, the more irrigation is obtained, consistent with the expected results. Meanwhile, a user-friendly software interface was developed to implement the DSS-VRI function.

Decision support for using mobile Rapid DNA analysis at the crime scene.

Mobile Rapid DNA technology is close to being incorporated into crime scene investigations, with the potential to identify a perpetrator within hours. However, the use of these techniques entails the risk of losing the sample and potential evidence, because the device not only consumes the inserted sample, it is also is less sensitive than traditional technologies used in forensic laboratories. Scene of Crime Officers (SoCOs) therefore will face a 'time/success rate trade-off' issue when making a decision to apply this technology. In this study we designed and experimentally tested a Decision Support System (DSS) for the use of Rapid DNA technologies based on Rational Decision Theory (RDT). In a vignette study, where SoCOs had to decide on the use of a Rapid DNA analysis device, participating SoCOs were assigned to either the control group (making decisions under standard conditions), the Success Rate (SR) group (making decisions with additional information on DNA success rates of traces), or the DSS group (making decisions supported by introduction to RDT, including information on DNA success rates of traces). This study provides positive evidence that a systematic approach for decision-making on using Rapid DNA analysis assists SoCOs in the decision to use the rapid device. The results demonstrated that participants using a DSS made different and more transparent decisions on the use of Rapid DNA analysis when different case characteristics were explicitly considered. In the DSS group the decision to apply Rapid DNA analysis was influenced by the factors "time pressure" and "trace characteristics" like DNA success rates. In the SR group, the decisions depended solely on the trace characteristics and in the control group the decisions did not show any systematic differences on crime type or trace characteristic. Guiding complex decisions on the use of Rapid DNA analyses with a DSS could be an important step towards the use of these devices at the crime scene.

mHealth App for iOS to Help in Diagnostic Decision in Ophthalmology to Primary Care Physicians.

Decision support systems (DSS) are increasingly demanded due that diagnosis is one of the main activities that physicians accomplish every day. This fact seems critical when primary care physicians deal with uncommon problems belonging to specialized areas. The main objective of this paper is the development and user evaluation of a mobile DSS for iOS named OphthalDSS. This app has as purpose helping in anterior segment ocular diseases' diagnosis, besides offering educative content about ophthalmic diseases to users. For the deployment of this work, firstly it has been used the Apple IDE, Xcode, to develop the OphthalDSS mobile application using Objective-C as programming language. The core of the decision support system implemented by OphthalDSS is a decision tree developed by expert ophthalmologists. In order to evaluate the Quality of Experience (QoE) of primary care physicians after having tried the OphthalDSS app, a written inquiry based on the Likert scale was used. A total of 50 physicians answered to it, after trying the app during 1 month in their medical consultation. OphthalDSS is capable of helping to make diagnoses of diseases related to the anterior segment of the eye. Other features of OphthalDSS are a guide of each disease and an educational section. A 70% of the physicians answered in the survey that OphthalDSS performs in the way that they expected, and a 95% assures their trust in the reliability of the clinical information. Moreover, a 75% of them think that the decision system has a proper performance. Most of the primary care physicians agree with that OphthalDSS does the function that they expected, it is a user-friendly and the contents and structure are adequate. We can conclude that OphthalDSS is a practical tool but physicians require extra content that makes it a really useful one.

A mobile decision support system for red eye diseases diagnosis: experience with medical students.

A good primary health care is the base for a better healthcare system. Taking a good decision on time by the primary health care physician could have a huge repercussion. In order to ease the diagnosis task arise the Decision Support Systems (DSS), which offer counselling instead of refresh the medical knowledge, in a profession where it is still learning every day. The implementation of these systems in diseases which are a frequent cause of visit to the doctor like ophthalmologic pathologies are, which affect directly to our quality of life, takes more importance. This paper aims to develop OphthalDSS, a totally new mobile DSS for red eye diseases diagnosis. The main utilities that OphthalDSS offers will be a study guide for medical students and a clinical decision support system for primary care professionals. Other important goal of this paper is to show the user experience results after OphthalDSS being used by medical students of the University of Valladolid. For achieving the main purpose of this research work, a decision algorithm will be developed and implemented by an Android mobile application. Moreover, the Quality of Experience (QoE) has been evaluated by the students through the questions of a short inquiry. The app developed which implements the algorithm OphthalDSS is capable of diagnose more than 30 eye's anterior segment diseases. A total of 67 medical students have evaluated the QoE. The students find the diseases' information presented very valuable, the appearance is adequate, it is always available and they have ever found what they were looking for. Furthermore, the students think that their quality of life has not been improved using the app and they can do the same without using the OphthalDSS app. OphthalDSS is easy to use, which is capable of diagnose more than 30 ocular diseases in addition to be used as a DSS tool as an educational tool at the same time.

A web-based microbiological hazard identification tool for infant foods.

An integrated approach to identify and assess Microbiological Hazards (MHs) and mitigate risks in infant food chains is crucial to ensure safe foods for infants and young children. A systematic procedure was developed to identify MHs in specific infant foods. This includes five major steps: 1) relevant hazard-food pairing, 2) process inactivation efficiency, 3) recontamination possibility after processing, 4) MHs growth opportunity, and 5) MHs-food association level. These steps were integrated into an online tool called the Microbiological Hazards IDentification (MiID) decision support system (DSS), targeting food companies, governmental agencies and academia users, and is accessible at https://foodmicrobiologywur.shinyapps.io/Microbial_hazards_ID/. The MiID DSS was validated in four case studies, focussing on infant formula, fruit puree, cereal-based meals, and fresh fruits, each representing distinct products and processing characteristics. The results obtained through the application of the MiID DSS, compared with identification by food safety experts, consistently identified the top MHs in these food products. This process affirms its effectiveness in systematic hazard identification. The introduction of the MiID DSS helps to structure the first steps in HACCP (hazard analysis) and in risk assessment (hazard identification) to follow a structured and well-documented procedure, balancing the risk of overlooking relevant MHs or including too many irrelevant MHs. It is a valuable addition to risk analysis/assessment in infant food chains and has the potential for future extension. This includes the incorporation of newly acquired data related to infant foods via a semi-publicly hosted platform, or it can be adapted for hazard identification in general food products using a similar framework.

Systematic risk ranking of microbiological hazards in infant foods.

Ensuring food safety, particularly for vulnerable groups, like infants and young children, requires identifying and prioritizing potential hazards in food chains. We previously developed a web-based decision support system (DSS) to identify specific microbiological hazards (MHs) in infant and toddler foods through a structured five-step process. This study takes the framework further by introducing systematic risk ranking (RR) steps to rank MH risks with seven criteria: process survival, recontamination, growth opportunity, meal preparation, hazard-food association evidence, food consumption habits of infants and toddlers in the EU, and MH severity. Each criterion is given a semi-quantitative or quantitative score or risk value, contributing to the final MH risk calculation via three aggregation methods: semi-quantitative risk scoring, semi-quantitative risk value, and outranking multi-criteria decision analysis (MCDA). To validate the criteria and ranking approaches, we conducted a case study to rank MH risks in infant formula, compared the results of the three risk ranking methods, and additionally evaluated the ranking results against expert opinions to ensure their accuracy. The results showed strong agreement among the three methods, consistently ranking Salmonella non-Typhi and Cronobacter spp. and Shiga-toxin-producing Escherichia coli as the top MH risks in infant formulae, with minor deviations. When MHs were ranked after an initial hazard identification step, all three methods produced nearly identical MH rankings, reinforcing the reliability of the ranking steps and the selected criteria. Notably, the risk value and MCDA methods provided more informative MH rankings compared to the risk scoring method. The risk value and risk scoring methods were implemented into an online tool, called the MIcrobiological hazards risk RAnking decision support system (Mira-DSS), available at https://foodmicrobiologywur.shinyapps.io/MIcrobial_hazards_RAnking/. In conclusion, our framework enables the ranking of MH risks, facilitating intervention comparisons and resource allocations to mitigate MH risks in infant foods, with potential applicability to broader food categories.

Evaluation of deep learning-based depression detection using medical claims data.

Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adoption of AI-based digital decision support (DDSS) is rare. One reason is that AI algorithms are often not evaluated based on large, real-world data. This research shows the potential of using deep learning on the medical claims data of 812,853 people between 2018 and 2022, with 26,973,943 ICD-10-coded diseases, to predict depression (F32 and F33 ICD-10 codes). The dataset used represents almost the entire adult population of Estonia. Based on these data, to show the critical importance of the underlying temporal properties of the data for the detection of depression, we evaluate the performance of non-sequential models (LR, FNN), sequential models (LSTM, CNN-LSTM) and the sequential model with a decay factor (GRU-Δt, GRU-decay). Furthermore, since explainability is necessary for the medical domain, we combine a self-attention model with the GRU decay and evaluate its performance. We named this combination Att-GRU-decay. After extensive empirical experimentation, our model (Att-GRU-decay), with an AUC score of 0.990, an AUPRC score of 0.974, a specificity of 0.999 and a sensitivity of 0.944, proved to be the most accurate. The results of our novel Att-GRU-decay model outperform the current state of the art, demonstrating the potential usefulness of deep learning algorithms for DDSS development. We further expand this by describing a possible application scenario of the proposed algorithm for depression screening in a general practitioner (GP) setting-not only to decrease healthcare costs, but also to improve the quality of care and ultimately decrease people's suffering.

Developing a disability determination model using a decision support system in Taiwan: a pilot study.

BACKGROUND/PURPOSE: The aims of our study were to: (1) develop the Disability Grading Decision Support System (DGDSS) and to (2) compare the new International Classification of Functioning, Disability, and Health (ICF)-based disability determination tool (ICF-DDT) with the diagnosis-based disability determination tool (D-DDT). METHODS: A total of 9357 patients recruited from 236 accredited institutions were evaluated using the ICF-DDT and the D-DDT, and the presence, severity, and category of the disability identified using the two determination tools were compared. In the DGDSS, the ICF-DDT consisted of four models comprising nine modules to determine the presence and the severity of the disability. The differences between models (modules) are the different combinations of World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) and Scale of Body Functions and Structures. RESULTS: Compared with the D-DDT, more patients were determined to be disability-free when using the ICF-DDT. Module 1-1 had the highest profoundly severe rate, and module 2-2 had the highest mild and moderate disability rates. Module 2-1 had the highest severe disability rate. Module 1-1 resulted in the smallest difference, and module 3-1 resulted in the largest difference, compared with the D-DDT. Feedback from users suggested that the DGDSS is a robust system if the original data are accurate. CONCLUSION: The presence, severity, and category of the disability determined using the ICF-DDT and the D-DDT were significantly different. The results of the DGDSS provide information for policymakers to determine the optimal allocation of social welfare and medical resources for people with disabilities.

Robust design of a logistics system using FePIA procedure and analysis of trade-offs between CO2 emissions and net present value.

The problems of flexible planning of the design of logistics systems for the collection of food products such as raw milk can result in a decrease in the performance of critical indicators for their performance. This paper proposes a new efficient methodology for robustly designing a first-mile logistics system for storing and refrigerating milk as a perishable product considering decisions related to open facilities and the flow of products, including sustainability indices. The proposed approach is modeled as a bi-objective problem by considering the minimization of greenhouse gas emissions (CO2) produced by milk transportation canteens and the maximization of the system configuration's net present value (NPV). We have analyzed and determined the most robust configuration for the first time and explained the robustness-NPV and robustness-CO2 relationships. The proposed mathematical model is solved by the Epsilon constraints method, and the robustness is calculated considering an extension of the FePIA methodology for multiobjective problems. A novel contribution is a balance in the possible future values generated by the company related to its cash flows and the generation of CO2 emissions when using a motorized transport frequently used in the shipment of raw milk considering a new important aspect such as the volume of product transported and the slope of the path between the production farm and the storage cooling tanks.

Multi-Criteria Performance Analysis Based on Physics of Decision - Application to COVID-19 and Future Pandemics.

The purpose of this study is to present a novel perspective on decision support based on the conventional SEIR pandemic model paradigm considering the risks and opportunities as physical forces deviating the expected performance trajectory of a system. The impact of a pandemic is measured by the deviation of the social system's performance trajectory within the geometrical framework of its Key Performance Indicators (KPIs). According to the overall premise of utilizing Ordinary Differential Equations to simulate epidemics, the deviations are connected to several alternative interventions. The model is essentially built on two sets of parameters: (i) social system parameters and (ii) pandemic parameters. The ultimate objective is to propose a multi-criteria performance framework to control pandemics that includes a combination of timely measures. On the one hand, the current study optimizes prospective strategies to manage the potential future pandemic, while on the other hand, it explores the COVID-19 epidemic in the state of Georgia (USA).

Intelligent Clinical Decision Support System for Managing COPD Patients.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients' future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work's CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients.

Machine learning-based improvement of an online rheumatology referral and triage system.

Introduction: Rheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy. Materials and methods: Data from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP). Results: A complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification. Conclusion: In summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage.

Machine learning-assisted prediction of pneumonia based on non-invasive measures.

Background: Pneumonia is an infection of the lungs that is characterized by high morbidity and mortality. The use of machine learning systems to detect respiratory diseases via non-invasive measures such as physical and laboratory parameters is gaining momentum and has been proposed to decrease diagnostic uncertainty associated with bacterial pneumonia. Herein, this study conducted several experiments using eight machine learning models to predict pneumonia based on biomarkers, laboratory parameters, and physical features. Methods: We perform machine-learning analysis on 535 different patients, each with 45 features. Data normalization to rescale all real-valued features was performed. Since it is a binary problem, we categorized each patient into one class at a time. We designed three experiments to evaluate the models: (1) feature selection techniques to select appropriate features for the models, (2) experiments on the imbalanced original dataset, and (3) experiments on the SMOTE data. We then compared eight machine learning models to evaluate their effectiveness in predicting pneumonia. Results: Biomarkers such as C-reactive protein and procalcitonin demonstrated the most significant discriminating power. Ensemble machine learning models such as RF (accuracy = 92.0%, precision = 91.3%, recall = 96.0%, f1-Score = 93.6%) and XGBoost (accuracy = 90.8%, precision = 92.6%, recall = 92.3%, f1-score = 92.4%) achieved the highest performance accuracy on the original dataset with AUCs of 0.96 and 0.97, respectively. On the SMOTE dataset, RF and XGBoost achieved the highest prediction results with f1-scores of 92.0 and 91.2%, respectively. Also, AUC of 0.97 was achieved for both RF and XGBoost models. Conclusions: Our models showed that in the diagnosis of pneumonia, individual clinical history, laboratory indicators, and symptoms do not have adequate discriminatory power. We can also conclude that the ensemble ML models performed better in this study.

Circular bio-economy via energy transition supported by Fuzzy Cognitive Map modeling towards sustainable low-carbon environment.

Several energy transition plans attempt to establish low-carbon practices towards a circular bio-economy in order to reduce greenhouse gas emissions. However, most actions only try to assuage the impacts of climate change without improving the resource flows generated by human activities. In this paper, we propose a semi-quantitative assessment of the impacts of biowaste-based energy transition by engaging all relevant social stakeholders' evaluation in the strategic plan. This holistic approach models a Decision Support System (DSS) to effectively evaluate the interplay of local and sectoral low-carbon actions. Regional energy alliances and stakeholders are used for participatory modeling to promote the buildup of the learning base of this DSS. The core pillar of the DSS involves the application of advanced features of soft computing for the development of a Fuzzy Cognitive Map (FCM) that elicits the inter-causalities of the critical factors affecting the energy transitions towards bio-economy options. The concepts participating in the map are established by experts, and their interrelations via a learning process that utilizes survey statistics. The strands of research include scenarios to highlight the effect of energy provision to urbanization and the increase of urban actors (social, technological, political) in influencing the decision making related to low-carbon policies. Particularly, we study a use case of a Greek region that, despite its munificent agricultural production, also disclosures a stimulated manufacturing economy sector. The proposed decision making tool uses analytics and optimization algorithms to guide competent authorities and decision makers to sustainable energy transitioning towards decarbonization.

Clinical Decision Support Systems in Breast Cancer: A Systematic Review.

Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

Decision support systems (DSS) for wastewater treatment plants - A review of the state of the art.

The use of decision support systems (DSS) allows integrating all the issues related with sustainable development in view of providing a useful support to solve multi-scenario problems. In this work an extensive review on the DSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide an updated compendium on DSSs in view of supporting researchers and engineers on the selection of the most suitable method to address their management/operation/design problems. Results showed that DSSs were mostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective in order to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based on quality and operational issues are very often applied to site-specific conditions. Finally, it would be important to encourage the development of more user-friendly DSSs to increase general interest and usability.

Protocol: Improving Access to Specialist Nephrology Care Among Rural/Remote Dwellers of Alberta: The Role of Electronic Consultation in Improving Care for Patients With Chronic Kidney Disease.

BACKGROUND: As the burden of chronic kidney disease (CKD) continues to increase, many geographically dispersed Canadians have limited access to specialist nephrology care, which tends to be centralized in major urban areas. As a result, many rural/remote-dwellers in Canada experience poor quality of care and related adverse outcomes. It is imperative to develop alternative care delivery mechanisms to ensure optimal health outcomes for all Canadians. OBJECTIVE: To investigate the feasibility and effectiveness of electronic consultation (eConsult) as a new model for interactions between specialists and primary care providers (PCPs) to improve access to care for patients with CKD. DESIGN: This is a sequential, mixed methods study that will be conducted in 3 phases. SETTING: The study will be conducted across the entire province of Alberta, supported by Alberta Kidney Care (formerly, Northern and Southern Alberta Renal Programs [NARP/SARP]). PATIENTS: Patients suffering from CKD will be included in the study. MEASUREMENTS: We will assess the barriers and enablers of implementation and adoption of an e-consultation protocol to facilitate access to care for patients with CKD in Alberta with a focus on rural/remote-dwellers with CKD. We will also evaluate the impact of the eConsult system (eg, improved access to specialist care, reduction in care gaps), assess the feasibility of province-wide implementation, and compare eConsult with practice facilitation versus eConsult alone in terms of access to specialist care, quality of care, and related outcomes. METHODS: The study will be conducted in 3 phases. In phase 1, we will assess the perceptions of stakeholders (ie, PCPs, nephrologists, patients, policymakers, and other care providers) to improve CKD care delivery, quality, and outcomes in Alberta with focus groups and semistructured interviews. Phase 2 will engage specific family physicians for their input on key factors and logistical issues affecting the feasibility of implementing eConsult for the care of patients with CKD. Phase 3 will provide academic detailing including practice facilitation to clinics in Alberta to assess how eConsult with practice facilitation compares with eConsult alone in terms of access to specialist care, quality of care, and related outcomes. RESULTS: We will assess stakeholder perceptions about potential barriers to and enablers of a new eConsult and decision support system strategy, focusing on elements that are most important for the design of a feasible and implementable intervention. We will develop, pilot test, and assess the impact of the eConsult model in improving access to specialist nephrology care and the feasibility of province-wide implementation. The final phase of the project will address key challenges for optimal care for patients with CKD living in rural, remote, and underserved areas of Alberta, particularly timely referral and disease management as well as the cost-effective benefits of eConsult. LIMITATIONS: Lack of high-speed Internet in many rural and remote areas of Alberta may lead to more time spent in completing the eConsult request online versus faxing a referral the traditional way. Allied health care staff (referral coordinators, administrative staff) require training to the eConsult system, and physicians at many remote sites do not have adequate staff to handle eConsult as an added task. CONCLUSIONS: Implementation of eConsult can favorably influence referral patterns, access to care, care quality, patient outcomes, and health care costs for people with CKD. Results of this study will inform the optimization of care for rural/remote-dwellers with CKD and will facilitate future partnerships with policymakers and provincial renal programs in Alberta to ensure optimal kidney health for all residents. TRIAL REGISTRATION: Not required.

CONTEXTE: Bien que le fardeau de l'insuffisance rénale chronique (IRC) ne cesse de s'alourdir, de nombreux Canadiens dispersés sur le plan géographique continuent d'avoir un accès limité à des soins spécialisés puisque ceux-ci sont davantage concentrés dans les grandes zones urbaines. Ainsi, dans les régions rurales/éloignées du Canada, de nombreux patients atteints d'IRC ne reçoivent pas les soins appropriés et subissent les conséquences néfastes des pathologies associées à leur état de santé. Il est donc essentiel de développer des mécanismes alternatifs de prestation de soins pour s'assurer que tous les Canadiens ont accès à des soins optimaux. OBJECTIF: Évaluer la faisabilité et l'efficacité d'un système de consultation électronique (eConsult) facilitant les interactions entre les spécialistes et les fournisseurs de soins primaires (FSP) dans le but d'améliorer l'accès des patients atteints d'IRC à des soins spécialisés. TYPE D'ÉTUDE: Une étude séquentielle en trois phases reposant sur des méthodes mixtes. CADRE: L'étude sera menée à la grandeur de l'Alberta avec le soutien financier du Alberta Kidney Care (anciennement Northern/Southern Alberta Renal Program [NARP/SARP]). SUJETS: Des patients atteints d'IRC participeront à l'étude. MESURES: Nous étudierons les facteurs qui entravent ou qui facilitent la mise en œuvre et l'adoption d'un système d'eConsult visant à améliorer l'accès des Albertains atteints d'IRT à des soins spécialisés, particulièrement ceux qui résident en région rurale/éloignée. Nous évaluerons les impacts de l'eConsult (meilleur accès à des soins spécialisés, réduction des disparités) et la faisabilité de son implantation à l'échelle de la province. Enfin, nous comparerons l'accès aux soins spécialisés, leur qualité et l'incidence des pathologies associées selon que l'eConsult est employée seule ou avec facilitation de la pratique. MÉTHODOLOGIE: L'étude se déroulera en trois étapes. Par l'entremise de groupes de discussion et d'interviews semi-structurées, la phase 1 évaluera la perception des différents intervenants (FSP, néphrologues, patients, décideurs et autres fournisseurs de soins) quant à l'amélioration de la prestation et de la qualité des soins spécialisés, et des résultats de santé. La phase 2 sondera l'avis de médecins de famille sélectionnés sur les problèmes logistiques et les principaux facteurs affectant l'implantation du système d'eConsult. La phase 3 fournira de la formation continue aux cliniques albertaines, notamment en facilitation de la pratique, et comparera les effets de l'eConsult avec facilitation de la pratique et de l'eConsult employée seule sur l'accès aux soins spécialisés, la qualité des soins et les pathologies associées. RÉSULTATS: Nous questionnerons les différents intervenants sur les éventuels obstacles et facilitateurs d'une nouvelle stratégie d'eConsult et d'aide à la prise de décision, en nous concentrant sur les éléments les plus importants pour la conception d'une intervention réalisable et applicable. Nous élaborerons le modèle d'eConsult, le mettrons à l'essai et évaluerons son incidence sur l'amélioration de l'accès aux soins spécialisés en néphrologie et sur la faisabilité de difficultés de prodiguer des soins optimaux aux patients atteints d'IRC des zones rurales, éloignées et mal desservies de l'Alberta, particulièrement en ce qui concerne l'aiguillage rapide et la gestion de la maladie. Les avantages économiques de l'eConsult seront également abordés au cours de cette phase. LIMITES: L'absence d'internet haute vitesse dans de nombreuses régions rurales et éloignées de l'Alberta pourrait faire en sorte qu'il soit plus rapide d'envoyer la demande par télécopieur que de la remplir en ligne. Le personnel paramédical (coordonnateurs de l'aiguillage, personnel administratif) doit être formé sur le système eConsult et plusieurs centers éloignés n'ont pas suffisamment de personnel pour s'en occuper comme tâche supplémentaire. CONCLUSION: L'implantation d'un système d'eConsult en contexte d'IRC pourrait améliorer les schémas d'aiguillage, l'accès aux soins spécialisés, leur qualité et les résultats des patients, de même que réduire les coûts de santé. Les résultats de cette étude guideront l'optimisation des soins en IRC dans les régions rurales/éloignées et faciliteront les futurs partenariats avec les décideurs et les programs provinciaux de néphrologie en Alberta, afin d'assurer une santé rénale optimale à tous les résidents.

Response planning for accidental oil spills in Persian Gulf: A decision support system (DSS) based on consequence modeling.

Different causes lead to accidental oil spills from fixed and mobile sources in the marine environment. Therefore, it is essential to have a systematic plan for mitigating oil spill consequences. In this research, a general DSS is proposed for passive and active response planning in Persian Gulf, before and after a spill. The DSS is based on NOAA's advanced oil spill model (GNOME), which is now linked with credible met-ocean datasets of CMEMS and ECMWF. The developed open-source tool converts the results of the Lagrangian oil spill model to quantitative parameters such as mean concentration and time of impact of oil. Using them, two new parameters, emergency response priority number (ERPN) and risk index (RI), are defined and used for response planning. The tool was tested in both deterministic and probabilistic modes, and found to be useful for evaluation of emergency response drills and risk-based prioritization of coastal areas.