Design Of Decision Support System Research Articles

Design of Decision Support System for Road Freight Transportation Routing Using Multilayer Zero One Goal Programming

The objective of this study was to develop a decision support system (DSS) to select the optimal road freight transportation route. This DSS is a proactive road freight transportation routing system which employs the standard criteria from the road freight transportation route’s physical characteristics for potential evaluation. The descriptions of potential scale were derived by the Delphi method based on accommodation of transportation. Moreover, the DSS can also calculate the relative weights of the decision criteria by Fuzzy Analytic Hierarchy Process (FAHP). Multilayer Zero One Goal Programming (MZOGP) is used as an optimization algorithm to select road freight transportation routes. The operation of this algorithm is to calculate the total deviation from three objectives: cost, time and, the deviation of road freight transportation route’s physical characteristic score which is calculated by comparing the route’s physical characteristic potential score with potential scores from users. The approach was tested on a realistic road freight transportation routing operation of a logistics service provider company. The empirical study showed that this DSS works successfully.

Multi-Criteria Decision Support System for Green Commercial Space Design

Commercial space features essential characteristics of attracting clients and creating profits; thus, the exterior and interior designs of conventional commercial space were often made to look grandiose and overdecorated. Over time, according to commercial attributes, operator preferences, and style of the designer, commercial spaces have constantly undergone renovation into varied styles. However, the physical renovation processhas caused multiple and composite types of environmental pollution, such as waste and noise pollution caused by breaking of walls or partitions, anddecorative paint pollution, as well as the use of high-energy-consuming lighting equipment, air-conditioning systems, and decorative materials. Global pollution has caused climate change, endangering living organismsand human life. Furthermore, no effective method exists to control the problem of high greenhouse gas emissions. Therefore, this study used energy-saving design concerns of a garden-type commercial space to propose an energy-saving evaluation model. The study combined three methodologies, the Delphi method, analytic hierarchy process, and fuzzy logic theory, to construct a multi-criteria decision support system for designing green commercial spaces, and used the green spatial design of a garden café as an example to illustrate the high objectivity and adaptability of the proposed model in practical application. The study also used an international award-winning case to validate that the proposed model had practical value as a reference to support key design decisions.

Designing Decision Support System for Scholarship Prediction Using Adaptive Neuro Fuzzy Inference System Algorithm

The increasing enthusiasm of students applying for scholarships caused the selection committee to experience difficulties in the decision-making process, the large and complex amount of data caused the committee to need a system that could assist in the decision making process. One method in the system that can help in decision making is artificial neural networks. This method is capable of reasoning like the human brain’s brain network to solve a problem into a form of neurons or equations as a basis for determining a solution or prediction. Researchers have done the method of artificial neural network using the Backpropagation method with an accuracy of 0.9090 and MAE 0.0001017 on the epoch to 329. Based on these results the researchers want to make improvements using a new method, namely adaptive neuro fuzzy inference system (ANFIS) and yields an accuracy value of 92.04% and Mean Absolute Error (MAE) of 0.0001001 on the epoch to 318. The test results were obtained using 3000 data through the K-3 Fold Validation test and 0.2 learning rate configuration and 0.2 momentum.

Usability of Reports Generated by a Computerised Dose Prediction Software.

Computerised dose prediction software assist clinicians in undertaking therapeutic drug monitoring by providing individualised dosing recommendations, typically communicated to prescribers in the form of a report. These software are highly sophisticated and accurate in predicting individualised dosage regimens, but if the information contained in the report is not understood by prescribers, the benefits of the software are not achieved. In this study, we set out to assess the perceived usability of a report generated from a dose prediction system. Fifteen prescribers were presented with a mock report and asked a number of questions to elicit their views of the report's content and design. Overall, we found that the mock report was effective in communicating the recommended dose of a drug, but this recommendation was presented alongside information that was not understood or was unlikely to be utilised by prescribers. In particular, the aspects of the report viewed negatively by end-users largely related to a lack of familiarity with the pharmacological terminology used in the report, which hindered understanding and caused confusion. Involving prescribers early on in the process of designing decision support systems is likely to result in systems and outputs that are more useful, usable and accessible to users.

A decision support framework and system for design of sustainable pharmaceutical supply chain network

With the issues of climate change continuously taking center stage, there is thus a pressing need for the pharmaceutical companies to assess the sustainability of their supply chains. However, the highly regulated nature of the industry has prompted various supply chain decisions such as material sourcing, siting of plants, and distribution network configuration to be locked into the registration of the new drug. This could consequently lead to inefficient supply chain operation during the drug commercial stage, yielding high greenhouse gas emissions per unit of drug product. Previously, we have proposed a systematic framework for design of a sustainable commercial-stage pharmaceutical supply chain network that can be driven from the early drug development stages. In this paper, we extend the framework by incorporating more functionalities. We also introduce a novel decision support system, called PharmaSC, which implements the framework. PharmaSC system has been developed by integrating two computer tools—an Excel-based analytic hierarchy process and LLamasoft’s Supply Chain Guru—into one coherent framework for selecting the most suitable suppliers and manufacturers, mapping a supply chain superstructure network, and optimizing the network to identify and resolve trade-offs between economic and environmental factor. To illustrate the system, an industrially motivated case study involving a paclitaxel production is solved.

CDSS-RM: a clinical decision support system reference model

Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the ‘Historical Decision’ bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges.

Machine learning in critical care: state-of-the-art and a sepsis case study

BackgroundLike other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments.ResultsThe increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU.ConclusionWe have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.

Design of a Clinical Decision Support System for Predicting Erectile Dysfunction in Men Using NHIRD Dataset.

Erectile dysfunction (ED) affects millions of men worldwide. Men with ED generally complain failure to attain or maintain an adequate erection during sexual activity. The prevalence of ED is strongly correlated with age, affecting about 40% of men at age 40 and nearly 70% at age 70. A variety of chronic diseases, including diabetes, ischemic heart disease, congestive heart failure, hypertension, depression, chronic renal failure, obstructive sleep apnea, prostate disease, gout, and sleep disorder, were reported to be associated with ED. In this study, data retrieved from a subset of the National Health Insurance Research Database of Taiwan were used for designing the clinical decision support system (CDSS) for predicting ED incidences in men. The positive cases were male patients aged 20-65 who were diagnosed with ED between January 2000 and December 2010 confirmed by at least three outpatient visits or at least one inpatient visit, while the negative cases were randomly selected from the database without a history of ED and were frequency (1:1), age, and index year matched with the ED patients. Data of a total of 2832 ED patients and 2832 non-ED patients, each consisting of 41 features including index age, 10 comorbidities, and 30 other comorbidity-related variables, were retrieved for designing the predictive models. Integrated genetic algorithm and support vector machine was adopted to design the CDSSs with two experiments of independent training and testing (ITT) conducted to verify their effectiveness. In the 1st ITT experiment, data extracted from January 2000 till December 2005 (61.51%, 1742 positive cases and 1742 negative cases) were used for training and validating and the data retrieved from January 2006 till December 2010 were used for testing (38.49%), whereas in the 2nd ITT experiment, data in the training set (77.78%) were extracted from January 2000 till Deceber 2007 and those in the testing set (22.22%) were retrieved afterward. Tenfold cross validation and three different objective functions were adopted for obtaining the optimal models with best predictive performance in the training phase. The testing results show that the CDSSs achieved a predictive performance with accuracy, sensitivity, specificity, g-mean, and area under ROC curve of 74.72%-76.65%, 72.33%-83.76%, 69.54%-77.10%, 0.7468-0.7632, and 0.766-0.817, respectively. In conclusion, the CDSSs designed based on cost-sensitive objective functions as well as salient comorbidity-related features achieve satisfactory predictive performance for predicting ED incidences.

Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data

ABSTRACTSustainable building design requires an interplay between multidisciplinary input and fulfilment of diverse criteria to align into one high-performing whole. BIM has already brought a profound change in that direction, by allowing execution of efficient collaborative workflows. However, design decision-making still relies heavily on rules of thumb and previous experiences, and not on sound evidence. To improve the design process and effectively build towards a sustainable future, we need to rely on the multiplicity of data available from our existing building stock. The objective of this research is, therefore, to transform existing data, discover new knowledge and inform future design decision-making in an evidence-based manner. This article looks specifically into this task by (1) outlining and distinguishing between the diverse building data sources and types, (2) indicating how the data can be analysed, (3) demonstrating how the discovered knowledge can be implemented in a semantic integration layer and (4) how it can be brought back to design professionals through the design aids they use. We, therefore, propose a performance-oriented design decision support system, relying on BIM, data mining and semantic data modelling, thereby allowing customized information retrieval according to a defined goal.

Rancangan Aplikasi Sistem Pendukung Keputusan (SPK) Pengangkatan Karyawan Menggunakan Metode Simple Additive Weighting (SAW) Pada PT. Ultra Prima Plast - Flexible Packaging

PT. Ultra Prima Plast - Flexible Packaging always performs a trial period on every new employee. In appointment of new employee candidate required selection process and proper observation during the trial period to maximize the quality of the prospective employee, so far decision-making in the appointment of employees after the completion of the trial period is still done manually by using the assessment of several criteria. The criteria used from the assessment of employee performance are: absenteeism, quality of work, initiative, discipline, responsibility, communication ability and organizational skills. The purpose of holding research on PT. Ultra Prima Plast is to find out what problems or constraints exist in managing employee data, refining existing processes and getting new employee appointments using Simple Additive Weighting (SAW) method. As for data collection methods, the authors used literature study methods, field studies and interviews. In system development, writer use tool (model) of structured system modeling with design tool that is Diagram Unified Modeling Language (UML). Implementation and program design using PHP programming language and MYSQL database. Therefore the authors lift the title "Design Decision Support System (DSS) Application Employee Appointment Using Simple Additive Weighting Method (SAW) At PT. Ultra Prima Plast - Flexible Packaging”

Energy Use and Weatherization Practices: Applications for Agent-Based Modeling to Support Vulnerable Populations

This work surveyed residents of an economically disadvantaged community on their attitudes toward weatherization and their energy use behaviors. To support urban leaders making decisions to mitigate the effects of large-scale climate change, data-driven simulation models are being developed. To ensure that these models are equitable, the needs of all citizens must be included, especially those most vulnerable to the impacts of climate change. The results of this survey indicate that residents are taking steps to weatherize and conserve energy, but they are hindered by a lack of resources and knowledge of available assistance programs. These results are being applied to agent-based models (ABM) to model emergent community behavior. These models will be used to design decision support systems for city stakeholders.

금강하구역 의사결정지원시스템의 개념 설계: 이해당사자 대화 지원 기능을 중심으로

하구역 관리 이슈를 둘러싼 의사결정 과정은 다양한 관점을 지닌 이해당사자가 관여하기 때문에 복잡하며 필연적으로 모호성 문제가 발생한다. 이해당사자 간 대화가 건설적인 형태로 진행되도록 하여 모호성 문제를 관리하는 것이 필요하다. 이 연구에서는 금강하구역 관리와 관련된 이해당사자간 대화를 촉진시켜 모호성 문제를 극복하는 데 초점을 맞춘 의사결정지원시스템을 설계하였다. 이해당사자들이 직관적으로 활용할 수 있도록, 각자의 관점을 구체화하고 타인의 관점과 비교할 수 있도록, 시스템 사용자들의 수행 결과를 지식 기반으로 활용하여 자신의 판단과 연계시킬 수 있도록, 온라인(개별) 과정과 오프라인(그룹) 과정이 유기적으로 이어지도록 설계하는 데 초점을 맞추었다. 시나리오 평가는 수질, 퇴적, 염분 등 3가지 항목에 대해 금강하구역 전체를 포괄하는 14개 구역, 4개 계절에 대해 얻어진 모의 결과값를 바탕으로 진행되며, 각 항목별 원 수치는 표준화하여 시나리오 비교를 위한 점수를 계산하였다. 의사결정지원의 절차는 평가항목 설정, 시나리오 선정, 가중치 부여, 대안 평가 및 결과 저장, 타 이해당사자의 결과 검토 및 피드백 등 총 5단계로 구성하였다. 이 의사결정지원시스템은 이해당사자 대화를 원활히 하고 상호 이해의 폭을 넓히는데 도움이 될 것이다.

SISTEM PENDUKUNG KEPUTUSAN MENENTUKAN KENAIKAN KELAS DENGAN MENGGUNAKAN METODE TOPSIS PADA SMK YAPIM MABAR

The most important decision-making in schools is class decision making. The old way with the manual system can lead to slow decision-making. This is of course caused by a very basic problem, namely the decision-making is not appropriate. Therefore a decision support system is needed that can help the teacher to determine the class increase. The design of decision support system in this study aims to apply the application to determine the increase of class, To overcome the problem, required a decision support system of computerized class increase which later can produce quality decisions that can process data and student values ​​effectively and efficiently and can measure the ability of students both academically and non academic so that activities that have been done manually can be done using the computer and mistakes in decision-making class increase can be avoided as well as time in determining decision-making class increase much faster and effective. Decision support system is designed using visual basic programming languages ​​2010 and SQL Server 2008. The process in this study using Topsis method.

Perancangan Sistem Pendukung Keputusan Pemilihan Supplier Bahan Baku pada PT. Jayatama Selaras

PT. Jayatama Selaras is a manufacturer of plastic packaging in Indonesia. The problem faced by the company is the delay in the arrival of raw materials and the quality of raw materials that are not in accordance with the specifications provided by the company, so the process of returning or re-ordering needs to be carried out to new suppliers, which increases the time needed to procure raw materials. In addition, there is no integrated system in data storage that is related to the process of procuring raw materials, such as supplier data, and raw material data. The purpose of this study is to design a decision support system in providing an objective selection of alternative suppliers for the Purchasing Department of PT. Jayatama Selaras. The design of a decision support system starts with analyzing the current system using the PIECES framework. Next is creating a database using MySQL based on data management components. The next system design is designing component management models. The model used in the design of decision support systems at PT. Jayatama Selaras is AHP and TOPSIS. The last stage is designing components of dialogue management or user interfaces using the Visual Basic programming language. The results of the decision support system design show that the system can determine the best alternative suppliers, for PVC and LDPE raw materials, for LDPE raw materials to produce alternative sequences of suppliers PT. Indo Thai Trading, PT. Itochu Indonesia, PT. Tempo Nagadi Trading, PT. Kharisma Karya Plast, PT. Akino Indonesia Trading, PT. Panca Budi Pratama and PT. Unipack Plasindo. In addition, a decision support system designed to produce a database of existing suppliers and facilitate the ordering of raw materials to suppliers. The design of this decision support system is addressed to the purchasing department, production department, quality control department, and accounting department.

An approach to automatic process deviation detection in a time-critical clinical process.

Prior research has shown that minor errors and deviations from recommended guidelines in complex medical processes can accumulate to increase the likelihood that a major error will go uncorrected and lead to an adverse outcome. Real-time automatic and accurate detection of process deviations may help medical teams better prevent or mitigate the effect of errors and improve patient outcomes. Our goal was to develop an approach for automatic detection of errors and process deviations in trauma resuscitation. Using video review, we coded activity traces of 95 pediatric trauma resuscitations collected in a Level 1 trauma center over two years (2014-2016). Twenty-four randomly selected activity traces were compared with a knowledge-driven model of trauma resuscitation workflow using a phase-based conformance checking algorithm for detecting true and false deviations (alarms). An analysis of false alarms identified three types of causes: (1) model gaps or discrepancies between the model ("work as imagined") and actual practice ("work as done"), (2) errors in activity traces coding, and (3) algorithm limitations. We repaired the system to remove model gaps, reduce coding errors, and address algorithm limitations. The repaired system was first evaluated with another 20 traces and then applied to the entire dataset of 95 traces. During the training, we detected 573 process deviations in 24 activity traces that include 1099 activities. Among these deviations, only 27% represented true deviations and the remaining 73% were false alarms. This initial deviation detection accuracy was only 66.6%, with a F1-score of 0.42. Detection accuracy of the repaired system increased to 95.2% (0.85 F1-score) during system validation and to 98.5% (0.96 F1-score) during testing. After deploying the repaired deviation detection system to all 95 activity traces, we detected 1060 process deviations in 5659 activities (11.2 deviations per resuscitation). Among the 5659 activities in these traces, 4893 fit the repaired knowledge-driven workflow model, 294 were errors of omission, 538 were errors of commission, and 228 were scheduling errors. Our approach to automatic deviation detection provides a method for identifying repeated, omitted and out-of-sequence activities that can be included in the design of decision support systems for complex medical processes. Our findings show the importance of assessing detected deviations for repairing a knowledge-driven model that best represents "work as done."

Optimal Design of LIDs in Urban Stormwater Systems Using a Harmony-Search Decision Support System

During the last years, climate changes and urbanization are causing huge urban pluvial flood events in many countries in the world, driving to both develop and apply effective and innovative approaches for the design and management of urban stormwater systems. The gradual urbanization is provoking the increase of impervious surfaces and, consequently, of surface runoff and velocity and the reduction of concentration times of watersheds, both increasing soil erosion and worsening the water quality as a consequence of the intensive contamination. In this field, Low Impact Development (LID) practices for urban runoff control can be intended as an effective approach to both improve the urban resilience against the flooding risk and assure environmental interventions to adequate the urban stormwater systems to both climate and land use changes. In this paper, a Decision Support System (DSS) for the optimal design of LIDs in urban watershed is presented and discussed. The procedure is tested on Fuorigrotta (IT) and Ponticelli (IT) urban watersheds, with the aim of assessing the effectiveness of LIDs application in reducing both the flooded and conveyed volumes, at the expense of cost-effective solutions.

A Decision Support System to Enhance Self-Management of Low Back Pain: Protocol for the selfBACK Project.

BackgroundLow back pain (LBP) is a leading cause of disability worldwide. Most patients with LBP encountered in primary care settings have nonspecific LBP, that is, pain with an unknown pathoanatomical cause. Self-management in the form of physical activity and strength and flexibility exercises along with patient education constitute the core components of the management of nonspecific LBP. However, the adherence to a self-management program is challenging for most patients, especially without feedback and reinforcement. Here we outline a protocol for the design and implementation of a decision support system (DSS), selfBACK, to be used by patients themselves to promote self-management of LBP.ObjectiveThe main objective of the selfBACK project is to improve self-management of nonspecific LBP to prevent chronicity, recurrence and pain-related disability. This is achieved by utilizing computer technology to develop personalized self-management plans based on individual patient data.MethodsThe decision support is conveyed to patients via a mobile phone app in the form of advice for self-management. Case-based reasoning (CBR), a technology that utilizes knowledge about previous cases along with data about the current patient case, is used to tailor the advice to the current patient, enabling a patient-centered intervention based on what has and has not been successful in previous patient cases. The data source for the CBR system comprises initial patient data collected by a Web-based questionnaire, weekly patient reports (eg, symptom progression), and a physical activity-detecting wristband. The effectiveness of the selfBACK DSS will be evaluated in a multinational, randomized controlled trial (RCT), targeting care-seeking patients with nonspecific LBP. A process evaluation will be carried out as an integral part of the RCT to document the implementation and patient experiences with selfBACK.ResultsThe selfBACK project was launched in January 2016 and will run until the end of 2020. The final version of the selfBACK DSS will be completed in 2018. The RCT will commence in February 2019 with pain-related disability at 3 months as the primary outcome. The trial results will be reported according to the CONSORT statement and the extended CONSORT-EHEALTH checklist. Exploitation of the results will be ongoing throughout the project period based on a business plan developed by the selfBACK consortium. Tailored digital support has been proposed as a promising approach to improve self-management of chronic disease. However, tailoring self-management advice according to the needs, motivation, symptoms, and progress of individual patients is a challenging task. Here we outline a protocol for the design and implementation of a stand-alone DSS based on the CBR technology with the potential to improve self-management of nonspecific LBP.ConclusionsThe selfBACK project will provide learning regarding the implementation and effectiveness of an app-based DSS for patients with nonspecific LBP.Registered Report IdentifierRR1-10.2196/9379

Sistem Pendukung Keputusan Penerima Beasiswa UMN dengan Profile Matching

This paper describes about the design and development of UMN scholarship decision support system using Profile Matching method. At Universitas Multimedia Nusantara (UMN), there are scholarships for students in order to ease the burden of education costs. There are achievement scholarships, scholarships for students whose parents died, and non-academic scholarships. Decision support systems play an important role in the termination of the final decision, and therefore this application is built by implementing the method of Profile Matching to aid in the selection of the scholarship decisions for students whose parent were died and non-academic scholarship. Profile Matching is a method that aims at taking decisions by assuming that there is an ideal level of predictor variables that must be met by the subjects studied, instead of the minimum rate that must be met or passed. From the results, it can be concluded that this method successfully implemented into applications that can help in decision making process. Result of user satisfaction level for this application is 72%, the result of manual calculation with the application calculation results is not much different so that this application can assist in supporting the UMN scholarship decision.
 Index Terms— decision support system, non-academic scholarship, Profile Matching, scholarships for student whose parents died, UMN

Development of a digital clinical pathway for emergency medicine: Lessons from usability testing and implementation failure.

Clinical pathways are used to support the management of patients in emergency departments. An existing document-based clinical pathway was used as the foundation on which to design and build a digital clinical pathway for acute chest pain, with the aim of improving clinical calculations, clinician decision-making, documentation, and data collection. Established principles of decision support system design were used to build an application within the existing electronic health record, before testing with a multidisciplinary team of doctors using a think-aloud protocol. Technical authoring was successful, however, usability testing revealed that the user experience and the flexibility of workflow within the application were critical barriers to implementation. Emergency medicine and acute care decision support systems face particular challenges to existing models of linear workflow that should be deliberately addressed in digital pathway design. We make key recommendations regarding digital pathway design in emergency medicine.

Brief Discussion on the Design of Decision Support System for Flood Control Operation in Yangtze River

Flood control operation is the top priority for the management of the Yangtze River. To improve the timeliness and reliability of operation decision, it is of great significance to establish a set of intelligent decision system for flood control management, which can serve multi-level users. In order to achieve the goal, the key is to modularize the core business functions through the construction of topology relations of river network and control sites for the Yangtze River. In this paper, it would integrate the functions of hydrological forecast, flood propagation, flood control operation and flood risk analysis for the core business. Based on the core business model and information expansion interface, we develop flood control decision support system of the Yangtze River by virtue of GIS visualization and interactive WebService technology. The system can realize the real-time query of water-rainfall information and the fast-time simulation of flood control scheduling scheme, so as to provide the professional, scientific and accurate technical support for the group decision in the flood control.