Implementation Of Decision Support System Research Articles

Comparative evaluation of different medication safety measures for the emergency department: physicians’ usage and acceptance of training, poster, checklist and computerized decision support

BackgroundAlthough usage and acceptance are important factors for a successful implementation of clinical decision support systems for medication, most studies only concentrate on their design and outcome. Our objective was to comparatively investigate a set of traditional medication safety measures such as medication safety training for physicians, paper-based posters and checklists concerning potential medication problems versus the additional benefit of a computer-assisted medication check. We concentrated on usage, acceptance and suitability of such interventions in a busy emergency department (ED) of a 749 bed acute tertiary care hospital.MethodsA retrospective, qualitative evaluation study was conducted using a field observation and a questionnaire-based survey. Six physicians were observed while treating 20 patient cases; the questionnaire, based on the Technology Acceptance Model 2 (TAM2), has been answered by nine ED physicians.ResultsDuring field observations, we did not observe direct use of any of the implemented interventions for medication safety (paper-based and electronic). Questionnaire results indicated that the electronic medication safety check was the most frequently used intervention, followed by checklist and posters. However, despite their positive attitude, physicians most often stated that they use the interventions in only up to ten percent for subjectively “critical” orders. Main reasons behind the low usage were deficits in ease-of-use and fit to the workflow. The intention to use the interventions was rather high after overcoming these barriers.ConclusionsMethodologically, the study contributes to Technology Acceptance Model (TAM) research in an ED setting and confirms TAM2 as a helpful diagnostic tool in identifying barriers for a successful implementation of medication safety interventions. In our case, identified barriers explaining the low utilization of the implemented medication safety interventions - despite their positive reception - include deficits in accessibility, briefing for the physicians about the interventions, ease-of-use and compatibility to the working environment.

Considerations for a Successful Clinical Decision Support System

Clinical decision support systems have the potential to improve patient care in a multitude of ways. Clinical decision support systems can aid in the reduction of medical errors and reduction in adverse drug events, ensure comprehensive treatment of patient illnesses and conditions, encourage the adherence to guidelines, shorten patient length of stay, and decrease expenses over time. A clinical decision support system is one of the key components for reaching compliance for Meaningful Use. In this article, the advantages, potential drawbacks, and clinical decision support system adoption barriers are discussed, followed by an in-depth review of the characteristics that make a clinical decision support system successful. The legal and ethical issues that come with the implementation of a clinical decision support system within an organization and the future expectations of clinical decision support system are reviewed.

Automation of information decision support to improve e-learning resources quality

Purpose. In conditions of active development of e-learning the high quality of e-learning resources is very important. Providing the high quality of e-learning resources in situation with mass higher education and rapid obsolescence of information requires the automation of information decision support for improving the quality of e-learning resources by development of decision support system. Methodology. The problem is solved by methods of artificial intelligence. The knowledge base of information structure of decision support system that is based on frame model of knowledge representation and inference production rules are developed. Findings. According to the results of the analysis of life cycle processes and requirements to the e-learning resources quality the information model of the structure of the knowledge base of the decision support system, the inference rules for the automatically generating of recommendations and the software implementation are developed. Practical value. It is established that the basic requirements for quality are performance, validity, reliability and manufacturability. It is shown that the using of a software implementation of decision support system for researched courses gives a growth of the quality according to the complex quality criteria. The information structure of a knowledge base system to support decision-making and rules of inference can be used by methodologists and content developers of learning systems.

Decision Support Systems for Water Resources Management in Developing Countries: Learning from Experiences in Africa

Decision support system (DSS) tools are rather popular in the literature on water resources management. The European Project “Splash” conducted a survey of the literature and of DSS implementation in developing countries with specific reference on Africa. Experts in the field were consulted through an ad hoc questionnaire and interviews. The results of the survey indicate that the exchange of experiences amongst projects with similar objectives or even the same case study is very limited, with a tendency towards restarting every time from scratch. As a consequence, it seems that DSS developments have produced only limited positive impacts. Most experts contacted shared either the frustration deriving from the limited impacts on intended end-users, who rarely used the tool after the project end, or in the case of ongoing projects, the preoccupation for future maintenance. Responses from the questionnaires indicate that priority efforts should not focus on developing the tools, but rather on improving the effectiveness and applicability of integrated water resource management legislative and planning frameworks, training and capacity building, networking and cooperation, harmonization of transnational data infrastructures and, very importantly, learning from past experiences and adopting enhanced protocols for DSS development.

Decision Support Systemof Herb's Production Schedulling Based on Good Traditional Medicine Manufacturing Practices (GTMMP) Standard

The purpose of this research is to develop a decision support system in the herbs production scheduling appropriates to Good Traditional Medicine Manufacturing Practices (GTMMP).Design of algorithm modelfor scheduling decision support system that complies with GTMMP standard was done using a network analysis technique, which combines the techniques Evaluation and Review Technique Program (PERT) and Critical Path Method (CPM). The structure of decision support systems consists of database management syatem and modelbase management system. The implementation of decision support systems is the consideration for companies that intend to certify GTMMP.

Impact of four training conditions on physician use of a web-based clinical decision support system

Training has been identified as an important barrier to implementation of clinical decision support systems (CDSSs), but little is known about the effectiveness of different training approaches. Using an observational retrospective cohort design, we examined the impact of four training conditions on physician use of a CDSS: (1) computer lab training with individualized follow-up (CL-FU) (n=40), (2) computer lab training without follow-up (CL) (n=177), (3) lecture demonstration (LD) (n=16), or (4) no training (NT) (n=134). Odds ratios of any use and ongoing use under training conditions were compared to no training over a 2-year follow-up period. CL-FU was associated with the highest percent of active users and odds for any use (90.0%, odds ratio (OR)=10.2, 95% confidence interval (CI): 3.2-32.9) and ongoing use (60.0%, OR=6.1 95% CI: 2.6-13.7), followed by CL (any use=81.4%, OR=5.3, CI: 2.9-9.6; ongoing use=28.8%, OR=1.7, 95% CI: 1.0-3.0). LD was not superior to no training (any use=47%, ongoing use=22.4%). Training format may have differential effects on initial and long-term follow-up of CDSSs use by physicians.

IMPACT OF REFLEXIVE ONTOLOGIES IN SEMANTIC CLINICAL DECISION SUPPORT SYSTEMS

Ontology processing is arguably a time-consuming process with high associated computational costs. Query actions constitute a crucial part of the reasoning process and are a primary source of time consumption. Reflexive ontologies (ROs) is a novel approach intended to reduce time consumption problems while providing a fast reaction from ontology-based applications. In this article we present the implementation of a knowledge-based clinical decision support system (CDSS) for the diagnosis of Alzheimer's disease, which was the benchmark used to evaluate the impact of RO in the overall performance of the system. The implementation details and the definition of the implementation methodology are exposed in this article, along with the results of the evaluation. Some novel techniques that aim to optimize the performance of ROs are also presented with highlights of the test application introduced in our previous work.

Implementation of a Clinical Decision Support System

Implementation of a Clinical Decision Support System

Design and Implementation of City Fire Rescue Decision Support System

Abstract The authors introduced major geographic data management platform ARCGIS, and developed city major fire extinguishing rescue decision support system based on GIS, by using ArcGIS Engine and VB, according to a large number of decision support system and fire fighting rescue strength model advanced. City major fire extinguishing rescue decision support system based on GIS is integrated with several functions, including layer management, query, fire analysis, fire force calculation, and dynamic visualization plan of fire fighting and rescue. This paper mainly introduced the system function, system design and system implementation.

Implementation of a Computer-Based Decision Support System for Outcomes Prediction and Clinical Triage: Initial Results of Two Pilot Studies

Objective: To develop a custom-built, computer-based clinical decision-support tool to help determine the underlying cause of the patient’s clinical presentation, its severity level and predicted outcome. Materials and Methods: A novel, customized, computer-based decision-support tool, rooted in the Bayesian Network technology was constructed. Based on documented data from patients’ medical records, the probabilistic predictive model was utilized to validate patients’ triage in two different clinical scenarios: 1. Prediction of neurologic deficits in patients resuscitated from cardiac arrest following an Acute Coronary Syndrome/ST-Elevation Myocardial Infarction (ACS/STEMI), and thus likelihood of benefit from (or futility of) emergent coronary revascularization. Study group consisted of 99 patients resuscitated from NSTEMI. For this particular study, the endpoints were in-hospital death, neurologic recovery and the accurate prediction of outcomes by the program. 2. Prediction of the likelihood of presence of and readmission for Acute Decompensated Heart Failure syndrome. Study groups consisted of 20 randomly generated patient clinical profiles (Phase I) and 100 cases from our emergency room electronic medical records: 55 cases of acute heart failure, 14 cases of pneumonia, 7 cases of COPD exacerbation and 11 cases of “other” conditions. End points were the accurate prediction of the underlying diagnosis and the correct disposition. In both studies, observed data from our institutional electronic medical records registry was utilized to populate the computer program and to validate its initial results. Subsequently, multiple, random clinical case scenarios were used to validate the predictive properties of the computer tool (Phase I) Finally (Phase II), the program was tested using real-world data from our institutional emergency room electronic medical records. Results: First study: Overall, 64 (65%) patients survived, while 35 (35%) died. On initial examination, 25% of patients were alert, 14% were minimally responsive and 60% were unresponsive. First Group (5 Hypothetical Case scenarios): Diagnostic accuracy rate 4/5 (80%) Study Cohort (99 cases) 95/99% (95%); Overall Accuracy: 87% Second study: Phase I: Diagnostic accuracy rate of 85%; Disposition accuracy rate of 80%; Overall Accuracy rate 82% Phase II: Diagnostic accuracy rate of 98%; Disposition accuracy rate of 91%; Overall accuracy rate of 94% Conclusions: A custom-built, computer based predictive model, utilizing a probability engine software and based on population-wide real-life clinical data, can be a useful adjunct in clinical decision making and medical triage. This takes special significance in such high-risk, fast-paced environments such as the emergency department and the intensive care unit. Keywords: Predictive modeling, Bioinformatics, Clinical Risk Assessment, Clinical Decision-making. Read more →

Predictions for oil slicks detected from satellite images using MyOcean forecasting data

Abstract. The pan-European capacity for the Ocean Monitoring and Forecasting (MyOcean) Marine Core Service, implementing the Global Monitoring for Environment and Security (GMES) objectives, targets the provision of ocean state observations from various platforms and analysis and forecasting products to assist, among other downscaling activities, the needs of the operational response to marine safety, particularly concerning oil spills. The MEDSLIK oil spill and trajectory prediction system makes use of the MyOcean regional and Cyprus Coastal Ocean Forecasting and Observing System (CYCOFOS) downscaled forecasting products for operational application in the Mediterranean and pre-operational use in the Black Sea. Advanced Synthetic Aperture Radar (ASAR) satellite remote sensing images from the European Space Agency (ESA) and European Maritime Safety Agency CleanSeaNet (EMSA-CSN) provide the means for routine monitoring of the southern European seas for the detection of illegal oil discharges. MEDSLIK offers various ways, to be described in this paper, of coupling the MyOcean forecasting data with ASAR images to provide both forecasts and hindcasts for such remotely observed oil slicks. The main concern will be the drift of the oil slick and also, in the case of the forecast mode, its diffusive spreading, although some attempt is also made to estimate the changes in the state of the oil. The successful link of the satellite-detected oil slicks with their operational predictions using the MyOcean products contributes to the operational response chain and the strengthening of maritime safety for accidental or illegal spills, in implementation of the Mediterranean Decision Support System for Marine Safety (MEDESS-4MS) regarding oil spills.

Developmental Issues of Web-based Decision Support System

Decision Support Systems (DSS) are computer programs that aid users in a problem solving or decision-making environment. DSS contains decision making information and other related data that may be of interest to unscrupulous competitors and stakeholders. Therefore, a DSS that is developed for web-based environment has to be secured from unauthorized access or malicious attacks. This study describes a framework of a secured DSS suitable to support decision making activities. Security issues are discussed from various perspectives with the proposed mechanisms to ensure a secured DSS implementation in distributed environment. Issues in web-based application development that include data growth, system performance and appearance compatibility are also presented together with their proposed solutions. Considering security and development issues could contribute towards successful implementation in the decision making process.

Modelling dynamics in decision support systems

Decision support systems (DSS) play an important role in supporting scheduling tasks in industrial settings. In this article, a special aspect of DSS for scheduling, namely their development, implementation and usage in relation to user participation, is examined. The outlined research model proposes that different forms of user participation lead to different levels of model complexity. This complexity influences in turn such outcome variables like system performance and user satisfaction. The research model integrates several existing concepts from technology acceptance, planned behaviour, involvement and participation research as well as control theory. These theories are expanded by the dynamics of the modelling process. For the empirical analysis, multiple case studies examining different cases of DSS development and implementation processes in different settings were conducted.

Prediction of quality parameters for biomass silage: A CFD approach

The increasing use of silage has resulted in continuous efforts to minimize the quality losses. As a consequence, there is need to develop systems to predict and evaluate quality parameters. The objective of the present study was the development of a computational fluid dynamics (CFDs) model for the prediction of air temperature and oxygen concentration temporal and spatial variations in silage storages. Two experimental semi-cylinder silo stacks were used for the validation of the model. For monitoring temperature and oxygen concentration, a network consisting of 18 wireless sensors was placed in each stack. In order to validate the CFD model for both cases of sufficient and insufficient covering one of the stacks was penetrated to emulate the influx of outside air. A good agreement was found between measured and predicted obtained results. Measured and predicted values for air temperature varied between 3% and 11% and for oxygen concentration between 5% and 14% with correlation coefficients between 0.76 and 0.81, and between 0.91 and 0.97, respectively. The results show the potential for the development and implementation of decision support systems for the prediction of quality parameters in storaged biomasses. With such systems, an early detection of process disturbances can be obtained and making possible preventive measures.

Implementation of a Clinical Decision Support System for Antimicrobial Stewardship

Clinical decision support systems (CDSSs) for antimicrobial stewardship require considerable human resources and financial investments. This pre-/postimplementation study evaluated the effect of a CDSS on performance of prospective audit with intervention and feedback and demonstrated an increase in interventions and recommendation acceptance countered by a substantial number of nonactionable alerts.

An open source decision support system implementation

Ashraf Farrag

Improving rates of herpes zoster vaccination with a clinical decision support system in a primary care practice

Herpes zoster (shingles) is a localized neurocutaneous eruption of blisters caused by reactivation of the varicella zoster virus. The cost of care for herpes zoster and its complications is estimated at $1.1 billion. The Centers for Disease Control and Prevention's Advisory Committee on Immunization Practices recommends a one-time dose of the vaccine for adults aged 60 years or older. Despite that recommendation, utilization of the vaccine is very low. One way to boost the delivery of preventive services such as vaccinations is with a computerized clinical decision support system. Our study found that the herpes zoster vaccination rate increased significantly after the implementation of such a system. To study utilization of herpes zoster vaccine before and after the implementation of a web-based clinical decision support software solution in a primary care practice. Billing data was utilized to determine number of herpes zoster vaccination administered to patients for a 12-month period during the implementation of the software solution. The utilization of vaccinations improved from 63 to 117 (53.8% increase) for one primary care practice and from 54 to 127 (42.5% increase) in the other primary care practice. Herpes zoster vaccination rate significantly improved with implementation of a web-based clinical decision support system.

Designing workshop management system supporting decision making with OODB

In this study, a workshop production management system supporting decision making is designed for use in small and medium-sized machine workshops. Small and medium sized companies’ quality, price, and delivery time issues are in a tough competitive environment. Especially such as the orders which cannot be delivered to customers in time, disruptions in production schedules and low production performance seem to be the major problems facing in this type of sector. Through this research it is aimed to design software to keep all the needed information in a suitable database and give the customers fast and accurate information. In order to support future implementation of decision support system, the software is designed such that it will enable the user to easily analyze its production parameters such as efficiency, cost deviation of production from planned cost, cost estimation of new production, etc. Due to the natural structure of this project Object Oriented database is chosen for implementation. These databases can be constructed through object-oriented language and in the scope of this work .NET based db4o is used. For software development, Unified Modelling Language (UML) is used since by using UML the database system is well defined, visualized, generated and specified considering complete project. The user interfaces are designed by using ASP.NET for easy access of the user location independently. It is seen that without using so much coding load, an effective object-oriented database can be designed. It is possible to use the same language for both application and object-oriented databases in contrast to relational databases. The designed database can be used in different machine workshops with small modifications depending on the specifications of workshops.

Design and Implementation of an Irrigation Decision Support System for Cotton in Xinjiang, China

Design and Implementation of an Irrigation Decision Support System for Cotton in Xinjiang, China

A multi-agent-based decision support system for bankruptcy contagion effects

With the increasing interdependence of marketing participants, distress experienced by a specific entity may cause other connecting firms to encounter financial difficulties, leading to a negative impact on their stock valuations. At the same time, individual investors have a great need to gain relevant information for portfolio risk management. The monitoring vision cannot be limited to investors’ portfolios but must take into account any potential candidates affected. Based on the ontological knowledge model of inter-firm relationships, the proposed multi-agent decision support system continuously observes real-time news reports and forecasts their potential impact on the corresponding stock price. After identifying relating companies for which significant market reactions can be expected, a wireless push-based message service promptly supplies information. A case study is used to illustrate the multi-agent-based decision support system (MAB-DSS) implementation and its use. The example shows that the MAB-DSS can automate the solution for intricate and dynamic valuation effects among interdependent firms and provide constructive advice for individual investors.