Implementation Of Decision Support System Research Articles

RETRACTED ARTICLE: Comparative analysis of spatial interpolation with climatic changes using inverse distance method

An ecological change on the planet the most flighty parameter is precipitation. It causes minor and real changes in the climatic changes. The time and space analyzed on the data and the spatial and temporal patterns are introduced in the required manner. For the unpredictable data to be predicted by using the spatial interpolation techniques. The major spatial interpolation methods are categorized based on the simple and complex mathematical modeling. Those studies involve interpolation techniques such as ordinary Krigging, inverse distance weighted (IDW), Spline, etc., Most of the techniques studied the hydrological modeling and deliver hydro mapping (Younghun Jung and Venkatesh Merwade 2015). Various techniques are used for the hydrological mapping. This study involved comparing the statistical interpolation with precipitation and temperature. The cumulative values in the dataset taken from the year 2001 to the year 2013 used in the study. The monthly and the yearly mean square value calculated using the spatial interpolation techniques (Dhamodarn and Shruthi 2016). The main objective of the study is to find the topographical features and provides high-resolution climate maps using mathematical modeling. In the result of comparing the above interpolation methods in the result will produce the high-resolution climate maps and generate the geographical patterns. Based on the study will produce climate change in the environment like disaster, flood, landslide. The comparative study of interpolation methods and mean variation can be resulted and used for the further implementation of spatial climatic condition and decision support system to the society. The area has undergone the study is Adyar river, situated in latitude 13.0012° N, Longitude 80.2565° E. Inverse Distance Weighted interpolation methods are analyzed by using the Geostatistical tool ArcGIS.

Decision Support Systems for Managing Innovation Through Project Selection in Public Sector R&D Environments

One of the primary components of innovation management in research and development (R&D) organizations is project selection. In public sector R&D environments, it is essential that improvements are made to the selection processes. These improvements should address the unique challenges faced by these organizations in order to increase the quality of the decisions that are made. Modern decision support systems (DSS) have the potential to meet these needs. The implementation of effective DSS backed by solid data management practices and driven by artificial intelligence technologies has the potential to enhance the management of public sector R&D innovation.

Implementation of a computer-guided consultation in the assessment of suspected obstructive sleep apnoea syndrome.

BackgroundWe describe implementation of a clinical decision support system, a computer-guided consultation (CGC), in the assessment of subjects referred with suspected obstructive sleep apnoea syndrome (OSAS).MethodsTwo cohorts of patients were assessed. The first 100 cases had data collected with the CGC by a specialist sleep physician (stage1). A further 100 cases were assessed by a nonspecialist using the CGC (stage 2). For each case, the diagnosis suggested by the CGC was compared with the final diagnosis made by a second specialist sleep physician blinded to the CGC diagnosis.ResultsStage 1: of 100 people evaluated, a final diagnosis of OSAS was made by both the sleep specialist and CGC in 88% of cases. In 7 of the remaining 12 cases, both agreed there was “No evidence of OSAS”; in 5 cases the CGC did not reach a final diagnosis instead prompting specialist referral. Stage 2: 100 people were evaluated; 95% were evaluable. Both CGC and the sleep specialist made a diagnosis of OSAS in 83 cases (87%), in 5 cases both agreed there was no OSAS, whereas in 7 cases the CGC prompted a specialist review due to unexplained symptoms. The CGC was concordant with the final diagnosis in 95% and 93% of cases in the two cohorts, respectively and where there was doubt, prompted for clinical review. No OSAS cases were overlooked by the CGC.ConclusionAn intelligent CGC program creates opportunities in sleep medicine management pathways to safely yet effectively utilise nonspecialists working under specialist supervision.

PT364 - Validation and implementation of a mobile app decision support system for quality assurance of multidisciplinary tumor boards (MTD) to optimize management of advanced testicular cancer

PT364 - Validation and implementation of a mobile app decision support system for quality assurance of multidisciplinary tumor boards (MTD) to optimize management of advanced testicular cancer

796 - Validation and implementation of a mobile app decision support system for quality assurance of tumor boards. Analyzing the concordance rates for prostate cancer at a multidisciplinary tumor board of a tertiary referral centre

796 - Validation and implementation of a mobile app decision support system for quality assurance of tumor boards. Analyzing the concordance rates for prostate cancer at a multidisciplinary tumor board of a tertiary referral centre

A social assessment of forest resource based on stakeholders’ perception: an application in three Balkans rural areas

ABSTRACT : An inclusive and transparent participatory process in the Balkan region can facilitate information’s exchange between decision-makers and local community, and increase social acceptance of forest management outcomes. The study – developed in the “FOR Balkans” project – has two main objectives: stakeholders’ engagement in forest management scenarios’ development and implementation of a Spatial Decision Support System to enhance sustainable and multifunctional forest management. To achieve them, a face-to-face questionnaire survey was conducted in three study areas in the Balkans: Shkrel district (Albania), Rugova valley (Kosovo), Knjaževac municipality (Serbia). The questionnaire – provided as a digital application – was administered to a sample of stakeholders in each study area. Stakeholders’ opinions towards ecosystem services and forest management practices were investigated. Results show that in Rugova provisioning services are the most important ecosystem services, that can be enhanced through a greater use of wood residues for energy purposes. In Shkrel, stakeholders consider supporting services as the most important category of ecosystem services that they can be maintained and improved through the implementation of sanitary fellings. In Knjaževac, stakeholders emphasize cultural services’ importance, which can be enhanced by promoting mixed forests and improving recreational facilities.

Investigating the Barriers to Physician Adoption of an Artificial Intelligence- Based Decision Support System in Emergency Care: An Interpretative Qualitative Study.

The development of artificial intelligence (AI) systems to support diagnostic decision-making is rapidly expanding in health care. However, important challenges remain in executing algorithmic systems at the frontlines of clinical practice. Hence, most often, these systems have not been trained with local data nor do they fit with context-specific patterns of care. This research examines the implementation of an AI-based decision support system (DSS) in the emergency department of a large Academic Health Center (AHC) in Canada, focusing specifically on the question of end-user adoption. Based in an interpretative perspective, the study analyzes the perceptions of healthcare managers, AI developers, physicians and nurses on the DSS, so as to make sense of the main barriers to its adoption by emergency physicians. The study points to the importance of considering interconnections between technical, human and organizational factors to better grasp the unique challenges raised by AI systems in health care. It further emphasizes the need to investigate actors' perceptions of AI in order to develop strategies to adequately test and adapt AI systems, and ensure that they meet the needs of health professionals and patients. This research is particularly relevant at a time when considerable investments are being made to develop and deploy AI-based systems in health care. Empirically probing the conditions under which AI-based systems can effectively be integrated into processes and workflow is essential for maximizing the benefits these investments can bring to the organization and delivery of care.

Implementation and Analysis of Two Knowledge Base Approaches for the Treatment of Chronic Wounds.

The access to data in healthcare is an enabler for the implementation of clinical decision support systems (CDSS) in practice. The usage of CDSS aims to be of efficient assistance to healthcare providers. The aim of the BMBF project "PosiThera", is to support the involved professions in the treatment process of chronic wounds. In this study we implemented the formalized knowledge of chronic wound diagnosis into two different knowledge base approaches, the HL7 Arden Syntax and a Petri net approach. The motivating factor behind our study was to use both approaches for the implementation of the projects knowledge base and to compare the results. We implemented the formalized knowledge successfully in both approaches. The results of our comparison showed similarities and differences of the Arden Syntax and the Petri net approach, which might support the evolution of both approaches in the future.

Implementation of Decision Support System for Determination of Employee Contract Extension Method Using SAW

Effect of rapid technological developments have contributed so as to provide an opportunity to study and analyze a problem. In the company of PT. Cipta Mandiri Agung Jaya, employee contract renewal process is done in less efficient. In general, the process of employee contract extension is carried out by way of division form a contract extension, has not made an assessment atupun evaluation of each employee. Through employee contract renewal process conducted so far gives a great influence to the advancement of the company. On this occasion, in view of the managerial aspects of the formulation of the problem is how to implement the SAW method in the determination of employee contract extension. How to implement a decision support system in the determination of contract extensions employees at PT. Court Of Human Self Jaya, how to design decision support systems in the SAW method using the employee contract extension programming language VB 2010 and the Microsoft Access database. The purpose was to analyze the issue formulation SAW method in the determination of employee contract extension, implementing a decision support system in the determination of contract extensions employees at PT. Court Of Human Self Jaya, pendukunng system design decisions in the SAW method using the employee contract extension programming language VB 2010 and the Microsoft Access database.

The effect of interactive analytical dashboard features on situation awareness and task performance

In recent years, new types of interactive analytical dashboard features have emerged for operational decision support systems (DSS). Analytical components of such features solve optimization problems hidden from the human eye, whereas interactive components involve the individual in the optimization process via graphical user interfaces (GUIs). Despite their expected value for organizations, little is known about the effectiveness of interactive analytical dashboards in operational DSS or their influences on human cognitive abilities. This paper contributes to the closing of this gap by exploring and empirically testing the effects of interactive analytical dashboard features on situation awareness (SA) and task performance in operational DSS. Using the theoretical lens of SA, we develop hypotheses about the effects of a what-if analysis as an interactive analytical dashboard feature on operational decision-makers' SA and task performance. The resulting research model is studied with a laboratory experiment, including eye-tracking data of 83 participants. Our findings show that although a what-if analysis leads to higher task performance, it may also reduce SA, nourishing a potential out-of-the-loop problem. Thus, designers and users of interactive analytical dashboards have to carefully mitigate these effects in the implementation and application of operational DSS. In this article, we translate our findings into implications for designing dashboards within operational DSS to help practitioners in their efforts to address the danger of the out-of-the-loop syndrome.

Exploring the early phase of implementation of a vaccine-based clinical decision support system in the community pharmacy

ObjectiveTo explore the implementation strategy of a recombinant zoster vaccine (RZV) clinical decision support (CDS) intervention in community pharmacy workflow to increase second-dose vaccination rates. SettingThe level of analysis was the unit (e.g., pharmacy). The participants were selected from across approximately 2200 pharmacies in 37 states on the basis of criteria believed to affect implementation success (e.g., size, location) using a sampling matrix. Practice descriptionLarge supermarket pharmacy chain. Practice innovationVaccine-based CDS intervention in community pharmacy workflow. EvaluationA mixed-methods contextual inquiry approach explored the implementation of a new RZV CDS workflow intervention. Data collection involved key informant, semistructured interviews and an electronic, Web-based survey. The survey was based on a validated instrument and was made available to all pharmacists nationwide within the study organization to assess views of the implementation’s appropriateness, acceptability, and feasibility during early implementation. Afterward, a series of semistructured, in-depth interviews were conducted until a point of saturation was reached. The interview guide was based on selected constructs of the Consolidated Framework for Implementation Research. ResultsA total of 1128 survey responses were collected. Survey respondents agreed or strongly agreed that the implementation was acceptable (78.34%), appropriate (79.92%), and feasible (80.53%). Twelve pharmacist participants were interviewed via telephone. Five themes emerged from the interviews, revealing facilitators and barriers that affected implementation of the intervention: intervention characteristics, outer setting, inner setting, characteristics of individuals, and process. ConclusionThe implementation of the RZV CDS “nudge” intervention was welcomed, suitable, and operable in the community pharmacy setting to meet the needs of the organization, employees, and patients. The contextual factors identified during the implementation process of this CDS intervention in a community pharmacy setting may be used in scaling this and future CDS interventions for public health initiatives aimed at pharmacists in this setting.

Basic Conditions for Implementation of an Effective Strategic Decision Support System at a Hospital

The strategic decision support system is an activity management system that can be adapted to an organization from the health care system. It is appropriate to be used by any hospital no matter of its capacity. The strategic decision support system enables to be estimated the financial results, the patients’ needs for achieving successes in their health recovery, the operational activity and the hospital capacity. The purpose of this study is to represent the methodology for strategic decision support system design for estimating the hospitals’ activity effectiveness.

Modified early warning score-based clinical decision support: cost impact and clinical outcomes in sepsis.

ObjectiveThe objective of this study was to assess the clinical and financial impact of a quality improvement project that utilized a modified Early Warning Score (mEWS)-based clinical decision support intervention targeting early recognition of sepsis decompensation.Materials and MethodsWe conducted a retrospective, interrupted time series study on all adult patients who received a diagnosis of sepsis and were exposed to an acute care floor with the intervention. Primary outcomes (total direct cost, length of stay [LOS], and mortality) were aggregated for each study month for the post-intervention period (March 1, 2016–February 28, 2017, n = 2118 visits) and compared to the pre-intervention period (November 1, 2014–October 31, 2015, n = 1546 visits).ResultsThe intervention was associated with a decrease in median total direct cost and hospital LOS by 23% (P = .047) and .63 days (P = .059), respectively. There was no significant change in mortality.DiscussionThe implementation of an mEWS-based clinical decision support system in eight acute care floors at an academic medical center was associated with reduced total direct cost and LOS for patients hospitalized with sepsis. This was seen without an associated increase in intensive care unit utilization or broad-spectrum antibiotic use.ConclusionAn automated sepsis decompensation detection system has the potential to improve clinical and financial outcomes such as LOS and total direct cost. Further evaluation is needed to validate generalizability and to understand the relative importance of individual elements of the intervention.

A data warehouse hybrid design framework using domain ontologies for local good-governance assessment

Purpose Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government. The purpose of this paper is to propose the extraction of the DW schema based on a mixed approach that adopts both i* framework for requirements-based representation and domain ontologies for data source representation, to extract the multi-dimensional (MD) elements. The data was collected from various sources and information systems (ISs) deployed in different municipalities. Design/methodology/approach The authors present a framework for the design and implementation of a DW for local good-governance assessment. The extraction of facts and dimensions of the DW’s MD schema is done using a hybrid approach, where the extraction of requirement-based DW schema and source-based DW schema are done in parallel followed by the reconciliation of the obtained schemas to obtain the good-governance assessment DW final design. Findings The authors developed a novel framework to design and implement a DW for local good-governance assessment. The framework enables the extraction of the DW MD schema by using domain ontologies to help capture semantic artifacts and minimize misconceptions and misunderstandings between different stakeholders. The introduction and use of domain ontologies during the design process serves the generalization and automation purpose of the framework. Research limitations/implications The presently conducted research faced two main limitations as follows: the first is the full automation of the design process of the DW and the second, and most important, is access to local government data as it remains limited because of the lack of digitally stored data in municipalities, especially in developing countries in addition to the difficulty of accessing the data because of regulatory aspects and bureaucracy. Practical implications The local government environment is among the public administrations most subject to change-adverse cultures and where the authors can face high levels of resistance and significant difficulties during the implementation of decision support systems, despite the commitment/engagement of decision-makers. Access to data sources stored by different ISs might be challenging. While approaching the municipalities for data access, it was done in the framework of a research project within one of the most notorious universities in the country, which gave more credibility and trust to the research team. There is also a need for further testing of the framework to reveal its scalability and performance characteristics. Originality/value Compared to other local government assessment ad hoc tools that are partially or entirely based on subjectively collected data, the framework provides a basis for automated design of a comprehensive local government DW using e-government domain ontologies for data source representation coupled with the goal, rationale and business process diagrams for user requirements representations, thus enabling the extraction of the final DW MD schema.

An automatic method based on daily in situ images and deep learning to date wheat heading stage

Accurate and timely observations of wheat phenology and, particularly, of heading date are instrumental for many scientific and technical domains such as wheat ecophysiology, crop breeding, crop management or precision agriculture. Visual annotation of the heading date in situ is a labour-intensive task that may become prohibitive in scientific and technical activities where high-throughput is needed. This study presents an automatic method to estimate wheat heading date from a series of daily images acquired by a fixed RGB camera in the field. A convolutional neural network (CNN) is trained to identify the presence of spikes in small patches. The heading date is then estimated from the dynamics of the spike presence in the patches over time. The method is applied and validated over a large set of 47 experimental sites located in different regions in France, covering three years with nine wheat cultivars. Results show that our method provides good estimates of the heading dates with a root mean square error close to 2 days when compared to the visual scoring from experts. It outperforms the predictions of a phenological model based on the ARCWHEAT crop model calibrated for our local conditions. The potentials and limits of the proposed methodology towards a possible operational implementation in agronomic applications and decision support systems are finally further discussed.

Implementation of an environmental decision support system for controlling the pre-oxidation step at a full-scale drinking water treatment plant.

Drinking water treatment plants (DWTPs) face changes in raw water quality, and treatment needs to be adjusted to produce the best water quality at the minimum environmental cost. An environmental decision support system (EDSS) was developed for aiding DWTP operators in choosing the adequate permanganate dosing rate in the pre-oxidation step. To this end, multiple linear regression (MLR) and multi-layer perceptron (MLP) models are compared for choosing the best predictive model. Besides, a case-based reasoning (CBR) model was approached to provide the user with a distribution of solutions given similar operating conditions in the past. The predictive model consisted of an MLP and has been validated against historical data with sufficient good accuracy for the utility needs (R2 = 0.76 and RSE = 0.13 mg·L-1). The integration of the predictive and the CBR models in an EDSS gives the user an augmented decision-making capacity of the process and has great potential for both assisting experienced users and for training new personnel in deciding the operational set-point of the process.

Critical Succsess Factors Decision Support System for SME

Decision support system is part of information system to support decision making. This research purpose to identify the critical factors influence the successful implementation of decision support systems in SMEs.The purpose of this paper is also to analyze the part of decision support systems in SMEs to see the optimization of decision support system development. This research uses a methodology by identifying systematic literature from several sources from 2006 to 2019. The study found 42 articles relating to decision support systems and SMEs. The results of this research are the critical success factors of decision support system implementation in SMEs.

Implementation of clinical decision support system for anticoagulant prescribing for patients with deep vein thrombosis

Background: Physicians adherence to recommendations for appropriate antithrombotic therapy of venous thromboembolism (VTE) can reduce the risk of recurrent VTE, pulmonary hypertension, bleeding and other adverse events. Clinical decision support systems (CDSS) are shown to increase physicians adherence to clinical guidelines.
 Aims: To assess effectivenes and safety of CDSS for anticoagulant prescribing for inhospital patients with VTE.
 Methods: A prospective cohort study was conducted in a Moscow general hospital from 06.30.2017 to 06.23.2018 to compare physicians compliance with clinical guidelines for DVT anticoagulant therapy, the rate of drug errors and direct costs of anticoagulant therapy before and after CDSS implementation (55 patients in control group and 49 in experimental group).
 Results: The rate of anticoagulant prescribing for patients with DVT did not alter significantly after CDSS implementation (96% compared with 91% before CDSS), but physicians compliance with recommendations on anticoagulant dosage increased from 32.7% to 73.5% (p = 0.0003) with corresponding decrease in the rate of anticoagulant prescribing errors from 1.35 to 0.65 per 1 patient (p = 0.0005). The length of stay and hemorrhagic complication rate did not differ between control and experimental groups. LMWH replacement with new oral anticoagulants has reduced the cost of anticoagulant therapy for 1 patient from 11.800 rubles (IQR = 7000) to 5.430 rubles (IQR = 5700) (p 0.005).
 Conclusions: СDSS can increase physicians adherence to recommended anticoagulant therapy for patients with DVT: to prevent unreasonable under-/overdosing or prolongation of anticoagulant therapy. CDSS for DVT drug therapy can be economically feasible.

Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates

The trade-off between computation time and predictive accuracy is important in the design and implementation of clinical decision support systems. Machine learning techniques with incremental updates have proven its usefulness in analyzing large collection of medical datasets for diseases diagnosis. This research aims to develop a predictive method for heart disease diagnosis using machine learning techniques. To this end, the proposed method is developed by unsupervised and supervised learning techniques. In particular, this research relies on Principal Component Analysis (PCA), Self-Organizing Map, Fuzzy Support Vector Machine (Fuzzy SVM), and two imputation techniques for missing value imputation. Furthermore, we apply the incremental PCA and FSVM for incremental learning of the data to reduce the computation time of disease prediction. Our data analysis on two real-world datasets, Cleveland and Statlog, showed that the use of incremental Fuzzy SVM can significantly improve the accuracy of heart disease classification. The experimental results further revealed that the method is effective in reducing the computation time of disease diagnosis in relation to the non-incremental learning technique.

Validation and implementation of a mobile app decision support system for quality assurance of tumor boards: Analyzing the concordance rates for testis cancer at a multidisciplinary tumor board of a tertiary referral center.

399 Background: Guideline-compliance in the management of GCT is suboptimal concerning and it results in significantly impaired long-term survival. To facilitating guideline concordant decisions even in less experienced MTD, we developed an algorithm-driven and validated mobile App to provide evidence-based recommendations. This App contains latest guideline versions and it is continuously updated to include practice changing clinical trials. It is the purpose of this study to analyse treatment decision made by MTD compared to guideline recommended therapy. Methods: 302 patients with GCT and MTD decision were randomly selected from our database. Concordance rates of MTD recommendations were compared to query results of the digital APP “Easy Oncology” performed by 2 independent, blinded reviewers. Descriptive statistics and data analysis included IBM´s statistics software SPSS Version 25. Correlation analysis of cancer case characteristics was performed by Kendalls Tau, Gamma, Pearson and Spearman tests. Results: Median age was 35 (18-82) years. Clinical stages I, IIA-C, and III were identified in 32%, 37%, and 31%, resp. According to IGCCCG 56%, 18%, and 25% demonstrated good, intermediate, and poor prognosis, resp. 56% were non-seminomas and 44% were seminomas. 60%, 16%, and 7% of MTD decisions were related to 1st-line, 2nd, and 3rd line therapy and 18% dealt with follow up. Overall concordance rate was 99% (301/302). Concordance rates in CS I, IIA-C, and III were 100%, 98% and 99%, resp. Concordance rates with regard to 1st, 2nd and 3rd line therapy were 99% (135/136), 100%, and 88% (13/14). Conclusions: Our validated expert-created mobile decision support system demonstrates easy and reliable application in daily routine in an expert tertiary referral centre. The APP relies on guideline and trial based information and avoids MTD decisions by expert opinions. It might improve the MTD decision in less experienced MTDs which is currently evaluated in a prospective study.