scholarly journals Real-time reconciled simulation as decision support tool for process operation

2021 ◽  
Vol 100 ◽  
pp. 41-64
Author(s):  
Anibal Galan ◽  
Cesar De Prada ◽  
Gloria Gutierrez ◽  
Daniel Sarabia ◽  
Rafael Gonzalez
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Decision Support Tool ◽  
Support Tool ◽  
Process Operation

Lessons learned from the implementation of the near real-time S2S4E Decision Support Tool

2021 ◽  
Author(s):  
Lluís Palma ◽  
Andrea Manrique ◽  
Llorenç Lledó ◽  
Andria Nicodemou ◽  
Pierre-Antoine Bretonnière ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Climate Models ◽  
Decision Support Tool ◽  
Lessons Learned ◽  
Cloud Infrastructure ◽  
Time Service ◽  
Support Tool ◽  
Wide Range ◽  
Prediction Systems

<p>Under the context of the H2020 S2S4E project, industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing during 18 months near real-time subseasonal and seasonal  forecasts tailored to the specific needs of the renewable energy sector. The tool aimed to breach the last mile gap between climate information and the end-user by paying attention to the interaction with agents from the sector, already used to work with weather information, and willing to extend their forecasting horizon by incorporating climate predictions into their daily operations.</p><p>With this purpose, the tool gathered a heterogeneous dataset of seven different essential climate variables and nine energy indicators, providing for each of them bias-adjusted probabilistic information paired with a reference skill metric. To achieve this, data from state-of-the-art prediction systems and reanalysis needed to be downloaded and post-processed, fulfilling a set of quality requirements that ensure the proper functioning of the operational service. During the design, implementation, and testing phases, a wide range of scientific and technical choices had to be made, making clear the difficulties of transferring scientific research to a user-oriented real-time service. A brief showcase will be presented, exemplifying the different tools, methodologies, and best practices applied to the data workflow, together with a case study performed in Oracle’s cloud infrastructure. We expect that by making a clear description of the process and the problems encountered, we will provide a valuable experience for both, upcoming attempts of similar implementations, and the organizations providing data from climate models and reanalysis.</p>

Interrater Reliability, Accuracy, and Triage Time Pre- and Post-implementation of a Real-Time Electronic Triage Decision-Support Tool

2020 ◽  
Vol 75 (4) ◽  
pp. 524-531 ◽  
Author(s):  
Shelley L. McLeod ◽  
Joy McCarron ◽  
Tamer Ahmed ◽  
Keerat Grewal ◽  
Nicole Mittmann ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Interrater Reliability ◽  
Decision Support Tool ◽  
Support Tool ◽  
Triage Decision ◽  
Triage Time

scholarly journals Reducing Wastage of Inhalation Anesthetics Using Real-time Decision Support to Notify of Excessive Fresh Gas Flow

2013 ◽  
Vol 118 (4) ◽  
pp. 874-884 ◽  
Author(s):  
Bala G. Nair ◽  
Gene N. Peterson ◽  
Moni B. Neradilek ◽  
Shu Fang Newman ◽  
Elaine Y. Huang ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Decision Rule ◽  
Gas Flow ◽  
Decision Rules ◽  
Decision Support Tool ◽  
Information Management System ◽  
Inhalation Anesthetics ◽  
Support Tool ◽  
The Mean

Abstract Background: Reduced consumption of inhalation anesthetics can be safely achieved by reducing excess fresh gas flow (FGF). In this study the authors describe the use of a real-time decision support tool to reduce excess FGF to lower, less wasteful levels. Method: The authors applied a decision support tool called the Smart Anesthesia Manager™ (University of Washington, Seattle, WA) that analyzes real-time data from an Anesthesia Information Management System to notify the anesthesia team if FGF exceeds 1 l/min. If sevoflurane consumption reached 2 minimum alveolar concentration-hour under low flow anesthesia (FGF < 2 l/min), a second message was generated to increase FGF to 2 l/min, to comply with Food and Drug Administration guidelines. To evaluate the tool, mean FGF between surgical incision and the end of procedure was compared in four phases: (1) a baseline period before instituting decision rules, (2) Intervention-1 when decision support to reduce FGF was applied, (3) Intervention-2 when the decision rule to reduce flow was deliberately inactivated, and (4) Intervention-3 when decision rules were reactivated. Results: The mean ± SD FGF reduced from 2.10 ± 1.12 l/min (n = 1,714) during baseline to 1.60 ± 1.01 l/min (n = 2,232) when decision rules were instituted (P < 0.001). When the decision rule to reduce flow was inactivated, mean FGF increased to 1.87 ± 1.15 l/min (n = 1,732) (P < 0.001), with an increasing trend in FGF of 0.1 l/min/month (P = 0.02). On reactivating the decision rules, the mean FGF came down to 1.59 ± 1.02 l/min (n = 1,845). Through the Smart Anesthesia Messenger™ system, the authors saved 9.5 l of sevoflurane, 6.0 l of desflurane, and 0.8 l isoflurane per month, translating to an annual savings of $104,916. Conclusions: Real-time notification is an effective way to reduce inhalation agent usage through decreased excess FGFs.

scholarly journals Bridging the Text-Image Gap: a Decision Support Tool for Real-Time PACS Browsing

2011 ◽  
Vol 25 (2) ◽  
pp. 227-239 ◽  
Author(s):  
Merlijn Sevenster ◽  
Rob van Ommering ◽  
Yuechen Qian
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Decision Support Tool ◽  
Support Tool

scholarly journals 2223. Real-time Prediction of Respiratory Pathogen Infection Based on Machine Learning Decision Support Tool

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S758-S759
Author(s):  
Ran Nir-Paz ◽  
Gal Almogy ◽  
Arie Keren ◽  
Guy Livne ◽  
Sharon Amit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Real Time ◽  
Learning Algorithm ◽  
Medical Center ◽  
Decision Support Tool ◽  
Disease Spread ◽  
Respiratory Pathogen ◽  
Predictive Values ◽  
Support Tool

Abstract Background Respiratory pathogens are a common cause of disease. Currently there is not a practical tool to predict the putative etiology of each case with an inexpensive, fast point-of-care assay. Here, we describe a decision support tool that enables the prediction of both bacterial and viral respiratory pathogen infections in a single patient, using a Machine Learning model. Methods The data were obtained from the Hadassah-Hebrew University Medical Center during a period of 10 years beginning from 2007 and contained more than 40,000 patients from a 1,000,000-population community for whom specimens were tested by either PCR or culture. The pathogens included were, H. influenzae; M. catarrhalis; S. pneumoniae; M. pneumoniae; Adenovirus; Human metapneumovirus; Influenza H1N1, A, B; parainfluenza 1,2 and 3; and RSV. We then created a Machine-Learning algorithm to simulate the spread of infection in the entire Jerusalem area. We defined transmission areas based on geographical distances of patients’ home-addresses. Then we prospectively tested the tool accuracy over a 4-month period, in addition to real-time improvement of the model. Results Initial model was created based on gender, age, home addresses and the diagnostics test results. We then reconstructed a putative spread pattern for each of the pathogens that can be correlated to potential “transmission routes.” The initial prediction tool had an AUC for most pathogens around 0.85. It ranged from 0.75 to 0.8 for the bacterial and 0.82 to 0.89 for the viral pathogens. In almost all pathogens the NPV was 0.98–0.99. We then tested the decision support tool prospectively over four consecutive months (January to April 2019—1,700 patients with respiratory complaints from whom samples were sent to the lab). While the AUC in the prospective cohort was 0.81 on average, the NPV remained high on 0.98. Conclusion The implementation of the decision support tool on respiratory pathogen diagnostics enables better prediction of patients not infected with either viral or bacterial pathogens. The use of such a tool can save more than 50% of diagnostic tests expenses as well as real-time mapping of disease spread. Improvement of the Machine Learning protocol may further promote the optimization of positive predictive values. Disclosures All authors: No reported disclosures.

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