Implementing Real-Time Clinical Decision Support Applications on OpenICE: A Case Study Using the National Early Warning System Algorithm

2019 ◽  
Author(s):  
David Arney ◽  
Yi Zhang ◽  
Julian M. Goldman ◽  
Barbara Dumas
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Clinical Decision

scholarly journals Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

2016 ◽  
Vol 124 (9) ◽  
pp. 1369-1375 ◽  
Author(s):  
Yuan Shi ◽  
Xu Liu ◽  
Suet-Yheng Kok ◽  
Jayanthi Rajarethinam ◽  
Shaohong Liang ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Policy Decision ◽  
Forecast Models

scholarly journals Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Simon Fong ◽  
Yang Zhang ◽  
Jinan Fiaidhi ◽  
Osama Mohammed ◽  
Sabah Mohammed
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Stream Mining ◽  
Diabetes Therapy

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

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

JAMIA Open ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 261-268
Author(s):  
Devin J Horton ◽  
Kencee K Graves ◽  
Polina V Kukhareva ◽  
Stacy A Johnson ◽  
Maribel Cedillo ◽  
...  
Keyword(s):  
Decision Support ◽  
Acute Care ◽  
Direct Cost ◽  
Clinical Decision Support ◽  
Early Warning ◽  
Clinical Decision ◽  
Early Warning Score ◽  
Total Direct Cost ◽  
Intervention Period ◽  
Support Cost

Abstract Objective The 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 Methods We 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). Results The 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. Discussion The 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. Conclusion An 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 Reliability-Based Early Warning System for Real-Time Monitoring on RC Buildings

2012 ◽  
Vol 446-449 ◽  
pp. 3422-3427
Author(s):  
Wang Sheng Liu ◽  
Ming Zhao
Keyword(s):  
Probability Distribution ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Limit State ◽  
Warning System ◽  
State Function ◽  
Real Time Monitoring ◽  
Rc Buildings ◽  
Load Effect

Today there is an urgent need for effective monitoring whether for old buildings or new ones. While conventional early warning system for real-time monitoring is based on safety factor, this paper proposes a new reliability-based framework to monitor the safety of RC buildings probabilistically. The framework includes modeling resistance, predicting probability distribution of load effect, calculating reliability and setting reliability index threshold. The in-situ test data enables to update the resistance model through a Bayesian process. Meanwhile, the observed monitoring data predicts the probability distribution of load effect. FORM is used to calculate the reliability because the limit state function for real-time monitoring is linear and simple. This study shows that the reliability-based early warning system is of more scientific sense in quantifying the safety and may be applied to many engineering fields.

Sign in / Sign up

Export Citation Format

Share Document