Journal article
Microsimulation Model Using Christiana Care Early Warning System (CEWS) to Evaluate Physiological Deterioration
IEEE journal of biomedical and health informatics, v 23(5), pp 2189-2195
Sep 2019
PMID: 30295635
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
While physiological warning signs prior to deterioration events during hospitalization have been widely studied, evaluating clinical interventions, such as rapid response team (RRT) activations, based on scoring systems remains an understudied area. Simulation of physiological deterioration patterns represented by scoring systems can facilitate testing different RRT policies without disturbing care processes. Christiana Care Early Warning System (CEWS) is a scoring system developed at the study hospital to detect the physiological warning signs and inform RRT activations. The objective of this study is to evaluate CEWS-triggered RRT policies based on patient demographics and policy structures. Using retrospective data derived from a subset of electronic health records between December 2015 and December 2016 (6000 patients), we developed a microsimulation model with integrated regression analysis to compare RRT policies on subpopulations defined by age, gender, and comorbidities to find score thresholds that result in the lowest percent of time spent above critical CEWS values. Policies that rely on average scores were more sensitive to threshold changes compared to policies that rely on current value and change in the CEWS. Policy using score threshold 10 provided the lowest percentage of time under the critical condition for majority of subpopulations. The proposed model is a novel framework to simulate individual deterioration patterns and systematically evaluate RRT policies based on their impact on health conditions. Our work highlights the importance of integration of data-driven models into personalized care and represents a significant opportunity to inform biomedical and health informatics research on designing and evaluating EWS-based clinical interventions.
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Details
- Title
- Microsimulation Model Using Christiana Care Early Warning System (CEWS) to Evaluate Physiological Deterioration
- Creators
- Bin Li - University of Arkansas at FayettevilleShengfan Zhang - University of Arkansas at FayettevilleStephen Hoover - Christiana Care Health SystemRyan Arnold - Drexel UniversityMuge Capan - Decision Sciences & MIS, Drexel University Bennett's LeBow College of Business, Philadelphia, PA, USA
- Publication Details
- IEEE journal of biomedical and health informatics, v 23(5), pp 2189-2195
- Publisher
- IEEE
- Grant note
- Christiana Care Health System Value Institute, the Department of Medicine
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000489729400039
- Scopus ID
- 2-s2.0-85054490521
- Other Identifier
- 991019168084604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Medical Informatics