Document Detail

Models of emergency departments for reducing patient waiting times.
MedLine Citation:
PMID:  19572015     Owner:  NLM     Status:  MEDLINE    
In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow through emergency departments. The objective of this work is to gain insights into the comparative contributions and limitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and practice guidelines. The models were developed independently, with a view to compare their suitability to emergency department simulation. The current models implement relatively simple general scenarios, and rely on a combination of simulated and real data to simulate patient flow in a single emergency department or in multiple interacting emergency departments. In addition, several concepts from telecommunications engineering are translated into this modeling context. The framework of multiple-priority queue systems and the genetic programming paradigm of evolutionary machine learning are applied as a means of forecasting patient wait times and as a means of evolving healthcare policy, respectively. The models' utility lies in their ability to provide qualitative insights into the relative sensitivities and impacts of model input parameters, to illuminate scenarios worthy of more complex investigation, and to iteratively validate the models as they continue to be refined and extended. The paper discusses future efforts to refine, extend, and validate the models with more data and real data relative to physical (spatial-topographical) and social inputs (staffing, patient care models, etc.). Real data obtained through proximity location and tracking system technologies is one example discussed.
Marek Laskowski; Robert D McLeod; Marcia R Friesen; Blake W Podaima; Attahiru S Alfa
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Publication Detail:
Type:  Journal Article     Date:  2009-07-02
Journal Detail:
Title:  PloS one     Volume:  4     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2009  
Date Detail:
Created Date:  2009-07-02     Completed Date:  2009-11-10     Revised Date:  2013-06-02    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e6127     Citation Subset:  IM    
Internet Innovation Centre, Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada.
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MeSH Terms
Emergency Service, Hospital / organization & administration*
Health Services Accessibility
Models, Organizational*
Personnel Staffing and Scheduling
Time and Motion Studies*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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