Authors:
Raphael Bahati
1
;
Michael Bauer
2
and
Femida Gwadry-Sridhar
1
Affiliations:
1
I-THINK Research and Lawson, Canada
;
2
The University of Western Ontario, Canada
Keyword(s):
Acute myocardial infarction, Predicting health outcomes, Mathematical modeling, Cluster analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Clinical Problems and Applications
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Acute Myocardial Infarction (AMI) remains a leading cause of mortality in most industrialized nations. Mortality rates for AMI patients are often used as a measure of the overall effectiveness of care provided by hospitals. Age, gender, and severity adjusted, the mortality rates within Canada have been shown to vary significantly from province to province. Some studies, for example, have shown significant variations between counties, even when adjacent to each other. In this paper, we present an approach aimed at understanding the causes of this variability by investigating the extent to which evidence-based therapies and processes within hospitals might be affecting mortality rates. We use cluster analysis to identify beneficial therapies and processes responsible for the improvement in treatment outcomes (as measured in terms of standardized mortality ratio) in benchmark compared to non-benchmark hospitals.