Ontology highlight
ABSTRACT: Study objective
We develop a novel approach for measuring regional outcomes for emergency care-sensitive conditions.Methods
We used statewide inpatient hospital discharge data from the Pennsylvania Healthcare Cost Containment Council. This cross-sectional, retrospective, population-based analysis used International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes to identify admissions for emergency care-sensitive conditions (ischemic stroke, ST-segment elevation myocardial infarction, out-of-hospital cardiac arrest, severe sepsis, and trauma). We analyzed the origin and destination patterns of patients, grouped hospitals with a hierarchical cluster analysis, and defined boundary shapefiles for emergency care service regions.Results
Optimal clustering configurations determined 10 emergency care service regions for Pennsylvania.Conclusion
We used cluster analysis to empirically identify regional use patterns for emergency conditions requiring a communitywide system response. This method of attribution allows regional performance to be benchmarked and could be used to develop population-based outcome measures after life-threatening illness and injury.
SUBMITTER: Carr BG
PROVIDER: S-EPMC10211477 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
Carr Brendan G BG Kilaru Austin S AS Karp David N DN Delgado M Kit MK Wiebe Douglas J DJ
Annals of emergency medicine 20180901 3
<h4>Study objective</h4>We develop a novel approach for measuring regional outcomes for emergency care-sensitive conditions.<h4>Methods</h4>We used statewide inpatient hospital discharge data from the Pennsylvania Healthcare Cost Containment Council. This cross-sectional, retrospective, population-based analysis used International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes to identify admissions for emergency care-sensitive conditions (ischemic stroke, ST-s ...[more]