Format

Send to

Choose Destination
Ann Emerg Med. 2018 Sep;72(3):237-245. doi: 10.1016/j.annemergmed.2018.03.004.

Quality Through Coopetition: An Empiric Approach to Measure Population Outcomes for Emergency Care-Sensitive Conditions.

Author information

1
Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA. Electronic address: brendan.carr@jefferson.edu.
2
Department of Emergency Medicine, Highland Hospital, Oakland, CA.
3
Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
4
Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
5
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

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.

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center