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

Abstract

We develop a novel approach for measuring regional outcomes for emergency care-sensitive conditions. 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. Optimal clustering configurations determined 10 emergency care service regions for Pennsylvania. 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.