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

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.