Brendan G Carr1, Austin S Kilaru2, David N Karp3, M Kit Delgado4, Douglas J Wiebe5. 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.
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.
Authors: Anita A Vashi; Tracy Urech; Brendan Carr; Liberty Greene; Theodore Warsavage; Renee Hsia; Steven M Asch Journal: JAMA Netw Open Date: 2019-08-02
Authors: Daniel N Holena; Elinore J Kaufman; Justin Hatchimonji; Brian P Smith; Ruiying Xiong; Thomas E Wasser; M Kit Delgado; Douglas J Wiebe; Brendan G Carr; Patrick M Reilly Journal: J Trauma Acute Care Surg Date: 2020-01 Impact factor: 3.697