title: Cost analysis of the use of emergency departments for primary care services in Charlotte, North Carolina.
abstract: BACKGROUND
  Patients often inappropriately seek emergency services for ambulatory care-sensitive conditions (ACSCs). The unnecessary use of emergency departments (EDs) is an expensive burden on hospitals and payers. Here, we identify factors influencing ED visits for ACSCs and analyze the costs of such visits for EDs and primary care clinics.
  
  
  METHODS
  Age, race, sex, and insurance data from 2007 for 3 primary care safety net clinics and 4 EDs in Charlotte, North Carolina, were analyzed using the New York University (NYU) algorithm to identify ACSC diagnoses. Cost analyses used hospital charge data and net margins as surrogates for payer and hospital system costs.
  
  
  RESULTS
  A total of 113,730 (59.4%) of 191,622 ED visits were for ACSCs. Factors that increased the number of ACSC-related visits included lack of insurance coverage; receipt of Medicaid insurance; age of less than 2 years; African American, Hispanic, or Native American race or ethnicity; and female sex. Charges in the EDs were 320%-728% higher than those in the primary care clinics, allowing for a potential savings of 69%-86% had ACSCs been treated in primary care clinics instead of in EDs.
  
  
  CONCLUSIONS
  The NYU algorithm may have inherent weaknesses in the categorization of ACSC-related visits and the accuracy of cost assignment, especially for vulnerable patients, such as those with comorbidities or those aged less than 2 years.
  
  
  CONCLUSIONS
  The majority of conditions treated during outpatient ED visits are treatable in primary care clinics or even preventable. Some groups are at higher risk for inappropriate use of EDs. Solutions to this complex problem will require payers and hospital systems to design and invest in novel targeted interventions.
authors: School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
authors: Andrew
authors: A
authors: McWilliams
authors: Hazel
authors: H
authors: Tapp
authors: Jolene
authors: J
authors: Barker
authors: Michael
authors: M
authors: Dulin
mesh_terms: D017677:Age Distribution; D000465:Algorithms; D016009:Chi-Square Distribution; D003365:Costs and Cost Analysis; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005006:Ethnicity / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D019458:Insurance Coverage / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data; D009657:North Carolina; D011320:Primary Health Care / Q000191:economics*; D012307:Risk Factors; D012737:Sex Factors; D014481:United States