title: Accuracy of prehospital transport time estimation.
abstract: OBJECTIVE
  Estimates of prehospital transport times are an important part of emergency care system research and planning; however, the accuracy of these estimates is unknown. The authors examined the accuracy of three estimation methods against observed transport times in a large cohort of prehospital patient transports.
  
  
  METHODS
  This was a validation study using prehospital records in King County, Washington, and southwestern Pennsylvania from 2002 to 2006 and 2005 to 2011, respectively. Transport time estimates were generated using three methods: linear arc distance, Google Maps, and ArcGIS Network Analyst. Estimation error, defined as the absolute difference between observed and estimated transport time, was assessed, as well as the proportion of estimated times that were within specified error thresholds. Based on the primary results, a regression estimate was used that incorporated population density, time of day, and season to assess improved accuracy. Finally, hospital catchment areas were compared using each method with a fixed drive time.
  
  
  RESULTS
  The authors analyzed 29,935 prehospital transports to 44 hospitals. The mean (± standard deviation [±SD]) absolute error was 4.8 (±7.3) minutes using linear arc, 3.5 (±5.4) minutes using Google Maps, and 4.4 (±5.7) minutes using ArcGIS. All pairwise comparisons were statistically significant (p < 0.01). Estimation accuracy was lower for each method among transports more than 20 minutes (mean [±SD] absolute error was 12.7 [±11.7] minutes for linear arc, 9.8 [±10.5] minutes for Google Maps, and 11.6 [±10.9] minutes for ArcGIS). Estimates were within 5 minutes of observed transport time for 79% of linear arc estimates, 86.6% of Google Maps estimates, and 81.3% of ArcGIS estimates. The regression-based approach did not substantially improve estimation. There were large differences in hospital catchment areas estimated by each method.
  
  
  CONCLUSIONS
  Route-based transport time estimates demonstrate moderate accuracy. These methods can be valuable for informing a host of decisions related to the system organization and patient access to emergency medical care; however, they should be employed with sensitivity to their limitations.
authors.affiliation: Clinical Research, Investigation and Systems Modeling of Acute Illness (CRISMA) Center, the Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA; The Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA.
authors.forename: David J
authors.initials: DJ
authors.lastname: Wallace
authors.forename: Jeremy M
authors.initials: JM
authors.lastname: Kahn
authors.forename: Derek C
authors.initials: DC
authors.lastname: Angus
authors.forename: Christian
authors.initials: C
authors.lastname: Martin-Gill
authors.forename: Clifton W
authors.initials: CW
authors.lastname: Callaway
authors.forename: Thomas D
authors.initials: TD
authors.lastname: Rea
authors.forename: Jagpreet
authors.initials: J
authors.lastname: Chhatwal
authors.forename: Kristen
authors.initials: K
authors.lastname: Kurland
authors.forename: Christopher W
authors.initials: CW
authors.lastname: Seymour
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D004632:Emergency Medical Services / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010414:Pennsylvania; D012044:Regression Analysis; D013997:Time Factors; D014187:Transportation of Patients / Q000706:statistics & numerical data*; D014861:Washington