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Article
Linking ambulance, emergency department and hospital admissions data: Understanding the emergency journey
Medical journal of Australia
  • Julia L Crilly, Queensland Health
  • John A O'Dwyer, CSIRO ICT Centre
  • Marilla A O'Dwyer, CSIRO ICT Centre
  • James F Lind, Queensland Health
  • Julia A.L. Peters, Gold Coast Hospital
  • Vivienne C Tippett, University of Queensland
  • Marianne C Wallis, Griffith Health Institute
  • Nerolie F Bost, Queensland Health
  • Gerben B Keijzers, Bond University
Date of this Version
2-21-2011
Document Type
Journal Article
Publication Details
Published Version.

Crilly, J.L., O'Dwyer, J.A., O'Dwyer, M.A., Lind, J.F., Peters, J.A.L., Tippett, V.C., Wallis, M.C., Bost, N.F. & Keijzers, G.B. (2011). Linking ambulance, emergency department and hospital admissions data: Understanding the emergency journey. Medical journal of Australia, 194(4), S34-S37.

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© Copyright Australasian Medical Publishing Company, 2011
Abstract

Objective: To assess the accuracy of data linkage across the spectrum of emergency care in the absence of a unique patient identifier, and to use the linked data to examine service delivery outcomes in an emergency department (ED) setting.

Design: Automated data linkage and manual data linkage were compared to determine their relative accuracy. Data were extracted from three separate health information systems: ambulance, ED and hospital inpatients, then linked to provide information about the emergency journey of each patient. The linking was done manually through physical review of records and automatically using a data linking tool (Health Data Integration) developed by the CSIRO (Commonwealth Scientific and Industrial Research Organisation). Match rate and quality of the linking were compared.

Setting: 10 835 patient presentations to a large, regional teaching hospital ED over a 2-month period (August – September 2007).

Results: Comparison of the manual and automated linkage outcomes for each pair of linked datasets demonstrated a sensitivity of between 95% and 99%; a specificity of between 75% and 99%; and a positive predictive value of between 88% and 95%.

Conclusions: Our results indicate that automated linking provides a sound basis for health service analysis, even in the absence of a unique patient identifier. The use of an automated linking tool yields accurate data suitable for planning and service delivery purposes and enables the data to be linked regularly to examine service delivery outcomes.

Citation Information
Julia L Crilly, John A O'Dwyer, Marilla A O'Dwyer, James F Lind, et al.. "Linking ambulance, emergency department and hospital admissions data: Understanding the emergency journey" Medical journal of Australia Vol. 194 Iss. 4 (2011) p. S34 - S37
Available at: http://works.bepress.com/gerben_keijzers/11/