Linking ambulance, emergency department and hospital admissions data: understanding the emergency journey

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...

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Veröffentlicht in:Medical journal of Australia 2011-02, Vol.194 (4), p.S34-S37
Hauptverfasser: Crilly, Julia L, O'Dwyer, John A, O'Dwyer, Marilla A, Lind, James F, Peters, Julia A L, Tippett, Vivienne C, Wallis, Marianne C, Bost, Nerolie F, Keijzers, Gerben B
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container_end_page S37
container_issue 4
container_start_page S34
container_title Medical journal of Australia
container_volume 194
creator Crilly, Julia L
O'Dwyer, John A
O'Dwyer, Marilla A
Lind, James F
Peters, Julia A L
Tippett, Vivienne C
Wallis, Marianne C
Bost, Nerolie F
Keijzers, Gerben B
description 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.
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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%. 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subjects Ambulances - statistics & numerical data
Data Collection
Emergency Medical Services - statistics & numerical data
Emergency medicine
Emergency Service, Hospital - statistics & numerical data
Health services administration
Hospitalization - statistics & numerical data
Hospitals, Teaching - statistics & numerical data
Humans
Outcome Assessment, Health Care - methods
Outcome Assessment, Health Care - statistics & numerical data
Patient Identification Systems
Queensland
title Linking ambulance, emergency department and hospital admissions data: understanding the emergency journey
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