Data harmonization of Australian and New Zealand Ambulance Service Datasets

Abstract: Background
Eight state-based ambulance services (emergency medical
services; EMS) across Australia and two across New Zealand
(NZ) provide emergency medical response to telephone calls
for assistance for the 26 million Australians and five million
New Zealanders. Because these ambulance services operate
independently from one another, significant variation exists in their
patient data collection methods, the variables collected and the
variable definitions. This has compromised performance benchmarking,
clinical audit and cross-border research opportunities
and translation of research to improve patient care. Ambulance
data harmonisation has occurred in the United States and United
Kingdom however, to-date no data harmonisation has occurred in
Australia.
Objectives
This study aims to compare ambulance service variables in
Australia and NZ to identify opportunities and barriers for data
harmonisation.
Method
Dataset variable lists for four ambulance services were available
online, and the remainder were requested from the ambulance
services. Three services provided their lists, and three did not
respond to the request. Two of these used the same electronic
patient care record (ePCR) system as one whose list had been
provided and one variable list was not sourced. Variables were
mapped to each other and several international standardized
terminology systems to identify variations and similarities in
variable names and definitions, and harmonisation opportunities.
Results
The ambulance services are at varying stages of maturity with
respect to data collection techniques. Four Australian ambulance
services used the same ePCR system, three used other ePCR
systems, one used paper-based records and both NZ services
used a single ePCR system. Only the NZ services had mapped
their variables to two international standardised terminology
systems (Systematized Nomenclature of Medicine – Clinical
Terms (SNOMED-CT) and Logical Observation Identifiers, Names
and Codes (LOINC) terms).
Three main barriers to harmonisation were identified. These
included the variables collected, the variable definitions and the
variable naming convention. The core variables available for
mapping varied and numbered from 27-69. Across the datasets,
variables with similar names often had different definitions and
variables that should have had different definitions, had the same.
For example ‘gender’ and ‘sex’ often had the same definition, despite accepted definitions indicating that ‘sex’ refers to the
biological chromosomal and anatomical distinction whereas
‘gender’ is defined as the gender to which a person identifies
which can include ‘unspecified’, ’transgender/sexual’, ’gender
diverse’, ’pan-gendered‘, and ’inter-gender‘. Finally, the naming
convention for similar/same variables differing between services.
For example the suburb to which an ambulance is called could
be named ‘scene suburb’, ‘city’, ‘suburb’, ‘scene location’ or even
‘From3’.
Conclusions
Variation exists in the variables collected by ambulance services
across Australia and NZ presenting a range of barriers to data
harmonization. However, the mapping in this study demonstrates
that data harmonisation in Australia and NZ is possible and
presents significant opportunities for improvement in patient
outcomes and performance audit. It would also facilitate quality,
large-scale, high-impact collaborative national and international
research.