Mastering Linked Datasets: The Future of Emergency Health Care Research

Abstract

Objectives: The aim of this work is to describe routine integration of prehospital emergency health records into a health master linkage file, delivering ongoing access to integrated patient treatment and outcome information for ambulance-attended patients in Queensland.

Methods: The Queensland Ambulance Service (QAS) data are integrated monthly into the Queensland Health Master Linkage File (MLF) using a linkage algorithm that relies on probabilistic matches in combination with deterministic rules based on patient demographic details, date, time and facility identifiers. Each ambulance record is assigned an enduring linkage key (unique patient identifier) and further processing determines whether each record matches with a corresponding hospital emergency department, admission or death registry record. In this study, all QAS electronic ambulance report form (eARF) records from October 2016 to December 2018 where at least 1 key linkage variable was present (n = 1,771,734) were integrated into the MLF.

Results: The majority of records (n = 1,456,502; 82.2%) were for transported patients, and 90.1% (n = 1,312,176) of these transports were to public hospital facilities. Of these transport records, 93.9% (n = 1,231,951) matched to emergency department (ED) records and 59.3% (n = 864,394) also linked to admitted patient records. Of ambulance non-transport records integrated into the MLF, 23.6% (n = 74,311) matched with ED records.

Conclusion: This study demonstrates robust linkage methods, quality assurance processes and high linkage rates of data across the continuum of care (prehospital/emergency department/admitted patient/death) in Queensland. The resulting infrastructure provides a high-quality linked dataset that facilitates complex research and analysis to inform critical functions such as quality improvement, system evaluation and design.

Acknowledgments

The authors would like to thank paramedic and hospital personnel for the care provided to these patients and submission of the clinical data. We also thank the clerical review officers at Data Linkage Queensland. We thank Tan Doan for his assistance in quantifying missing key linkage variables.

Disclosure Statement

The authors report no conflict of interest.