• I E Blanchard, T S Williamson, P Ronksley, B Hagel, D Niven, S Dean, M N Shah, E S Lang, and C J Doig.
• Received April 6, 2021 from Emergency Medical Services, Emergency, Critical Care, Alberta Health Services, Alberta, Canada (IEB, DN, SD, ESL, CJD); Cumming School of Medicine - Community Health Sciences, Critical Care, Emergency, and Pediatrics; Faculty of Kinesiology, Sports Injury Prevention Research Centre, University of Calgary, Alberta Children's Hospital Research Institute and O'Brien Institute for Public Health, Calgary, Alberta, Canada (IEB, TSW, PR, BH, DN, SD, ESL, CJD); Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA (MNS). Revision received September 1, 2021; accepted for publication September 1, 2021.
• Prehosp Emerg Care. 2022 Nov 1; 26 (6): 801810801-810.

AbstractObjective: Linking emergency medical services (EMS) data to hospital outcomes is important for quality assurance and research initiatives. However, non-linkage due to missing or incomplete patient information may increase the risk of bias and distort findings. The purpose of this study was to explore if an optimization strategy, in addition to an existing linkage process, improved the linkage rate and reduced selection and information bias. Methods: 4,150 transported patients in a metropolitan EMS system in Alberta, Canada from 2016/17 were linked to two Emergency Department (ED) databases by a standard strategy using a unique health care number, date/time of ED arrival, and hospital name. An optimized strategy added additional linkage steps incorporating last name, year of birth, and a manual search. The strategies were compared to assess the rate of linkage, and to describe event and patient-level characteristics of unlinked records. Results: The standard strategy resulted in 3,650 out of 4,150 (88.0%) linked records (95% CI 86.9%-88.9%). Of the 500 non-linked records, an additional 381 were linked by the optimized strategy (n = 4,031/4,150 [97.1%; 95% CI: 96.6%-97.6%]). There were no false positive linkages. The highest linkage failure was in 25 to 34 year-old patients (n = 93/478, 19.5%), males (n = 236/1975, 12.0%), Echo level events (n = 15/77, 19.5%), and emergency transport (45/231, 19.5%). The optimized strategy improved linkage in these groups by 68.8% (64/93), 79.2% (187/236), 40.0% (6/15), and 51.1% (23/45) respectively. For dispatch card, the highest linkage failure occurred in Card 24-Pregnancy/Childbirth/Miscarriage (n = 30/44, 68.2%), Card 27-Stab/Gunshot/Penetrating Trauma (n = 6/17, 35.3%), and Card 9-Cardiac/Respiratory Arrest/Death (n = 12/46, 26.1%). The optimized strategy improved linkage by 10.0% (3/30), 83.3% (5/6), and 41.7% (5/12) respectively. For the 119 unlinked records, 71 (59.7%) had sufficient information for linkage, but no appropriately matching records could be found. Conclusion: An optimized sequential deterministic strategy linking EMS data to ED outcomes improved the linkage rate without increasing the number of false positive links, and reduced the potential for bias. Even with adequate information, some records were not linked to their ED visit. This study underscores the importance of understanding how data are linked to hospital outcomes in EMS research and the potential for bias.

16 keywords...

hide…