Estimating the Burden of Alcohol on Ambulance Callouts through Development and Validation of an Algorithm Using Electronic Patient Records

Francesco Manca, Jim Lewsey, Ryan Waterson, Sarah M. Kernaghan, David Fitzpatrick, Daniel Mackay, Colin Angus and Niamh Fitzgerald
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Francesco Manca: Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8QQ, UK
Jim Lewsey: Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8QQ, UK
Ryan Waterson: Business Intelligence Department, Scottish Ambulance Service, Edinburgh EH12 9EB, UK
Sarah M. Kernaghan: Business Intelligence Department, Scottish Ambulance Service, Edinburgh EH12 9EB, UK
David Fitzpatrick: Faculty of Health Sciences & Sport, University of Stirling, Stirling FK9 4LA, UK
Daniel Mackay: Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8QQ, UK
Colin Angus: School of Health and Related Research, University of Sheffield, Sheffield S10 2TN, UK
Niamh Fitzgerald: Faculty of Health Sciences & Sport, University of Stirling, Stirling FK9 4LA, UK

IJERPH, 2021, vol. 18, issue 12, 1-14

Abstract: Background: Alcohol consumption places a significant burden on emergency services, including ambulance services, which often represent patients’ first, and sometimes only, contact with health services. We aimed to (1) improve the assessment of this burden on ambulance services in Scotland using a low-cost and easy to implement algorithm to screen free-text in electronic patient record forms (ePRFs), and (2) present estimates on the burden of alcohol on ambulance callouts in Scotland. Methods: Two paramedics manually reviewed 5416 ePRFs to make a professional judgement of whether they were alcohol-related, establishing a gold standard for assessing our algorithm performance. They also extracted all words or phrases relating to alcohol. An automatic algorithm to identify alcohol-related callouts using free-text in EPRs was developed using these extracts. Results: Our algorithm had a specificity of 0.941 and a sensitivity of 0.996 in detecting alcohol-related callouts. Applying the algorithm to all callout records in Scotland in 2019, we identified 86,780 (16.2%) as alcohol-related. At weekends, this percentage was 18.5%. Conclusions: Alcohol-related callouts constitute a significant burden on the Scottish Ambulance Service. Our algorithm is significantly more sensitive than previous methods used to identify alcohol-related ambulance callouts. This approach and the resulting data have potential for the evaluation of alcohol policy interventions as well as for conducting wider epidemiological research.

Keywords: ambulance callouts; burden of alcohol; algorithm development; routine health records; paramedics; Scotland (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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