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利用电子病历开发和验证算法估算因酒精导致的救护车出动负担。

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

机构信息

Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8QQ, UK.

Business Intelligence Department, Scottish Ambulance Service, Edinburgh EH12 9EB, UK.

出版信息

Int J Environ Res Public Health. 2021 Jun 11;18(12):6363. doi: 10.3390/ijerph18126363.

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.

摘要

背景

饮酒给急救服务带来了巨大负担,包括救护车服务,这些服务往往是患者与卫生服务机构的首次接触,有时甚至是唯一接触。我们旨在:(1)利用一种低成本且易于实施的算法来筛选电子病历表单中的自由文本,从而改进对苏格兰救护车服务负担的评估;(2)提供苏格兰因酒精导致的救护车呼叫负担的估计值。

方法

两名护理人员手动审查了 5416 份电子病历,对其是否与酒精有关做出专业判断,为评估我们的算法性能建立了黄金标准。他们还提取了与酒精有关的所有词语或短语。使用这些提取物开发了一种自动算法,以识别电子病历中的与酒精有关的呼叫。

结果

我们的算法在检测与酒精有关的呼叫方面具有 0.941 的特异性和 0.996 的敏感性。将该算法应用于 2019 年苏格兰所有的呼叫记录,我们确定了 86780 次(16.2%)与酒精有关。在周末,这一比例为 18.5%。

结论

与酒精有关的呼叫给苏格兰救护车服务带来了巨大负担。我们的算法比以前用于识别与酒精有关的救护车呼叫的方法更加敏感。这种方法和由此产生的数据有可能用于评估酒精政策干预措施,以及进行更广泛的流行病学研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5ca/8296189/e364048f34ac/ijerph-18-06363-g001.jpg

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