初级和二级医疗之间的数字鸿沟:利用 SARS-CoV-2 住院病例进行分析。

IF 2.2 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES Health Informatics Journal Pub Date : 2024-07-01 DOI:10.1177/14604582241249929
Amit Sagi, Vipin Asopa, Benjamin Mitchell, Mahalingam Shiyamasundaran, Caleb Koch, Fanuelle Getachew, Irrum Afzal, David Sochart, Richard Field
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引用次数: 0

摘要

利用两个急诊科收治的 773 名 SARS-CoV-2 患者的数据,分析了全科医生护理记录摘要(GP-SCR)和急诊科(ED.)记录中的 ICD-10 编码差异,以及这是否与死亡率升高有关。死亡患者的 GP-SCR 和 ED 记录中 ICD-10 代码的平均数量高于存活患者(所有 p < .0001)。与急诊室入院文书工作中人工收集的数据相比,全科医生预先存在的数字数据能更好地预测死亡率。在急诊室记录中,高达 78.47% 的 GP-SCR 代码被遗漏,高达 45.49% 的急诊室记录代码不在 GP-SCR 中。遗漏的 ICD-10 编码中的一部分被确定为能够预测结果;随着遗漏编码比例的增加,死亡率也呈上升趋势。将 GP-SCR 提供给更广泛的医疗界的举措应能改善患者护理并减少基于机器学习算法开发过程中的偏差。
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The digital divide between primary and secondary care: An analysis using SARS-CoV-2 hospital admissions.

Using data from two ED. departments of 773 patients admitted with SARS-CoV-2, ICD-10 codes derived from the General Practitioner - Summary Care Record (GP-SCR) and Emergency Department (ED.) records were analysed for code discrepancies and whether this related to increased mortality. The average number of ICD-10 codes in both GP-SCR and ED. records was higher for patients who died than patients who survived (all p < .0001). Pre-existing GP digital data provides a better prediction of mortality than data collected manually during admission clerking in the ED. Up to 78.47% of GP-SCR codes were missed in the ED. records and up to 45.49% of the ED. record codes were not in the GP-SCR. A subset of missed ICD-10 codes were identified as being able to predict outcome; a trend towards increasing death rate as the proportion of missed codes increases. Initiatives to make the GP-SCR available to the wider healthcare community should improve patient care and reduce bias during development of machine learning based algorithms.

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来源期刊
Health Informatics Journal
Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS
CiteScore
7.80
自引率
6.70%
发文量
80
审稿时长
6 months
期刊介绍: Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
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