重复使用数据库造成的偏差:在医院为极度脆弱的病人编码

IF 3.4 3区 医学 Q1 HEALTH POLICY & SERVICES Health Policy and Technology Pub Date : 2024-06-01 DOI:10.1016/j.hlpt.2024.100851
Carine Milcent
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引用次数: 0

摘要

本文通过对法国电子病历 EMR 数据库的分析,探讨了编码过程中的异质性所造成的偏差。一般来说,研究人员和专业人士通常不仅会将数据用于其主要功能,还会将其用于多种其他目的。然而,他们对信息的编码方式可能与利用现有数据库的其他目的不一致。利用 EMR 急症护理数据库和 EMR 康复护理数据库,我们选取了 80 多万名在康复住院期间被编码为社会弱势群体的患者。我们进行了统计分析,以描述异质性的类型,并比较不同医院状态和个人社会脆弱性角色中脆弱性编码过程的分布情况。此外,还对编码过程率进行了分析。本文利用急症护理数据库和康复护理数据库,展示了社会脆弱性编码过程中的异质性。对于在康复住院期间被ICD-10编码为社会弱势人群的患者群体,其在急诊住院期间被编码为社会弱势人群的概率在公立医院比私立医院高出11.4%。EMR 系统的实施导致了编码过程的异质性。本文最后指出,编码过程中的异质性不是随机的,而是经过计算的。在流行病学研究或卫生经济学项目中应用这一数据库,如果将患者的脆弱性信息考虑在内,可能会导致意外的偏差结果。这些发现也可能对使用 EMR 的政策制定者在许多医疗机构中计划实施新的改革有用。
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Bias due to re-used databases: Coding in hospital for extremely vulnerable patients

Objective

This paper interrogates bias caused by heterogeneity in coding processes through an analysis of electronic medical records EMR databases in France. In general, researchers and professionals often apply data not only for its primary function but also for multiple alternative purposes. However, how they code information might be inconsistent with alternative purposes that exploit existing databases.

Methods

Using the EMR acute care and the EMR rehabilitation care databases, we select more than 800,000 patients coded as socially vulnerable during their rehabilitation stay. Statistical analysis was conducted to describe the types of heterogeneity and to compare the distribution of vulnerability coding processes across different hospital statuses and individual social vulnerability roles. Coding process rates were also analyzed.

Results

This paper shows the heterogeneity in this process of social vulnerability coding, exploiting acute care database and rehabilitation care database. For groups of patients with ICD-10 coded as socially vulnerable during their rehabilitation stays, the probability of being previously coded as such during their acute care stay is 11.4 % higher in the public sector than in the private one.

Conclusion

Implementing the EMR system leads to heterogeneity in the coding process. The paper concludes by arguing that heterogeneity in coding is not random but rather calculated. Applying this database in epidemiologic studies or health economics projects that factor in patients’ vulnerability information may lead to unintended biased results. These findings might also be useful for policymakers using EMR to plan for implementing new reforms in many healthcare settings.

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来源期刊
Health Policy and Technology
Health Policy and Technology Medicine-Health Policy
CiteScore
9.20
自引率
3.30%
发文量
78
审稿时长
88 days
期刊介绍: Health Policy and Technology (HPT), is the official journal of the Fellowship of Postgraduate Medicine (FPM), a cross-disciplinary journal, which focuses on past, present and future health policy and the role of technology in clinical and non-clinical national and international health environments. HPT provides a further excellent way for the FPM to continue to make important national and international contributions to development of policy and practice within medicine and related disciplines. The aim of HPT is to publish relevant, timely and accessible articles and commentaries to support policy-makers, health professionals, health technology providers, patient groups and academia interested in health policy and technology. Topics covered by HPT will include: - Health technology, including drug discovery, diagnostics, medicines, devices, therapeutic delivery and eHealth systems - Cross-national comparisons on health policy using evidence-based approaches - National studies on health policy to determine the outcomes of technology-driven initiatives - Cross-border eHealth including health tourism - The digital divide in mobility, access and affordability of healthcare - Health technology assessment (HTA) methods and tools for evaluating the effectiveness of clinical and non-clinical health technologies - Health and eHealth indicators and benchmarks (measure/metrics) for understanding the adoption and diffusion of health technologies - Health and eHealth models and frameworks to support policy-makers and other stakeholders in decision-making - Stakeholder engagement with health technologies (clinical and patient/citizen buy-in) - Regulation and health economics
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