本文由小咖机器人翻译整理

期刊来源：Ann Intern Med

原文链接：https://doi.org/10.7326/M23-2960

摘要内容如下：

背景

人们越来越关注医疗保健算法对种族和民族差异的潜在影响。

目的

检查关于卫生保健算法和相关缓解策略如何影响种族和民族差异的证据。

数据源

在多个数据库中搜索2011年1月1日至2023年9月30日期间发表的相关研究。

研究选择

使用预先定义的标准和双重审查，对研究进行筛选和选择，以确定:1）算法对健康和卫生保健结果中种族和民族差异的影响；2）在算法的开发、验证、传播和实施中减轻种族和民族偏见的策略或方法的影响。

数据提取

使用ROBINS-I（非随机干预研究中的偏倚风险）工具和改编的CARE-CPM（临床预测模型中种族和民族公平的关键评估）公平扩展，通过偏倚风险评估提取感兴趣的结果（即获得医疗保健的机会、护理质量和健康结果）。

数据综合

纳入63项研究（51项建模研究、4项回顾性研究、2项前瞻性研究、5项前后对照研究和1项随机对照试验）。发现算法的异质性证据：a）减少差异（例如，修订的肾脏分配系统），B）延续或加剧差异（例如，应用于重症护理资源分配的疾病严重程度评分），和/或C）对选择的结果（例如，心脏途径[病史、心电图、年龄、风险因素和肌钙蛋白]）没有统计学上的显著影响。为了减少差异，确定了7种策略：删除一个输入变量，替换一个变量，添加种族，添加一个非基于种族的变量，改变模型开发中使用的人口的种族和民族构成，为子群体创建单独的阈值，以及修改算法分析技术。

局限性

结果主要基于建模研究，并且可能高度特定于具体情况。

结论

算法可以减轻、延续和加剧种族和民族差异，无论是否明确使用种族和民族，但证据是异质的。算法的意向性和实现会影响对差异的影响，并且可能会对结果进行权衡。

主要资金来源

医疗质量和研究机构。

英文原文如下：

Abstracts

BACKGROUND  There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities.

PURPOSE  To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities.

DATA SOURCES  Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023.

STUDY SELECTION  Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms.

DATA EXTRACTION  Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension.

DATA SYNTHESIS  Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques.

LIMITATION  Results are mostly based on modeling studies and may be highly context-specific.

CONCLUSION  Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes.

PRIMARY FUNDING SOURCE  Agency for Healthcare Quality and Research.

-----------分割线---------

点击链接：https://www.mediecogroup.com/community/user/vip/categories/ ，成为医咖会员，获取12项专属权益。