There has been a steady rise in the use of clinical decision support (CDS) tools to guide nephrology as well as general clinical care. Through guidance set by federal agencies and concerns raised by clinical investigators, there has been an equal rise in understanding whether such tools exhibit algorithmic bias leading to unfairness. This has spurred the more fundamental question of whether sensitive variables such as race should be included in CDS tools. In order to properly answer this question, it is necessary to understand how algorithmic bias arises. We break down 3 sources of bias encountered when using electronic health record data to develop CDS tools: (1) use of proxy variables, (2) observability concerns and (3) underlying heterogeneity. We discuss how answering the question of whether to include sensitive variables like race often hinges more on qualitative considerations than on quantitative analysis, dependent on the function that the sensitive variable serves. Based on our experience with our own institution’s CDS governance group, we show how health system–based governance committees play a central role in guiding these difficult and important considerations. Ultimately, our goal is to foster a community practice of model development and governance teams that emphasizes consciousness about sensitive variables and prioritizes equity.

中文翻译：

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加强肾脏病学的临床决策支持：通过人工智能治理解决算法偏差


使用临床决策支持 （CDS） 工具指导肾脏病学和一般临床护理的情况稳步增加。通过联邦机构制定的指南和临床研究人员提出的担忧，对此类工具是否表现出导致不公平的算法偏见的理解也有所提高。这引发了一个更基本的问题，即是否应该将种族等敏感变量包含在 CDS 工具中。为了正确回答这个问题，有必要了解算法偏差是如何产生的。我们分解了使用电子健康记录数据开发 CDS 工具时遇到的 3 个偏差来源：（1） 使用代理变量，（2） 可观察性问题和 （3） 潜在的异质性。我们讨论了回答是否包括种族等敏感变量的问题通常更多地取决于定性考虑而不是定量分析，这取决于敏感变量所服务的功能。根据我们与自己机构的 CDS 治理小组合作的经验，我们展示了基于卫生系统的治理委员会如何在指导这些困难和重要的考虑因素方面发挥核心作用。最终，我们的目标是培养模型开发和治理团队的社区实践，强调对敏感变量的意识并优先考虑公平性。