Ivy Zalaya K, Hwee Sharon, Kimball Brittany C, Evans Michael D, Marka Nicholas, Bendel Catherine, Boucher Alexander A
Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
J Racial Ethn Health Disparities. 2024 Aug 19. doi: 10.1007/s40615-024-02132-8.
Personal implicit biases may contribute to inequitable health outcomes, but the mechanisms of these effects are unclear at a system level. This study aimed to determine whether stigmatizing subjective terms in electronic medical records (EMR) reflect larger societal racial biases. A cross-sectional study was conducted using natural language processing software of all documentation where one or more predefined stigmatizing words were used between January 1, 2019 and June 30, 2021. EMR from emergency care and inpatient encounters in a metropolitan healthcare system were analyzed, focused on the presence or absence of race-based differences in word usage, either by specific terms or by groupings of negative or positive terms based on the common perceptions of the words. The persistence ("stickiness") of negative and/or positive characterizations in subsequent encounters for an individual was also evaluated. Final analyses included 12,238 encounters for 9135 patients, ranging from newborn to 104 years old. White (68%) vs Black/African American (17%) were the analyzed groups. Several negative terms (e.g., noncompliant, disrespectful, and curse words) were significantly more frequent in encounters with Black/African American patients. In contrast, positive terms (e.g., compliant, polite) were statistically more likely to be in White patients' documentation. Independent of race, negative characterizations were twice as likely to persist compared with positive ones in subsequent encounters. The use of stigmatizing language in documentation mirrors the same race-based inequities seen in medical outcomes and larger sociodemographic trends. This may contribute to observed healthcare outcome differences by disseminating one's implicit biases to unknown future healthcare providers.
个人的隐性偏见可能导致不公平的健康结果，但在系统层面上，这些影响的机制尚不清楚。本研究旨在确定电子病历（EMR）中带有污名化的主观词汇是否反映了更大范围的社会种族偏见。我们使用自然语言处理软件对2019年1月1日至2021年6月30日期间所有使用了一个或多个预定义污名化词汇的文档进行了横断面研究。对一个大都市医疗系统中急诊和住院病历的电子病历进行了分析，重点关注基于种族的词汇使用差异，无论是具体词汇，还是根据对这些词汇的普遍认知将负面或正面词汇进行分组。还评估了个体在后续就诊中负面和/或正面描述的持续性（“粘性”）。最终分析包括9135名患者的12238次就诊，年龄范围从新生儿到104岁。分析的群体为白人（68%）和黑人/非裔美国人（17%）。在与黑人/非裔美国人患者的就诊记录中，几个负面词汇（如不依从、无礼和脏话）出现的频率明显更高。相比之下，正面词汇（如依从、礼貌）在白人患者的文档中在统计学上更有可能出现。与种族无关，在后续就诊中，负面描述持续存在的可能性是正面描述的两倍。文档中使用污名化语言反映了在医疗结果和更大的社会人口趋势中看到的基于种族的相同不公平现象。这可能会通过将个人的隐性偏见传播给未来未知的医疗服务提供者，导致观察到的医疗结果差异。
