PDF(Pubmed)
Abstract：
UNASSIGNED: Individuals from minoritized racial and ethnic backgrounds experience pernicious and pervasive health disparities that have emerged, in part, from clinician bias.
UNASSIGNED: We used a natural language processing approach to examine whether linguistic markers in electronic health record (EHR) notes differ based on the race and ethnicity of the patient. To validate this methodological approach, we also assessed the extent to which clinicians perceive linguistic markers to be indicative of bias.
UNASSIGNED: In this cross-sectional study, we extracted EHR notes for patients who were aged 18 years or older; had more than 5 years of diabetes diagnosis codes; and received care between 2006 and 2014 from family physicians, general internists, or endocrinologists practicing in an urban, academic network of clinics. The race and ethnicity of patients were defined as White non-Hispanic, Black non-Hispanic, or Hispanic or Latino. We hypothesized that Sentiment Analysis and Social Cognition Engine (SEANCE) components (ie, negative adjectives, positive adjectives, joy words, fear and disgust words, politics words, respect words, trust verbs, and well-being words) and mean word count would be indicators of bias if racial differences emerged. We performed linear mixed effects analyses to examine the relationship between the outcomes of interest (the SEANCE components and word count) and patient race and ethnicity, controlling for patient age. To validate this approach, we asked clinicians to indicate the extent to which they thought variation in the use of SEANCE language domains for different racial and ethnic groups was reflective of bias in EHR notes.
UNASSIGNED: We examined EHR notes (n=12,905) of Black non-Hispanic, White non-Hispanic, and Hispanic or Latino patients (n=1562), who were seen by 281 physicians. A total of 27 clinicians participated in the validation study. In terms of bias, participants rated negative adjectives as 8.63 (SD 2.06), fear and disgust words as 8.11 (SD 2.15), and positive adjectives as 7.93 (SD 2.46) on a scale of 1 to 10, with 10 being extremely indicative of bias. Notes for Black non-Hispanic patients contained significantly more negative adjectives (coefficient 0.07, SE 0.02) and significantly more fear and disgust words (coefficient 0.007, SE 0.002) than those for White non-Hispanic patients. The notes for Hispanic or Latino patients included significantly fewer positive adjectives (coefficient -0.02, SE 0.007), trust verbs (coefficient -0.009, SE 0.004), and joy words (coefficient -0.03, SE 0.01) than those for White non-Hispanic patients.
UNASSIGNED: This approach may enable physicians and researchers to identify and mitigate bias in medical interactions, with the goal of reducing health disparities stemming from bias.
UNASSIGNED: We used a natural language processing approach to examine whether linguistic markers in electronic health record (EHR) notes differ based on the race and ethnicity of the patient. To validate this methodological approach, we also assessed the extent to which clinicians perceive linguistic markers to be indicative of bias.
UNASSIGNED: In this cross-sectional study, we extracted EHR notes for patients who were aged 18 years or older; had more than 5 years of diabetes diagnosis codes; and received care between 2006 and 2014 from family physicians, general internists, or endocrinologists practicing in an urban, academic network of clinics. The race and ethnicity of patients were defined as White non-Hispanic, Black non-Hispanic, or Hispanic or Latino. We hypothesized that Sentiment Analysis and Social Cognition Engine (SEANCE) components (ie, negative adjectives, positive adjectives, joy words, fear and disgust words, politics words, respect words, trust verbs, and well-being words) and mean word count would be indicators of bias if racial differences emerged. We performed linear mixed effects analyses to examine the relationship between the outcomes of interest (the SEANCE components and word count) and patient race and ethnicity, controlling for patient age. To validate this approach, we asked clinicians to indicate the extent to which they thought variation in the use of SEANCE language domains for different racial and ethnic groups was reflective of bias in EHR notes.
UNASSIGNED: We examined EHR notes (n=12,905) of Black non-Hispanic, White non-Hispanic, and Hispanic or Latino patients (n=1562), who were seen by 281 physicians. A total of 27 clinicians participated in the validation study. In terms of bias, participants rated negative adjectives as 8.63 (SD 2.06), fear and disgust words as 8.11 (SD 2.15), and positive adjectives as 7.93 (SD 2.46) on a scale of 1 to 10, with 10 being extremely indicative of bias. Notes for Black non-Hispanic patients contained significantly more negative adjectives (coefficient 0.07, SE 0.02) and significantly more fear and disgust words (coefficient 0.007, SE 0.002) than those for White non-Hispanic patients. The notes for Hispanic or Latino patients included significantly fewer positive adjectives (coefficient -0.02, SE 0.007), trust verbs (coefficient -0.009, SE 0.004), and joy words (coefficient -0.03, SE 0.01) than those for White non-Hispanic patients.
UNASSIGNED: This approach may enable physicians and researchers to identify and mitigate bias in medical interactions, with the goal of reducing health disparities stemming from bias.
摘要：
■来自少数民族和种族背景的个人经历了已经出现的有害和普遍的健康差异，在某种程度上,来自临床医生的偏见。
我们使用自然语言处理方法来检查电子健康记录（EHR）注释中的语言标记是否因患者的种族和种族而异。为了验证这种方法论方法，我们还评估了临床医生认为语言标记指示偏倚的程度.
■在这项横断面研究中，我们提取了18岁或18岁以上的患者的EHR记录;有超过5年的糖尿病诊断代码;并在2006年至2014年期间接受了家庭医生的护理，一般内科医生，或者在城市里执业的内分泌学家，学术网络的诊所。患者的种族和种族被定义为白人非西班牙裔，黑人非西班牙裔,西班牙裔或拉丁裔.我们假设情感分析和社会认知引擎(SEANCE)组件(即，否定形容词，积极的形容词，喜悦的话，恐惧和厌恶的话，政治话语,尊重的话，信任动词,和幸福词），如果出现种族差异，平均字数将是偏见的指标。我们进行了线性混合效应分析，以检查感兴趣的结果（SEANCE组件和单词计数）与患者种族和种族之间的关系。控制患者年龄。为了验证这种方法，我们要求临床医生说明他们认为不同种族和族裔群体使用SEANCE语言领域的差异反映了EHR注释中的偏见的程度.
■我们检查了黑人非西班牙裔的EHR注释（n=12,905），白人非西班牙裔,和西班牙裔或拉丁裔患者（n=1562），有281名医生看过。共有27名临床医生参与了验证研究。就偏见而言，参与者将负面形容词评为8.63(SD2.06)，恐惧和厌恶词为8.11(SD2.15)，和积极的形容词为7.93（SD2.46）在1到10的范围内，其中10非常表明偏见。与白人非西班牙裔患者相比，黑人非西班牙裔患者的注释包含明显更多的阴性形容词（系数0.07，SE0.02）和明显更多的恐惧和厌恶词（系数0.007，SE0.002）。西班牙裔或拉丁裔患者的注释包括明显较少的阳性形容词（系数-0.02，SE0.007），信任动词(系数-0.009，SE0.004)，和喜悦词（系数-0.03，SE0.01）高于白人非西班牙裔患者。
■这种方法可能使医生和研究人员能够识别和减轻医疗互动中的偏见，以减少由偏见引起的健康差异为目标。
我们使用自然语言处理方法来检查电子健康记录（EHR）注释中的语言标记是否因患者的种族和种族而异。为了验证这种方法论方法，我们还评估了临床医生认为语言标记指示偏倚的程度.
■在这项横断面研究中，我们提取了18岁或18岁以上的患者的EHR记录;有超过5年的糖尿病诊断代码;并在2006年至2014年期间接受了家庭医生的护理，一般内科医生，或者在城市里执业的内分泌学家，学术网络的诊所。患者的种族和种族被定义为白人非西班牙裔，黑人非西班牙裔,西班牙裔或拉丁裔.我们假设情感分析和社会认知引擎(SEANCE)组件(即，否定形容词，积极的形容词，喜悦的话，恐惧和厌恶的话，政治话语,尊重的话，信任动词,和幸福词），如果出现种族差异，平均字数将是偏见的指标。我们进行了线性混合效应分析，以检查感兴趣的结果（SEANCE组件和单词计数）与患者种族和种族之间的关系。控制患者年龄。为了验证这种方法，我们要求临床医生说明他们认为不同种族和族裔群体使用SEANCE语言领域的差异反映了EHR注释中的偏见的程度.
■我们检查了黑人非西班牙裔的EHR注释（n=12,905），白人非西班牙裔,和西班牙裔或拉丁裔患者（n=1562），有281名医生看过。共有27名临床医生参与了验证研究。就偏见而言，参与者将负面形容词评为8.63(SD2.06)，恐惧和厌恶词为8.11(SD2.15)，和积极的形容词为7.93（SD2.46）在1到10的范围内，其中10非常表明偏见。与白人非西班牙裔患者相比，黑人非西班牙裔患者的注释包含明显更多的阴性形容词（系数0.07，SE0.02）和明显更多的恐惧和厌恶词（系数0.007，SE0.002）。西班牙裔或拉丁裔患者的注释包括明显较少的阳性形容词（系数-0.02，SE0.007），信任动词(系数-0.009，SE0.004)，和喜悦词（系数-0.03，SE0.01）高于白人非西班牙裔患者。
■这种方法可能使医生和研究人员能够识别和减轻医疗互动中的偏见，以减少由偏见引起的健康差异为目标。
