Ford Camron D, Bodley Thomas, Betts Martin, Fowler Rob A, Gordon Alexis, James Michele, Rawal Shail, Reppas-Rindlisbacher Christina, Tam Paul, Tomlinson George, Yarnell Christopher J
Scarborough Health Network, Toronto, Canada.
Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
PLOS Digit Health. 2025 Sep 3;4(9):e0000999. doi: 10.1371/journal.pdig.0000999. eCollection 2025 Sep.
Accurate preferred language data is a prerequisite for providing high-quality care. We investigated the accuracy of preferred language data in the electronic health record (EHR) of a large community hospital network in Toronto, Canada. We conducted a point-prevalence audit of patients admitted to intensive care, internal medicine, and nephrology services at three hospitals. We asked each patient "What is your preferred language for health care communication?" and reported on agreement (with 95% confidence intervals [CI]) between interview-based and EHR-based preferred language. We used Bayesian multilevel logistic regression to analyze the association between patient factors and the accuracy of the EHR for patients who preferred a non-English language. Between June 17, 2024, and July 19, 2024, we interviewed 323 patients, of whom 124 (38%) preferred a non-English language. Median age was 77 years and 46% were female. EHR accuracy was 86% for all patients. The probability of the EHR correctly identifying a patient with non-English preferred language (sensitivity) was 69% (CI 60-77), specificity was 97% (CI 94-99), positive predictive value was 95% (CI 88-98), and negative predictive value was 83% (CI 79-87). There were 26 different non-English preferred languages, most commonly Cantonese (27%) and Tamil (14%). Accuracy was better for patients who were female or older, and varied by hospital and medical service. Mechanisms to improve accuracy for language preference data are needed to improve the validity of research studying preferred language, mitigate algorithmic bias, and overcome language-based inequities.
准确的首选语言数据是提供高质量医疗服务的前提条件。我们调查了加拿大多伦多一个大型社区医院网络电子健康记录（EHR）中首选语言数据的准确性。我们对三家医院重症监护、内科和肾病科收治的患者进行了时点患病率审计。我们询问每位患者“您在医疗沟通中首选的语言是什么？”，并报告基于访谈和基于EHR的首选语言之间的一致性（95%置信区间[CI]）。我们使用贝叶斯多层逻辑回归分析患者因素与首选非英语语言患者的EHR准确性之间的关联。在2024年6月17日至2024年7月19日期间，我们采访了323名患者，其中124名（38%）首选非英语语言。中位年龄为77岁，46%为女性。所有患者的EHR准确率为86%。EHR正确识别首选非英语语言患者的概率（敏感性）为69%（CI 60 - 77），特异性为97%（CI 94 - 99），阳性预测值为95%（CI 88 - 98），阴性预测值为83%（CI 79 - 87）。有26种不同的非英语首选语言，最常见的是粤语（27%）和泰米尔语（14%）。女性或年龄较大的患者准确率更高，且因医院和医疗服务而异。需要改进语言偏好数据准确性的机制，以提高研究首选语言的研究的有效性，减轻算法偏差，并克服基于语言的不平等。
