Mukherjee Neha, Lee Roy, Ngyuen Nhat, Bickell Nina, Richardson Lynne D, Ngai Ka Ming
Mount Sinai Health System/Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Icahn School of Medicine at Mount Sinai, Institute for Health Equity Research, New York, USA.
J Gen Intern Med. 2025 Jan 28. doi: 10.1007/s11606-025-09378-w.
Over 60 million patients in the USA have limited English proficiency (LEP) and experience barriers in care. Still, there exists no standardized method of monitoring the utilization of language interpreting services (LIS).
To introduce a methodological approach to systematically monitor utilization of LIS for LEP patients.
We utilized a One-To-Many Match algorithm to align inpatient visits of LEP patients from the electronic health record (EHR) with corresponding calls from LIS billing logs, using a unique patient identifier (MRN) and LIS call dates within patient's admit and discharge dates. Due to error when MRNs are recorded by LIS, the FuzzyWuzzy Probabilistic String-Matching technique was utilized to enhance match accuracy where exact matches were unattainable, addressing inherent complexities in language data matching.
The study involved 5823 inpatient encounters with a non-English preference in an urban hospital system in 2020, representing a linguistically diverse patient base, and attempted to match these against 183,655 LIS call logs.
Our approach successfully matched 83.1% (4389 out of 5823) of inpatient encounters to an LIS call.
We observed significant language-specific disparities in LIS usage, with Spanish leading in call volume at 2737 calls (exact matches) and 845 (probabilistic matches). Concordance rates varied, exceeding 94% for all languages in exact matches and ranging from 53.9% for Arabic to 71.6% for Russian in probabilistic matches. The average frequency of LIS calls was about one call per day per language group in the inpatient setting.
The study provides vital insights into language service preferences, frequency, and duration. These findings emphasize the need for standard methods in monitoring LIS usage to enhance patient outcomes for LEP patients.
美国有超过6000万患者英语水平有限(LEP),在就医过程中面临障碍。然而,目前尚无标准化方法来监测语言口译服务(LIS)的使用情况。
引入一种系统监测LEP患者LIS使用情况的方法。
我们使用一对多匹配算法,通过唯一的患者标识符(MRN)以及患者入院和出院日期内的LIS通话日期,将电子健康记录(EHR)中LEP患者的住院就诊信息与LIS计费日志中的相应通话进行匹配。由于LIS记录MRN时存在错误,因此在无法实现精确匹配的情况下,使用模糊概率字符串匹配技术来提高匹配准确性,以解决语言数据匹配中固有的复杂性。
该研究涉及2020年一家城市医院系统中5823次有非英语偏好的住院病例,代表了语言背景多样的患者群体,并尝试将这些病例与183,655条LIS通话记录进行匹配。
我们的方法成功将83.1%(5823例中的4389例)的住院病例与LIS通话进行了匹配。
我们观察到LIS使用情况存在显著的语言特异性差异,西班牙语的通话量最高,有2737次(精确匹配)和845次(概率匹配)。一致率各不相同,精确匹配中所有语言的一致率均超过94%,概率匹配中从阿拉伯语的53.9%到俄语的71.6%不等。住院环境中每个语言组LIS通话的平均频率约为每天一次。
该研究为语言服务偏好、频率和时长提供了重要见解。这些发现强调了需要采用标准方法来监测LIS的使用情况,以改善LEP患者的治疗效果。