VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah; Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah; Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
Am J Prev Med. 2020 Nov;59(5):755-763. doi: 10.1016/j.amepre.2020.05.026. Epub 2020 Oct 1.
Despite improvements in electronic medical record capability to collect data on sexual orientation, not all healthcare systems have adopted this practice. This can limit the usability of systemwide electronic medical record data for sexual minority research. One viable resource might be the documentation of sexual orientation within clinical notes. The authors developed an approach to identify sexual orientation documentation and subsequently derived a cohort of sexual minority patients using clinical notes from the Veterans Health Administration electronic medical record.
A hybrid natural language processing approach was developed and used to identify and categorize instances of terms and phrases related to sexual orientation in Veterans Health Administration clinical notes from 2000 to 2019. System performance was assessed with positive predictive value and sensitivity. Data were analyzed in 2019.
A total of 2,413,584 sexual minority terms/phrases were found within clinical notes, of which 439,039 (18%) were found to be related to patient sexual orientation with a positive predictive value of 85.9%. Documentation of sexual orientation was found for 115,312 patients. When compared with 2,262 patients with a record of administrative coding for homosexuality, the system found mentions of sexual orientation for 1,808 patients (79.9% sensitivity).
When systemwide structured data are unavailable or inconsistent, deriving a cohort of sexual minority patients in electronic medical records for research is possible and permits longitudinal analysis across multiple clinical domains. Although limitations and challenges to the approach were identified, this study makes an important step forward for the Veterans Health Administration sexual minority research, and the methodology can be applied in other healthcare organizations.
尽管电子病历在收集性取向数据方面有了改进，但并非所有医疗系统都采用了这种做法。这可能会限制系统范围内电子病历数据在研究性少数群体时的可用性。一个可行的资源可能是临床记录中关于性取向的文档。作者开发了一种方法来识别性取向文档，并随后使用退伍军人事务部电子病历中的临床记录得出了性少数群体患者队列。
开发了一种混合自然语言处理方法，用于识别和分类退伍军人事务部临床记录中 2000 年至 2019 年与性取向相关的术语和短语实例。使用阳性预测值和灵敏度评估系统性能。数据分析于 2019 年进行。
在临床记录中总共发现了 2,413,584 个性少数术语/短语，其中 439,039（18%）与患者的性取向有关，阳性预测值为 85.9%。为 115,312 名患者记录了性取向。与有同性恋行政编码记录的 2,262 名患者相比，该系统发现了 1,808 名患者（79.9%的敏感性）提到了性取向。
当系统范围的结构化数据不可用时或不一致时，在电子病历中为研究得出性少数群体患者队列是可行的，并允许在多个临床领域进行纵向分析。尽管该方法存在局限性和挑战，但这项研究为退伍军人事务部的性少数群体研究迈出了重要的一步，并且该方法可以应用于其他医疗机构。
