Minnesota Disability Determination Services, 121 7th Place E, Saint Paul, MN 55101, United States.
Minnesota Disability Determination Services, 121 7th Place E, Saint Paul, MN 55101, United States.
J Biomed Inform. 2018 Jun;82:41-46. doi: 10.1016/j.jbi.2018.04.012. Epub 2018 Apr 26.
Homeless patients face a variety of obstacles in pursuit of basic social services. Acknowledging this, the Social Security Administration directs employees to prioritize homeless patients and handle their disability claims with special care. However, under existing manual processes for identification of homelessness, many homeless patients never receive the special service to which they are entitled. In this paper, we explore address validation and automatic annotation of electronic health records to improve identification of homeless patients.
We developed a sample of claims containing medical records at the moment of arrival in a single office. Using address validation software, we reconciled patient addresses with public directories of homeless shelters, veterans' hospitals and clinics, and correctional facilities. Other tools annotated electronic health records. We trained random forests to identify homeless patients and validated each model with 10-fold cross validation.
For our finished model, the area under the receiver operating characteristic curve was 0.942. The random forest improved sensitivity from 0.067 to 0.879 but decreased positive predictive value to 0.382.
Presumed false positive classifications bore many characteristics of homelessness. Organizations could use these methods to prompt early collection of information necessary to avoid labor-intensive attempts to reestablish contact with homeless individuals. Annually, such methods could benefit tens of thousands of patients who are homeless, destitute, and in urgent need of assistance.
We were able to identify many more homeless patients through a combination of automatic address validation and natural language processing of unstructured electronic health records.
无家可归的患者在寻求基本社会服务时面临各种障碍。社会保障管理局认识到这一点，指导员工优先考虑无家可归的患者，并特别关注他们的残疾索赔。然而，在现有的无家可归身份识别手动流程下，许多无家可归的患者从未获得他们应得的特殊服务。在本文中，我们探讨了地址验证和电子健康记录的自动注释，以改善无家可归患者的识别。
我们开发了一个包含单个办公室就诊时医疗记录的索赔样本。使用地址验证软件，我们将患者地址与无家可归者收容所、退伍军人医院和诊所以及惩教设施的公共目录进行了协调。其他工具对电子健康记录进行了注释。我们使用随机森林来识别无家可归的患者，并使用 10 折交叉验证来验证每个模型。
对于我们完成的模型，接收器操作特征曲线下的面积为 0.942。随机森林将敏感性从 0.067提高到 0.879，但将阳性预测值降低到 0.382。
假定的假阳性分类具有许多无家可归的特征。组织可以使用这些方法来提示尽早收集必要的信息，以避免与无家可归者进行劳动密集型的重新联系尝试。每年，这些方法都可以使成千上万无家可归、贫困和急需帮助的患者受益。
我们能够通过自动地址验证和对非结构化电子健康记录的自然语言处理相结合，识别出更多的无家可归患者。
