Computable social factor phenotyping using EHR and HIE data

大多数卫生系统试图衡量患者的社会风险因素，但这种数据收集通常令人担忧 有操作和概念上的困难。多领域筛选问卷面临信度、效度和 工作流程挑战。区域级别的数据不是单个特征的有效代理。诊断代码为 没有得到充分利用。使用自然语言处理(NLP)从文本中提取社会因素的日常使用是 超出了大多数组织的能力范围。因此，卫生保健组织需要更多的可实施性和有效性 衡量社会因素的方法。有了可执行和有效的方法，卫生系统将有更多 有效地解决与患者的社会风险因素相关的负面成本、质量和健康结果。 这项建议的目的是评估患者水平可计算的社会因素表型的有效性 用于预测患者的医疗成本和使用率增加的风险。可计算的表型是com- 通过单个数据元素或一组数据元素、观测值或 事件。由于这些表型源自现有的医疗保健业务和电子数据系统，它们 已经做好了广泛实施的准备。我们的中心假设是表型是从 现有的结构化人口统计、临床和业务运营数据将支持同等或更有效的推断- 与其他测量方法相比，这些方法更能反映患者的社会风险。以强劲的初步数据为基础， 在专家的指导下，我们将确定六个新的社会因素现象的有效性和有用性。 根据EHR和健康信息交换(HIE)内已收集的信息计算的类型，通过 目的1，评估患者水平可计算的社会因素表型的同时效度， 比较计算表型、多领域问卷和NLP与GOLD的同时效度 两个卫生系统中社会因素的标准衡量标准。目的2，评估患者水平的预测效度 可计算的社会因素表型，将评估可计算表型的有效性，多领域问题- NILES、NLP以及预测成本和利用率的组合方法。目标3，评估以下各项的可靠性(偏差) 患者水平可计算的社会因素表型跨越患者性别、种族、民族和年龄，评估 衡量方法在服务不足人群中的可重复性。我们将采用多种方法 识别和缓解潜在偏见的研究方法。这一项目将导致更有效和可实施的 患者社会因素测评的方法。这项拟议的研究具有重要意义，因为它直接预测了 解决组织在应对患者社会风险方面面临的挑战，并将提供关键投入 支持各组织努力实现学习型卫生系统。这一建议是创新的，它将 社会因素的心理测量学，并确定EHR和HIE数据的新用法。通过使用多个和 不同的人群，我们解决的是社会经济上处于不利地位的少数族裔的优先群体 人口和老年人。

Most health systems attempt to measure patients' social risk factors, but such data collection is typically fraught with operational and conceptual difficulties. Multi-domain screening questionnaires face reliability, validity, and workflow challenges. Area-level data are not valid proxies for individual characteristics. Diagnosis codes are underutilized. The day-to-day use of natural language processing (NLP) to extract social factors from text is beyond the capacity of most organizations. Thus, health care organizations need more implementable and valid approaches to measuring social factors. With implementable and valid approaches, health systems will more effectively address the negative cost, quality and health outcomes associated with patients' social risk factors. The objective of this proposal is to assess the validity of patient-level computable social factor phenotypes for use in predicting patients' risk of increased healthcare costs and utilization. Computable phenotypes are com- posites of characteristics defined through single data elements or a collection of data elements, observations or events. Because these phenotypes derive from existing healthcare operations and electronic data systems, they are well-positioned for widespread implementation. Our central hypothesis is that phenotypes computed from existing structured demographic, clinical, and business operations data will support equally or more valid infer- ences about patient social risks than other measurement approaches. Building upon strong preliminary data and direction from experts in the field, we will determine the validity and usefulness of six novel social factor pheno- types computed from already collected information within EHRs and health information exchanges (HIE) through the following aims: Aim 1, Assess the concurrent validity of patient-level computable social factor phenotypes, compares the concurrent validity of computed phenotypes, multi-domain questionnaires, and NLP against gold standard measures of social factors in two health systems. Aim 2, Assess the predictive validity of patient-level computable social factor phenotypes, will assess the validity of computable phenotypes, multi-domain question- naires, NLP, and combined approaches in predicting costs and utilization. Aim 3, Assess the reliability (bias) of patient-level computable social factor phenotypes across patient gender, race, ethnicity, and age, assesses the reproducibility of measurement approaches across underserved populations. We will employ a multi-method research approach to identify and mitigate potential bias. This project will lead to more valid and implementable approaches to patient social factor measurement. The proposed research is significant because it directly ad- dresses the challenges organizations face in addressing patients' social risks and will provide key inputs to support organizations efforts at achieving a learning health system. This proposal is innovative by advancing the psychometrics of social factors and identifying novel usages of EHR and HIE data. By working with multiple and diverse populations, we address the priority populations of socioeconomically disadvantaged, racial minority populations, and the elderly.