Computable social factor phenotyping using EHR and HIE data

大多数卫生系统都试图测量患者的社会风险因素，但这种数据收集通常充满了 操作和概念上的困难。多领域筛选问卷面临的可靠性，有效性， 工作流程挑战。区域一级的数据不是个别特征的有效替代。诊断代码为 利用不足。日常使用自然语言处理（NLP）从文本中提取社会因素， 超出了大多数组织的能力。因此，医疗机构需要更多的可执行性和有效性 衡量社会因素的方法。有了可执行和有效的办法，卫生系统将更加 有效解决与患者社会风险因素相关的负面成本、质量和健康结果。 本提案的目的是评估患者水平的可计算社会因素表型的有效性， 用于预测患者的医疗保健费用和利用率增加的风险。可计算的表型是COM- 通过单个数据元素或数据元素、观察结果或 事件由于这些表型来自现有的医疗保健操作和电子数据系统， 已经做好了广泛实施的准备我们的中心假设是， 现有的结构化人口统计、临床和业务运营数据将支持同样或更有效的推断， 患者的社会风险比其他测量方法。基于强有力的初步数据， 在该领域专家的指导下，我们将确定六个新的社会因素表型的有效性和实用性， 根据EHR和健康信息交换（HIE）中已收集的信息计算的类型， 以下目的：目的1，评估患者水平的可计算社会因素表型的并发有效性， 比较了计算表型、多领域问卷和NLP对Gold的同时有效性 两个卫生系统中社会因素的标准计量。目的2，评估患者水平的预测效度 可计算社会因素表型，将评估可计算表型的有效性，多领域问题- naires，自然语言处理，并在预测成本和利用率的组合方法。目标3，评估的可靠性（偏倚） 跨患者性别、种族、民族和年龄的患者水平可计算社会因素表型，评估 测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 研究方法，以确定和减轻潜在的偏见。该项目将导致更有效和可执行的 患者社会因素测量方法。这项研究意义重大，因为它直接影响到... 论述了组织在解决患者社会风险方面面临的挑战，并将为以下方面提供关键投入： 支持各组织努力建立一个学习型保健系统。这一建议具有创新性， 社会因素的心理测量和识别EHR和HIE数据的新用途。通过与多个和 不同的人群，我们解决社会经济弱势群体，少数民族， 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.