Developing data tools to reduce CVD disparities via Health Information Exchanges

开发数据工具，通过健康信息交换减少 CVD 差异

• 批准号: 9766361
• 负责人: JAMES ZOU
• 金额: $ 19.09万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-17 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766361
• 关键词: Accident and Emergency department; Address; Administrator; Blood Pressure; California; Cardiovascular Diseases; Cardiovascular Models; Case Management; Cessation of life; Clinic; Clinical; Code; Country; County; Data; Data Analyses; Data Set; Dyslipidemias; Electronic Health Record; Event; Funding; Goals; Gold; Guidelines; Health; Health Care Costs; Health Personnel; Health Surveys; Health care facility; Health system; Healthcare; Heart failure; Hospitalization; Hospitals; Housing; Human; Individual; Informatics; Information Systems; Insurance; Intervention; Laboratories; Literature; Low income; Machine Learning; Medical; Meta-Analysis; Methods; Modeling; Morbidity - disease rate; Myocardial Infarction; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Patients; Performance; Persons; Pharmacologic Substance; Pharmacy facility; Phase; Physicians; Population; Population Surveillance; Preventive therapy; Preventive treatment; Primary Health Care; Primary Prevention; Public Health; Race; Research; Risk; Risk Adjustment; Risk Assessment; Risk Factors; Sampling; Secondary Prevention; Secure; Social Work; Software Tools; Standardization; Stroke; Surveys; Techniques; Testing; Unemployment; United States; Validation; Vocabulary; Work; base; cardiovascular disorder prevention; cardiovascular disorder risk; cardiovascular disorder therapy; care providers; clinical diagnostics; cost; cost effective; data exchange; data resource; deep learning; demographics; disability; disorder prevention; health care service; health care service utilization; high risk; improved; innovation; learning strategy; medical specialties; mortality; novel; open source; payment; programs; prospective; residence; risk prediction model; safety net; semiparametric; service providers; social; social health determinants; socioeconomics; tool; treatment disparity; user-friendly; welfare

ABSTRACT CVD disparities across the race/ethnic and socioeconomic gradients are exacerbated by barriers to receiving guideline-based primary or secondary preventive treatment—such as appropriate blood pressure, dyslipidemia, and type 2 diabetes treatment.1–6 Most patients not receiving guideline-based treatment are actually insured and have seen a primary care provider in the past year.1 To reduce CVD disparities by better targeting disease prevention and treatment, healthcare administrators and county departments of public health have begun pooling data resources across healthcare and public health systems—such as across clinics, emergency rooms and hospitals, pharmacies, laboratories, and administrative datasets.7–9 The idea behind pooling such datasets is to better identify persons most in need, and direct targeted interventions to them. Solano County, California, a medium-sized, diverse, low-income county, has developed one of the first, and most comprehensive health information exchanges (HIEs), including: (i) electronic health record data from all emergency rooms, hospitals, primary care and specialty clinics and care facilities in the county; (ii) labs from all laboratory service providers; (iii) prescription details from all pharmacies; (iv) validated social determinants of health surveys administered in clinics; and (v) administrative datasets, including welfare, disability, housing, and geocoded neighborhood features. While several other counties are following suit to develop large, secure HIEs across healthcare and public health systems, a key challenge remains: how to cheaply, accurately, and rapidly analyze HIEs to identify which persons should be targeted for interventions. Without reliable, user- friendly, cost-effective, and generalizable data analysis programs, counties are unable to use the massive data at their disposal to address preventable causes of morbidity and mortality. The objective of this application is to apply our unique machine-learning innovations to develop open-source programs that can enable counties to identify persons at high risk for preventable CVD events and deaths. We will test the hypothesis that electronic health record data alone are insufficient to provide accurate risk prediction for preventable CVD events and deaths. Rather, we believe that key survey and administrative data providing information on social determinants of health will improve identification of high-risk patients. To test our hypothesis, we will develop and validate open-source, generalizable programs to: (Aim 1) rapidly identify persons in need of improved primary and secondary prevention of CVD by systematically comparing the performance of three alternative machine learning approaches to read HIE data, as compared to human clinician chart reviewers; and (Aim 2) perform multi-level risk assessment by automatically calibrating and validating models of CVD event risk, utilization and cost to HIE data, to identify the added value of administrative and social determinants data as compared to clinical or claims-based data alone. Our work will produce generalizable software tools for counties across the country to analyze HIE data and reduce preventable CVD disparities.

摘要 心血管疾病在种族/民族和社会经济梯度上的差异因接受治疗的障碍而加剧 基于指南的初级或二级预防性治疗-如适当的血压， 血脂异常和2型糖尿病治疗。1 -6大多数未接受基于指南的治疗的患者 实际投保，并在过去一年中看到了初级保健提供者。1为了减少心血管疾病的差异， 针对疾病预防和治疗，医疗保健管理人员和县公共卫生部门 已经开始在医疗保健和公共卫生系统之间汇集数据资源-例如在诊所之间， 急诊室、医院、药房、实验室和行政办公室。7 -9 汇集这些数据集是为了更好地确定最需要帮助的人，并针对他们采取有针对性的干预措施。 加州的索拉诺县是一个中等规模、多样化、低收入的县，是最早开发的县之一， 最全面的健康信息交换，包括：（i）所有人的电子健康记录数据 急诊室，医院，初级保健和专科诊所和保健设施在县;（二）实验室从所有 实验室服务提供者;（三）所有药房的处方细节;（四）经验证的社会决定因素 在诊所进行的健康调查;以及（v）行政数据集，包括福利、残疾、住房， 和地理编码的邻域特征。虽然其他几个县也在效仿， 在医疗保健和公共卫生系统的高收入企业中，一个关键的挑战仍然是：如何便宜，准确， 快速分析高收入家庭经济实体，以确定哪些人应作为干预的目标。如果没有可靠的，用户- 友好的，具有成本效益的，可推广的数据分析程序，县无法使用大量的数据 解决可预防的发病和死亡原因。本申请的目的是 应用我们独特的机器学习创新来开发开源项目，使各县能够 确定可预防心血管事件和死亡的高风险人群。我们将检验电子 健康记录数据本身不足以提供可预防CVD事件的准确风险预测， 死亡相反，我们认为提供社会信息的关键调查和行政数据 健康的决定因素将改善对高风险患者的识别。为了验证我们的假设，我们将开发 并验证开源，可推广的程序：（目标1）快速识别需要改善的人 通过系统地比较三种替代药物的性能， 与人类临床医生图表审查者相比，机器学习方法可以读取HIE数据;和（目标2） 通过自动校准和验证CVD事件风险模型来执行多级风险评估， 利用和成本HIE数据，以确定行政和社会决定因素数据的附加值， 与临床或基于索赔的数据相比。我们的工作将产生可推广的软件工具， 全国各县分析HIE数据，减少可预防的CVD差异。

项目成果

Video-based AI for beat-to-beat assessment of cardiac function.

• DOI: 10.1038/s41586-020-2145-8
• 发表时间: 2020-04
• 期刊: Nature
• 影响因子: 64.8
• 作者: Ouyang D;He B;Ghorbani A;Yuan N;Ebinger J;Langlotz CP;Heidenreich PA;Harrington RA;Liang DH;Ashley EA;Zou JY
• 通讯作者: Zou JY