Developing data tools to reduce CVD disparities via Health Information Exchanges

摘要 跨种族/族裔和社会经济梯度的心脑血管疾病差距因获得 以指导方针为基础的一级或二级预防治疗--如适当的血压， 血脂异常和2型糖尿病治疗1-6大多数没有接受指南治疗的患者是 实际参保，并在过去一年中看过初级保健提供者。1通过更好地减少心血管疾病差距 以疾病预防和治疗为目标，卫生行政人员和县公共卫生部门 已经开始跨医疗保健和公共卫生系统共享数据资源--例如跨诊所、 急诊室和医院、药房、实验室和行政数据集。7-9背后的想法 汇集这些数据集是为了更好地识别最需要帮助的人，并对他们进行有针对性的干预。 加利福尼亚州索拉诺县是一个中等规模、多元化、低收入的县，已经开发出了第一批、 最全面的健康信息交流(HIE)，包括：(1)来自 县内的急诊室、医院、初级保健和专科诊所以及护理设施；(2)来自所有国家的实验室 实验室服务提供者；(3)所有药房的处方详情；(4)有效的社会决定因素 在诊所进行的健康调查；和(V)行政数据集，包括福利、残疾、住房、 和地理编码的邻域特征。而其他几个县也在效仿，发展大型、安全的 在医疗保健和公共卫生系统中，一个关键的挑战仍然是：如何以低廉、准确和 快速分析HIE，以确定哪些人应该成为干预的目标。如果没有可靠的、用户- 友好、经济高效、可推广的数据分析程序，县无法使用海量数据 以解决可预防的致病和死亡原因。此应用程序的目标是 应用我们独特的机器学习创新来开发开源程序，使县能够 确定可预防的心血管疾病事件和死亡的高危人群。我们将检验这样的假设：电子化 仅有健康记录数据不足以为可预防的心血管事件提供准确的风险预测 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危患者的识别。为了检验我们的假设，我们将开发 并验证开源、可推广的程序以：(目标1)快速识别需要改进的人 系统比较三种方案对心血管疾病的一级和二级预防效果 与人类临床医生图表审查者相比，读取HIE数据的机器学习方法；以及(目标2) 通过自动校准和验证心血管事件风险模型来执行多级别风险评估， HIE数据的利用率和成本，以确定行政和社会决定因素数据的附加值 与仅基于临床或索赔的数据相比。我们的工作将产生通用化的软件工具 分析HIE数据，减少可预防的心血管疾病差距。

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.