National Association of Chronic Disease Directors (NACDD), Decatur, Georgia (Dr Hohman); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (Drs Klompas and Cocoros); Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, Massachusetts (Dr Li); Louisiana Public Health Institute, New Orleans, Louisiana (Dr Carton); Division for Heart Disease and Stroke Prevention (Dr Jackson and Ms Wall) and Office of the Director (Dr Wiltz), National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia; and Washington State Department of Health, Tumwater, Washington (Dr Karras). Ms Martinez is an independent consultant to NACDD. Dr Kraus is an independent consultant to Public Health Informatics Institute, a program of the Task Force for Global Health.
J Public Health Manag Pract. 2023;29(2):162-173. doi: 10.1097/PHH.0000000000001693.
Electronic health record (EHR) data can potentially make chronic disease surveillance more timely, actionable, and sustainable. Although use of EHR data can address numerous limitations of traditional surveillance methods, timely surveillance data with broad population coverage require scalable systems. This report describes implementation, challenges, and lessons learned from the Multi-State EHR-Based Network for Disease Surveillance (MENDS) to help inform how others work with EHR data to develop distributed networks for surveillance.
Funded by the Centers for Disease Control and Prevention (CDC), MENDS is a data modernization demonstration project that aims to develop a timely national chronic disease sentinel surveillance system using EHR data. It facilitates partnerships between data contributors (health information exchanges, other data aggregators) and data users (state and local health departments). MENDS uses query and visualization software to track local emerging trends. The program also uses statistical and geospatial methods to generate prevalence estimates of chronic disease risk measures at the national and local levels. Resulting data products are designed to inform public health practice and improve the health of the population.
MENDS includes 5 partner sites that leverage EHR data from 91 health system and clinic partners and represents approximately 10 million patients across the United States. Key areas of implementation include governance, partnerships, technical infrastructure and support, chronic disease algorithms and validation, weighting and modeling, and workforce education for public health data users.
MENDS presents a scalable distributed network model for implementing national chronic disease surveillance that leverages EHR data. Priorities as MENDS matures include producing prevalence estimates at various geographic and subpopulation levels, developing enhanced data sharing and interoperability capacity using international data standards, scaling the network to improve coverage nationally and among underrepresented geographic areas and subpopulations, and expanding surveillance of additional chronic disease measures and social determinants of health.
电子健康记录 (EHR) 数据有可能使慢性病监测更加及时、可行和可持续。尽管 EHR 数据的使用可以解决传统监测方法的许多局限性，但具有广泛人口覆盖范围的及时监测数据需要可扩展的系统。本报告介绍了多州基于电子健康记录的疾病监测网络 (MENDS) 的实施情况、挑战和经验教训，以帮助其他机构了解如何利用电子健康记录数据开发分布式监测网络。
该项目由疾病控制与预防中心 (CDC) 资助，是一个数据现代化示范项目，旨在利用 EHR 数据开发及时的全国慢性病监测系统。它促进了数据贡献者（健康信息交换中心、其他数据聚合器）和数据使用者（州和地方卫生部门）之间的伙伴关系。MENDS 使用查询和可视化软件来跟踪当地新出现的趋势。该项目还使用统计和地理空间方法生成全国和地方一级慢性病风险措施的流行率估计。产生的数据产品旨在为公共卫生实践提供信息，并改善人口健康。
MENDS 包括 5 个合作伙伴站点，利用来自 91 个卫生系统和诊所合作伙伴的 EHR 数据，代表美国约 1000 万患者。实施的重点领域包括治理、伙伴关系、技术基础设施和支持、慢性病算法和验证、加权和建模，以及公共卫生数据使用者的劳动力教育。
MENDS 提出了一种利用 EHR 数据实施全国慢性病监测的可扩展分布式网络模型。随着 MENDS 的成熟，优先事项包括在各种地理和亚人群水平上生成流行率估计值，使用国际数据标准开发增强的数据共享和互操作性能力，扩大网络覆盖范围以提高全国和代表性不足的地理区域和亚人群的覆盖率，以及扩大对其他慢性病措施和健康的社会决定因素的监测。
