Design of the New York City Macroscope: Innovations in Population Health Surveillance Using Electronic Health Records.
Design of the New York City Macroscope: Innovations in Population Health Surveillance Using Electronic Health Records.
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DOI：
10.13063/2327-9214.1265
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Thorpe LE
中科院分区：
文献类型：
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作者：
Newton-Dame R;McVeigh KH;Schreibstein L;Perlman S;Lurie-Moroni E;Jacobson L;Greene C;Snell E;Thorpe LE
电子健康记录（EHR）有可能提供有关慢性健康状况的实时，廉价的标准化健康数据。尽管扩展迅速，但用于慢性病监测的EHR数据评估有限。我们目前的设计和方法为纽约市（NYC）宏观，EHR为基础的慢性病监测系统。本方法报告是描述NYC Macroscope的开发和验证的三部分系列中的第一部分。本报告详细介绍了《纽约公约》宏观框架的基础设施;指标定义;为最大限度地提高数据质量而作出的设计决定;抽样人口的特点;所收集数据的完整性;以及从这项工作中吸取的经验教训。第二份报告描述了用于评估NYC宏观患病率估计的有效性和稳健性的方法;提出了肥胖，吸烟，抑郁症和流感疫苗接种估计的验证结果;并讨论了我们的研究结果对NYC和其他开展类似工作的司法管辖区的影响。第三份报告将相同的验证方法应用于代谢结果，包括糖尿病、高血压和高脂血症的患病率、治疗和控制。我们设计了NYC Macroscope，以与当地的“黄金标准”进行比较，2013-14 NYC健康和营养检查调查，以及2013年社区健康调查。纽约市宏观指标涵盖糖尿病、高血压和高脂血症的患病率、治疗和控制;以及流感疫苗接种、肥胖、抑郁和吸烟的患病率。指标按年龄、性别和社区贫困进行分层，并加权到纽约市的护理人口，仅限于初级保健患者。指标查询被分发到初级保健实践的虚拟网络; 392个实践和716，076名成年患者被保留在最终样本中。2013年，NYC Macroscope覆盖了10%的初级保健提供者和15%的纽约市所有成年患者（按社区划分，8-47%的患者）。数据完整性因领域而异，从高血压患者的血压98%到抑郁症筛查的33%。纽约市进行的设计和验证工作在这里描述，以提供一个潜在的蓝图，利用电子健康档案的人口健康监测。为了复制像NYC Macroscope这样的模式，司法管辖区应该建立买进;建立信息能力;使用标准，简单的案例定义;建立文件质量阈值;限制初级保健提供者;并根据目标人群对样本进行加权。
Electronic health records (EHRs) have the potential to offer real-time, inexpensive standardized health data about chronic health conditions. Despite rapid expansion, EHR data evaluations for chronic disease surveillance have been limited. We present design and methods for the New York City (NYC) Macroscope, an EHR-based chronic disease surveillance system. This methods report is the first in a three part series describing the development and validation of the NYC Macroscope. This report describes in detail the infrastructure underlying the NYC Macroscope; indicator definitions; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. The second report describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia. We designed the NYC Macroscope for comparison to a local “gold standard,” the 2013–14 NYC Health and Nutrition Examination Survey, and the telephonic 2013 Community Health Survey. NYC Macroscope indicators covered prevalence, treatment, and control of diabetes, hypertension, and hyperlipidemia; and prevalence of influenza vaccination, obesity, depression and smoking. Indicators were stratified by age, sex, and neighborhood poverty, and weighted to the in-care NYC population and limited to primary care patients. Indicator queries were distributed to a virtual network of primary care practices; 392 practices and 716,076 adult patients were retained in the final sample. The NYC Macroscope covered 10% of primary care providers and 15% of all adult patients in NYC in 2013 (8–47% of patients by neighborhood). Data completeness varied by domain from 98% for blood pressure among patients with hypertension to 33% for depression screening. Design and validation efforts undertaken by NYC are described here to provide one potential blueprint for leveraging EHRs for population health monitoring. To replicate a model like NYC Macroscope, jurisdictions should establish buy-in; build informatics capacity; use standard, simple case defnitions; establish documentation quality thresholds; restrict to primary care providers; and weight the sample to a target population.