Design of the New York City Macroscope: Innovations in Population Health Surveillance Using Electronic Health Records

Abstract

Introduction: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.Methods: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.Findings: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.Discussion: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.

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

“…Hub data are transformed into NYC Macroscope data through filtering and weighting 4. Filtering, which is intended to reduce double counting and improve data quality, limits records to primary care providers (internal medicine without a subspecialty, pediatrics, geriatrics, or family medicine) with at least 10 patients ages 20 years and older.…”

“…This report is the second in a three-part series describing the development and validation of the NYC Macroscope. The first report describes in detail the infrastructure underlying the NYC Macroscope, 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 4. This second report, which addresses concerns related to sampling bias and data quality, 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.…”

Monitoring Prevalence, Treatment, and Control Of Metabolic Conditions In New York City Adults Using 2013 Primary Care Electronic Health Records: A Surveillance Validation Study

“…NYC Macroscope data were extracted from structured EHR fields using ICD-9 diagnosis codes, vital signs, laboratory data, and medication information, and were developed to mirror widely used survey-based surveillance definitions 18–19…”

“…ICD-9 codes to identify a diagnosis of hypertension: ‘362.11’, ‘401.0’, ‘401.1’, ‘401.9’, ‘402.00’, ‘402.01’, ‘402.1’, ‘402.10’, ‘402.11’, ‘402.9’, ‘402.90’, ‘402.91’, ‘403’, ‘403.0’, ‘403.00’, ‘403.1’, ‘403.10’, ‘403.9’, ‘403.90’, ‘404’, ‘404.0’, ‘404.00’, ‘404.01’, ‘404.1’, ‘404.10’, ‘404.11’, ‘404.9’, ‘404.90’, ‘404.91’, ‘437.2’.bSee Newton-Dame et al 201619 for a complete list of medications used in each deinition.cDiagnoses assessed through end of 2013. ICD-9 codes to identify a diagnosis of hyperlipidemia: ‘272.0’, ‘272.1’, ‘272.2’, ‘272.3’, ‘272.4’, ‘272.7’, ‘272.8’, ‘272.9’…”

“…This report is the third in a three-part series describing the development and validation of the NYC Macroscope. The first report describes in detail the governance and technical infrastructure underlying the NYC Macroscope, design decisions made to maximize data quality, characteristics of the patient population sampled, completeness of data, and lessons learned in developing the system 19. The second report describes validation methods used to evaluate the validity and robustness of NYC Macroscope estimates and presents validation results for estimates of obesity, smoking, depression, and influenza vaccination 20.…”