{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:08:04Z","timestamp":1789027684970,"version":"build-2803163510"},"reference-count":0,"publisher":"Ubiquity Press, Ltd.","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["eGEMs"],"abstract":"Introduction:\u00a0Electronic health records (EHRs) have the potential to offer\n real-time, inexpensive standardized health data about chronic health conditions. Despite\n rapid expansion, EHR data evaluations for chronic disease surveillance have been\n limited. We present design and methods for the New York City (NYC) Macroscope, an\n EHR-based chronic disease surveillance system.This methods report is the first in a\n three part series describing the development and validation of the NYC Macroscope. This\n report describes in detail the infrastructure underlying the NYC Macroscope; indicator\n definitions; design decisions that were made to maximize data quality; characteristics\n of the population sampled; completeness of data collected; and lessons learned from\n doing this work. The second report describes the methods used to evaluate the validity\n and robustness of NYC Macroscope prevalence estimates; presents validation results for\n estimates of obesity, smoking, depression and influenza vaccination; and discusses the\n implications of our findings for NYC and for other jurisdictions embarking on similar\n work. The third report applies the same validation methods to metabolic outcomes,\n including the prevalence, treatment and control of diabetes, hypertension and\n hyperlipidemia.Methods:\u00a0We designed the NYC Macroscope for comparison to a local \u201cgold\n standard,\u201d the 2013-14 NYC Health and Nutrition Examination Survey, and the telephonic\n 2013 Community Health Survey. NYC Macroscope indicators covered prevalence, treatment,\n and control of diabetes, hypertension, and hyperlipidemia; and prevalence of influenza\n vaccination, obesity, depression and smoking. Indicators were stratified by age, sex,\n and neighborhood poverty, and weighted to the in-care NYC population and limited to\n primary care patients. Indicator queries were distributed to a virtual network of\n primary care practices; 392 practices and 716,076 adult patients were retained in the\n final sample.Findings:\u00a0The NYC Macroscope covered 10% of primary care providers and 15%\n of all adult patients in NYC in 2013 (8-47% of patients by neighborhood). Data\n completeness varied by domain from 98% for blood pressure among patients with\n hypertension to 33% for depression screening.Discussion:\u00a0Design and validation efforts\n undertaken by NYC are described here to provide one potential blueprint for leveraging\n EHRs for population health monitoring. To replicate a model like NYC Macroscope,\n jurisdictions should establish buy-in; build informatics capacity; use standard, simple\n case definitions; establish documentation quality thresholds; restrict to primary care\n providers; and weight the sample to a target population.<\/jats:p>","DOI":"10.13063\/2327-9214.1265","type":"journal-article","created":{"date-parts":[[2016,12,16]],"date-time":"2016-12-16T23:29:54Z","timestamp":1481930994000},"page":"26","source":"Crossref","is-referenced-by-count":17,"title":["Design of the New York City Macroscope: Innovations in Population Health\n Surveillance Using Electronic Health Records"],"prefix":"10.5334","volume":"4","author":[{"given":"Remle","family":"Newton-Dame","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katharine H.","family":"McVeigh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren","family":"Schreibstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharon","family":"Perlman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liz","family":"Lurie-Moroni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Jacobson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolyn","family":"Greene","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabeth","family":"Snell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorna E.","family":"Thorpe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3285","published-online":{"date-parts":[[2016,12,15]]},"container-title":["eGEMs (Generating Evidence & Methods to improve patient outcomes)"],"original-title":[],"link":[{"URL":"https:\/\/up-j-gemgem.ubiquityjournal.website\/articles\/149\/147","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/up-j-gemgem.ubiquityjournal.website\/articles\/149\/147","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T11:59:30Z","timestamp":1673956770000},"score":1,"resource":{"primary":{"URL":"https:\/\/up-j-gemgem.ubiquityjournal.website\/articles\/149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,15]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,5,15]]}},"URL":"https:\/\/doi.org\/10.13063\/2327-9214.1265","relation":{},"ISSN":["2327-9214"],"issn-type":[{"value":"2327-9214","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,15]]}}}