ArticleEGEMS (Washington, DC)2017
Generalizability of Indicators from the New York City Macroscope Electronic Health Record Surveillance System to Systems Based on Other EHR Platforms.
Article in EGEMS (Washington, DC), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
- Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city.BMC medical research methodology · 2020Article
- A step closer to nationwide electronic health record-based chronic disease surveillance: characterizing asthma prevalence and emergency department utilization from 100 million patient records through a novel multisite collaboration.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Using the emergency department to investigate smoking in young adults.Annals of epidemiology · 2019Article
- Using Calibration to Reduce Measurement Error in Prevalence Estimates Based on Electronic Health Records.Preventing chronic disease · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
introductionThe New York City (NYC) Macroscope is an electronic health record (EHR) surveillance system based on a distributed network of primary care records from the Hub Population Health System. In a previous 3-part series published in
methodsWe abstracted primary care chart data from more than 20 EHR software systems for 142 participants of the 2013-14 NYC Health and Nutrition Examination Survey who did not contribute data to the NYC Macroscope. We then computed the sensitivity and specificity for indicators, comparing data abstracted from EHRs with survey data.
resultsObesity and diabetes indicators had moderate to high sensitivity (0.81-0.96) and high specificity (0.94-0.98). Smoking status and hypertension indicators had moderate sensitivity (0.78-0.90) and moderate to high specificity (0.88-0.98); sensitivity improved when the sample was restricted to records from providers who attested to Stage 1 Meaningful Use. Hyperlipidemia indicators had moderate sensitivity (≥0.72) and low specificity (≤0.59), with minimal changes when restricting to Stage 1 Meaningful Use. DISCUSSION: Indicators for obesity and diabetes used in the NYC Macroscope can be adapted to other EHR surveillance systems with minimal validation. However, additional validation of smoking status and hypertension indicators is recommended and further development of hyperlipidemia indicators is needed.
conclusionOur findings suggest that many of the EHR-based surveillance indicators developed and validated for the NYC Macroscope are generalizable for use in other EHR surveillance systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.