Evidence map›Paper›PMID 26667605›Full record

ReviewAnnual review of public health2016

Using Electronic Health Records for Population Health Research: A Review of Methods and Applications.

Joan A Casey, Brian S Schwartz, Walter F Stewart, Nancy E Adler

Abstract readReview
In one paragraph

Review in Annual review of public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 362 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
362citing papers in PubMed, 7 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

362 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  5. Learning competing risks across multiple hospitals: one-shot distributed algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024
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  9. Big data: from signal to action.Proceedings (Baylor University. Medical Center) · 2026
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302 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Joan A CaseyRobert Wood Johnson Foundation Health and Society Scholars Program at the University of California, San Francisco, and the University of California, Berkeley, Berkeley, California 94720-7360; joanacasey@berkeley.edu.
Brian S SchwartzDepartments of Environmental Health Sciences and Epidemiology, Bloomberg School of Public Health, and the Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21205; email: bschwar1@jhu.edu.
Walter F StewartResearch, Development and Dissemination, Sutter Health, Walnut Creek, California 94596; email: stewarwf@sutterhealth.org.
Nancy E AdlerCenter for Health and Community and the Department of Psychiatry, University of California, San Francisco, California 94118; email: nancy.adler@ucsf.edu.

Funding

The Impact of Unconventional Natural Gas Development on Maternal, Perinatal, and Childhood Health: an Electronic Health Record ApproachR00ES027023 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI CASEY, JOAN A · 2019 to 2021
$747k
The impact of unconventional natural gas development on maternal, perinatal, and childhood health: An electronic health record approachK99ES027023 · NIEHS · UNIVERSITY OF CALIFORNIA BERKELEY · PI CASEY, JOAN A · 2017 to 2018
$203k
NIEHS NIH HHS K99 ES027023NIEHS NIH HHS R00 ES027023
6 · The paper itself

Abstract

The use and functionality of electronic health records (EHRs) have increased rapidly in the past decade. Although the primary purpose of EHRs is clinical, researchers have used them to conduct epidemiologic investigations, ranging from cross-sectional studies within a given hospital to longitudinal studies on geographically distributed patients. Herein, we describe EHRs, examine their use in population health research, and compare them with traditional epidemiologic methods. We describe diverse research applications that benefit from the large sample sizes and generalizable patient populations afforded by EHRs. These have included reevaluation of prior findings, a range of diseases and subgroups, environmental and social epidemiology, stigmatized conditions, predictive modeling, and evaluation of natural experiments. Although studies using primary data collection methods may have more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. Future EHR epidemiology with enhanced collection of social/behavior measures, linkage with vital records, and integration of emerging technologies such as personal sensing could improve clinical care and population health.

Indexed as

Epidemiologic Research DesignModels, TheoreticalAge FactorsData CollectionDiagnostic Techniques and ProceduresElectronic Health RecordsEnvironmentHealth BehaviorHumansReproducibility of ResultsSex FactorsSocial EnvironmentSocioeconomic FactorsVital SignsEHRelectronic health recordsenvironmental epidemiologygeographic information systemshealth determinantssocial epidemiology

Identifiers

PMID26667605
PMCPMC6724703

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.