Addressing Bias in Electronic Health Record-based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise

Addressing Bias in Electronic Health Record-based Surveillance of Cardiovascular Disease Risk:... Purpose of review Use of the electronic health record (EHR) characterize CVD risk in populations. However, understand- for cardiovascular disease (CVD) surveillance is increasingly ing the biases that arise from EHR datasets is instrumental in common. However, these data can introduce systematic error planning epidemiological studies and interpreting study find- that influences the internal and external validity of study find- ings. Strategies to reduce the impact of bias in the context of ings. We reviewed recent literature on EHR-based studies of EHR data can increase the quality and utility of these data. CVD risk to summarize the most common types of bias that . . arise. Subsequently, we recommend strategies informed by Keywords Electronic health record Bias Cardiovascular . . work from others as well as our own to reduce the impact of disease Risk factors Epidemiology these biases in future research. Recent findings Systematic error, or bias, is a concern in all observational research including EHR-based studies of CVD Introduction risk surveillance. Patients captured in an EHR system may not be representative of the general population, due to issues such Cardiovascular disease (CVD) is the leading cause of morbid- as informed presence bias, perceptions about the healthcare ity http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Epidemiology Reports Springer Journals

Addressing Bias in Electronic Health Record-based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by Springer International Publishing AG
Subject
Medicine & Public Health; Epidemiology
eISSN
2196-2995
D.O.I.
10.1007/s40471-017-0130-z
Publisher site
See Article on Publisher Site

Abstract

Purpose of review Use of the electronic health record (EHR) characterize CVD risk in populations. However, understand- for cardiovascular disease (CVD) surveillance is increasingly ing the biases that arise from EHR datasets is instrumental in common. However, these data can introduce systematic error planning epidemiological studies and interpreting study find- that influences the internal and external validity of study find- ings. Strategies to reduce the impact of bias in the context of ings. We reviewed recent literature on EHR-based studies of EHR data can increase the quality and utility of these data. CVD risk to summarize the most common types of bias that . . arise. Subsequently, we recommend strategies informed by Keywords Electronic health record Bias Cardiovascular . . work from others as well as our own to reduce the impact of disease Risk factors Epidemiology these biases in future research. Recent findings Systematic error, or bias, is a concern in all observational research including EHR-based studies of CVD Introduction risk surveillance. Patients captured in an EHR system may not be representative of the general population, due to issues such Cardiovascular disease (CVD) is the leading cause of morbid- as informed presence bias, perceptions about the healthcare ity

Journal

Current Epidemiology ReportsSpringer Journals

Published: Nov 2, 2017

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