Improving Population Health Using Electronic Health Records: Methods for Data Management and Epidemiological Analysis

Neal D. Goldstein

March 16, 2017 by CRC Press
Reference - 254 Pages
ISBN 9781498759106 - CAT# K27675

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Presents a cohesive look at performing research to improve population health from large scale data sources such as electronic health records

Gives practical, hands-on advice for conducting research from large data sources.

Studies design, analysis, and interpretation with an eye towards electronic health records

Presents the first research guide that considers all stakeholders.

Gives a step-by-step guide to conducting population health research using secondary data sources.


Electronic health records (EHRs) have become commonplace in the medical profession. Health data are readily captured and permanently stored in a digital fashion, and consequently, are increasingly being utilized in health research. The quality of this research depends upon the investigator’s ability to obtain the correct data to answer the correct question. It is easy to churn out poor quality research from the EHR; it is much harder to produce meaningful results that influence the population’s health.

Improving Population Health Using Electronic Health Records takes the reader through the process of conducting meaningful research from data in the EHR. It de-mystifies the entire research process, from how to ask the right kind of research questions, to obtaining data with particular emphasis on data management and manipulation, to performing a valid statistical analyses, and interpreting and presenting the results in a clear, concise fashion that has the potential to improve population health.

This book can be used as a hands-on how-to guide of performing research from EHR data in either a piece-meal fashion, selecting only the topics of greatest interest, or a complete guide to the entire research process.

Readers will benefit from the intuitive presentation of complex methods with a multitude of examples. It is invaluable reading for researchers and clinicians who are not otherwise familiar with the complexities of working with large data sets.


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