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The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide

IMPORTANCE: Research in obstetrics and gynecology (OB/GYN) increasingly relies on “big data” and observational study designs. There is a gap in practitioner-relevant guides to interpret and critique such research. OBJECTIVE: This guide is an introduction to interpreting research using observational...

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Autores principales: Goodin, Amie, Delcher, Chris, Valenzuela, Chelsea, Wang, Xi, Zhu, Yanmin, Roussos-Ross, Dikea, Brown, Joshua D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704657/
https://www.ncbi.nlm.nih.gov/pubmed/29164265
http://dx.doi.org/10.1097/OGX.0000000000000504
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author Goodin, Amie
Delcher, Chris
Valenzuela, Chelsea
Wang, Xi
Zhu, Yanmin
Roussos-Ross, Dikea
Brown, Joshua D.
author_facet Goodin, Amie
Delcher, Chris
Valenzuela, Chelsea
Wang, Xi
Zhu, Yanmin
Roussos-Ross, Dikea
Brown, Joshua D.
author_sort Goodin, Amie
collection PubMed
description IMPORTANCE: Research in obstetrics and gynecology (OB/GYN) increasingly relies on “big data” and observational study designs. There is a gap in practitioner-relevant guides to interpret and critique such research. OBJECTIVE: This guide is an introduction to interpreting research using observational data and provides explanations and context for related terminology. In addition, it serves as a guide for critiquing OB/GYN studies that use observational data by outlining how to assess common pitfalls of experimental and observational study designs. Lastly, the piece provides a compendium of observational data resources commonly used within OB/GYN research. EVIDENCE ACQUISITION: Review of literature was conducted for the collection of definitions and examples of terminology related to observational data research. Data resources were collected via Web search and researcher recommendations. Next, each data resource was reviewed and analyzed for content and accessibility. Contents of data resources were organized into summary tables and matched to relevant literature examples. RESULTS: We identified 26 observational data resources frequently used in secondary analysis for OB/GYN research. Cost, accessibility considerations for software/hardware capabilities, and contents of each data resource varied substantially. CONCLUSIONS AND RELEVANCE: Observational data sources can provide researchers with a variety of options in tackling their research questions related to OB/GYN practice, patient health outcomes, trends in utilization of medications/procedures, or prevalence estimates of disease states. Insurance claims data resources are useful for population-level prevalence estimates and utilization trends, whereas electronic health record–derived data and patient survey data may be more useful for exploring patient behaviors and trends in practice. TARGET AUDIENCE: Obstetricians and gynecologists, family physicians. LEARNING OBJECTIVES: After completing this activity, the learner should be better able to identify and define terminology used in observational data research; compare the features, strengths, and limitations of observational study designs and randomized controlled trials; distinguish between types of observational data (eg, insurance administrative claims, discharges, electronic health record databases, surveys, surveillance data) and weigh the strengths and limitations of research that uses each data type; interpret and critique OB/GYN research that uses observational data and secondary data analysis; and gain exposure and familiarity with a selection of observational data sets used to study topics relevant to obstetrical and gynecological practice and/or health outcomes.
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spelling pubmed-57046572017-12-11 The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide Goodin, Amie Delcher, Chris Valenzuela, Chelsea Wang, Xi Zhu, Yanmin Roussos-Ross, Dikea Brown, Joshua D. Obstet Gynecol Surv CME Articles IMPORTANCE: Research in obstetrics and gynecology (OB/GYN) increasingly relies on “big data” and observational study designs. There is a gap in practitioner-relevant guides to interpret and critique such research. OBJECTIVE: This guide is an introduction to interpreting research using observational data and provides explanations and context for related terminology. In addition, it serves as a guide for critiquing OB/GYN studies that use observational data by outlining how to assess common pitfalls of experimental and observational study designs. Lastly, the piece provides a compendium of observational data resources commonly used within OB/GYN research. EVIDENCE ACQUISITION: Review of literature was conducted for the collection of definitions and examples of terminology related to observational data research. Data resources were collected via Web search and researcher recommendations. Next, each data resource was reviewed and analyzed for content and accessibility. Contents of data resources were organized into summary tables and matched to relevant literature examples. RESULTS: We identified 26 observational data resources frequently used in secondary analysis for OB/GYN research. Cost, accessibility considerations for software/hardware capabilities, and contents of each data resource varied substantially. CONCLUSIONS AND RELEVANCE: Observational data sources can provide researchers with a variety of options in tackling their research questions related to OB/GYN practice, patient health outcomes, trends in utilization of medications/procedures, or prevalence estimates of disease states. Insurance claims data resources are useful for population-level prevalence estimates and utilization trends, whereas electronic health record–derived data and patient survey data may be more useful for exploring patient behaviors and trends in practice. TARGET AUDIENCE: Obstetricians and gynecologists, family physicians. LEARNING OBJECTIVES: After completing this activity, the learner should be better able to identify and define terminology used in observational data research; compare the features, strengths, and limitations of observational study designs and randomized controlled trials; distinguish between types of observational data (eg, insurance administrative claims, discharges, electronic health record databases, surveys, surveillance data) and weigh the strengths and limitations of research that uses each data type; interpret and critique OB/GYN research that uses observational data and secondary data analysis; and gain exposure and familiarity with a selection of observational data sets used to study topics relevant to obstetrical and gynecological practice and/or health outcomes. Lippincott Williams & Wilkins 2017-11 2017-11-15 /pmc/articles/PMC5704657/ /pubmed/29164265 http://dx.doi.org/10.1097/OGX.0000000000000504 Text en Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle CME Articles
Goodin, Amie
Delcher, Chris
Valenzuela, Chelsea
Wang, Xi
Zhu, Yanmin
Roussos-Ross, Dikea
Brown, Joshua D.
The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide
title The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide
title_full The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide
title_fullStr The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide
title_full_unstemmed The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide
title_short The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide
title_sort power and pitfalls of big data research in obstetrics and gynecology: a consumer's guide
topic CME Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704657/
https://www.ncbi.nlm.nih.gov/pubmed/29164265
http://dx.doi.org/10.1097/OGX.0000000000000504
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