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 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.