title: Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy.
abstract: BACKGROUND
  With the growing use of electronic medical records, electronic health records (EHRs), and personal health records (PHRs) for health care delivery, new opportunities have arisen for population health researchers. Our objective was to characterize PHR users and examine sample representativeness and nonresponse bias in a study of pregnant women recruited via the PHR.
  
  
  METHODS
  Demographic characteristics were examined for PHR users and nonusers. Enrolled study participants (responders, n=187) were then compared with nonresponders and a representative sample of the target population.
  
  
  RESULTS
  PHR patient portal users (34 percent of eligible persons) were older and more likely to be White, have private health insurance, and develop gestational diabetes than nonusers. Of eligible persons (all PHR users), 11 percent (187/1,713) completed a self-administered PHR based questionnaire. Participants in the research study were more likely to be non-Hispanic White (90 percent versus 79 percent) and married (85 percent versus 77 percent), and were less likely to be Non-Hispanic Black (3 percent versus 12 percent) or Hispanic (3 percent versus 6 percent). Responders and nonresponders were similar regarding age distribution, employment status, and health insurance status. Demographic characteristics were similar between responders and nonresponders.
  
  
  CONCLUSIONS
  Demographic characteristics of the study population differed from the general population, consistent with patterns seen in traditional population-based studies. The PHR may be an efficient method for recruiting and conducting observational research with additional benefits of efficiency and cost-cost-effectiveness.
authors.affiliation: Division of Epidemiology, The Ohio State University College of Public Health.
authors.forename: Julie K
authors.initials: JK
authors.lastname: Bower
authors.affiliation: Division of Environmental Health Sciences, The Ohio University College of Public Health.
authors.forename: Claire E
authors.initials: CE
authors.lastname: Bollinger
authors.affiliation: Division of Epidemiology, The Ohio State University College of Public Health.
authors.forename: Randi E
authors.initials: RE
authors.lastname: Foraker
authors.affiliation: Division of Environmental Health Sciences, The Ohio University College of Public Health.
authors.forename: Darryl B
authors.initials: DB
authors.lastname: Hood
authors.affiliation: Division of Biostatistics, The Ohio State University College of Public Health.
authors.forename: Abigail B
authors.initials: AB
authors.lastname: Shoben
authors.affiliation: Institute for Informatics, Washington University School of Medicine.
authors.forename: Albert M
authors.initials: AM
authors.lastname: Lai
keywords: Bias; Data Collection; Electronic Health Records; Epidemiology; Population Health