title: Cloud Based Surveys to Assess Patient Perceptions of Health Care: 1000 Respondents in 3 days for US $300.
abstract: BACKGROUND
  There are many challenges in conducting surveys of study participants, including cost, time, and ability to obtain quality and reproducible work. Cloudsourcing (an arrangement where a cloud provider is paid to carry out services that could be provided in-house) has the potential to provide vastly larger, less expensive, and more generalizable survey pools.
  
  
  OBJECTIVE
  The objective of this study is to evaluate, using Amazon's Mechanical Turk (MTurk), a cloud-based workforce to assess patients' perspectives of health care.
  
  
  METHODS
  A national online survey posted to Amazon's MTurk consisted of 33 multiple choice and open-ended questions. Continuous attributes were compared using t tests.
  
  
  RESULTS
  We obtained 1084 responses for a total cost of US $298.10 in less than 3 days with 300 responses in under 6 hours. Of those, 44.74% (485/1084) were male and 54.80% (594/1084) female, representing 49 out of 50 states and aged 18 to 69 years.
  
  
  CONCLUSIONS
  Amazon's MTurk is a potentially useful survey method for attaining information regarding public opinions and/or knowledge with the distinct advantage of cost, speed, and a wide and relatively good representation of the general population, in a confidential setting for respondents.
authors.affiliation: Mount Sinai Medical Center, Department of Obstetrics Gynecology and Reproductive Science, Icahn School of Medicine, New York, NY, United States.
authors.forename: Jonah
authors.identifier: http://orcid.org/0000-0002-2041-0950
authors.initials: J
authors.lastname: Bardos
authors.forename: Jenna
authors.identifier: http://orcid.org/0000-0003-0122-093X
authors.initials: J
authors.lastname: Friedenthal
authors.forename: Jessica
authors.identifier: http://orcid.org/0000-0002-1716-2650
authors.initials: J
authors.lastname: Spiegelman
authors.forename: Zev
authors.identifier: http://orcid.org/0000-0001-8563-3250
authors.initials: Z
authors.lastname: Williams
keywords: MTurk; Mechanical Turk; cloud-based survey; crowdsourcing; health care perceptions; medical survey