Objective Large clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals. Methods The GEMINI database contained 245 559 patient admissions at 7 hospitals in Ontario, Canada from 2010 to 2017. We performed 7 computational data quality checks and iteratively re-extracted data from hospitals to correct problems. Thereafter, GEMINI data were compared to data that were manually abstracted from the hospital’s electronic medical record for 23 419 selected data points on a sample of 7488 patients. Results Computational checks flagged 103 potential data quality issues, which were either corrected or documented to inform future analysis. For example, we identified the inclusion of canceled radiology tests, a time shift of transfusion data, and mistakenly processing the chemical symbol for sodium (“Na”) as a missing value. Manual validation identified 1 important data quality issue that was not detected by computational checks: transfusion dates and times at 1 site were unreliable. Apart from that single issue, across all data tables, GEMINI data had high overall accuracy (ranging from 98%–100%), sensitivity (95%–100%), specificity (99%–100%), positive predictive value (93%–100%), and negative predictive value (99%–100%) compared to the gold standard. Discussion and Conclusion Computational data quality checks with iterative re-extraction facilitated reliable data collection from hospitals but missed 1 critical quality issue. Combining computational and manual approaches may be optimal for assessing the quality of large multisite clinical databases.

BackgroundMechanical Turk (MTurk) is an online portal operated by Amazon where ‘requesters’ (individuals or businesses) can submit jobs for ‘workers.’ MTurk is used extensively by academics as a quick and cheap means of collecting questionnaire data, including information on alcohol consumption, from a diverse sample of participants. We tested the feasibility of recruiting for alcohol Internet intervention trials through MTurk.MethodsParticipants, 18 years or older, who drank at least weekly were recruited for four intervention trials (combined sample size, N = 11,107). The same basic recruitment strategy was employed for each trial – invite participants to complete a survey about alcohol consumption (less than 15 min in length, US$1.50 payment), identify eligible participants who drank in a hazardous fashion, invite those eligible to complete a follow-up survey ($10 payment), randomize participants to be sent or not sent information to access an online intervention for hazardous alcohol use. Procedures where put in place to optimize the chances that participants could only complete the baseline survey once.ResultsThere was a substantially slower rate of recruitment by the fourth trial compared to the earlier trials. Demographic characteristics also varied across trials (age, sex, employment and marital status). Patterns of alcohol consumption, while displaying some differences, did not appear to vary in a linear fashion between trials.ConclusionsIt is possible to recruit large (but not inexhaustible) numbers of people who drink in a hazardous fashion. Issues for online intervention research when employing this sample are discussed.

ObjectivesTo determine whether Amazon's Mechanical Turk (MTurk) might be a viable means of recruiting participants for online intervention research. This was accomplished by conducting a randomized controlled trial of a previously validated intervention with participants recruited through MTurk.MethodsParticipants were recruited to complete an online survey about their alcohol use through the MTurk platform. Those who met eligibility criterion for age and problem drinking were invited to complete a 3-month follow-up. Those who agreed were randomized to receive access to an online brief intervention for drinking or were assigned to a no intervention control group (i.e., thanked and told that they would be re-contacted in 3 months).ResultsA total of 423 participants were recruited, of which 85% were followed-up at 3-months. All participants were recruited in 3.2 h. Only 1/3 of participants asked to access the online brief intervention did so. Of the 4 outcome variables (number of drinks in a typical week, highest number on one occasion, number of consequences, AUDIT consumption subscale), one displayed a significant difference between conditions. Participants in the intervention group reported a greater reduction between on the AUDIT consumption subscale between baseline and 3-month follow-up compared to those in the no intervention control group (p = 0.004).ConclusionsDespite the current pilot showing only limited evidence of impact of the intervention among participants recruited through MTurk, there is potential for conducting trials employing this population (particularly if methods are employed to make sure that participants receive the intervention). This potential is important as it could allow for the rapid conduct of multiple trials during the development stages of online interventions.