Aernoud Fiolet

319 Text-mining in electronic healthcare records can be used for screening and data collection Manual participant identification, as used in the LoDoCo2 (reference standard) Trial investigators of the LoDoCo2 trial used two steps to identify trial participants. First, manual screening was performed for eligibility using the EHR files prior to their outpatient clinic visit. Second, patients were interviewed face-to-face to verify eligibility and ask for participation. After providing informed consent, participation in the trial ensued. Baseline data extraction methods Automatic, using text-mining from EHRs A query was developed to automatically collect data form the EHRs on nineteen variables, which contained information about demography, medical history, procedure history, and drug use as reported in the baseline table of the trials’ methods paper (Supplement 1b). For the development of this query the same methods were employed as used for the participant identification query. Conventional data extraction, as used in the LoDoCo2 trial (reference standard) In the LoDoCo2 trial, data were collected manually during face-to-face baseline interviews at trial enrollment with participants. Interview data was first recorded as source data on-site and afterward entered into the trial’s EDC system. Analysis Participant identification efficiency For each site, the number of unique patient visits during the trial recruitment period, number of patients automatically identified as potentially eligible, and number of patients enrolled in the trial were recorded and compared to the number of patients enrolled in the actual trial. For both methods, a theoretical yield was calculated based on the patients needed to screen for identification. For determining the yield of the automatic participant identification, the number of enrolled trial participants was used as a proxy as it was not possible to assess how many of the automatically identified potentially eligible patients would have been enrolled retrospectively. Data collection accuracy Results of automated EHR text-mining were compared to manually collected trial data on their distributions and accuracy (defined as [true positive data points +