Aernoud Fiolet

323 Text-mining in electronic healthcare records can be used for screening and data collection beta-blocker therapy differed between the participating medical centers, with hypertension ranging from 52.2% to 64.2%, antiplatelet therapy from 60.3% to 86.4% and beta blocker use ranging from 66.4% to 84.7%. DISCUSSION This study shows that it is feasible to use automated EHR text-mining to identify eligible trial participants and collect baseline data. By identifying eligible patients, only 20.1% of the original 92,466 visiting patients had to be screened manually for trial inclusion. In this 20.1%, 82.4%of the participantswere present. Data extracted from EHRs showed an average accuracy of 87.1% to the manually collected data of the LoDoCo2 trial. Several studies have investigated the opportunities of using EHRs for recruitment and data collection in clinical research and trials, but only a few compare EHR data to trial data. 17,18,19,20,21 In general, studies focusing on assessing EHR data quality showed mixed results. 10,22,23,24 Results from studies focusing on structured EHR data and text-mining in separate EHR components generally showed low yields for EHR quality data. 22,23,24 A study from 2013 assessed the completeness of structured EHR data to trial eligibility criteria originating from multiple trials, showing that 35% of the patient characteristics derived from the eligibility criteria were available in structured EHR data at the time. 23 In the same year, EHR medication lists were shown to have very broad accuracy (10–90%). 22 Studies automatically text-mining EHRs integrally, however, reported more favorable results with accuracies comparable to those found in this study. 10,24 In addition, registries based on routinely collected data have been reported to be of high value for trial recruitment and data collection. 25 Implications for using EHR data in clinical research When the quality of EHR data extraction is of an acceptable level, it could improve efficacy in trial conduct. As such, EHR data collection would allow the reallocation of resources and a reduction in execution costs. 7 Participant identification efficiency Using automated EHR text-mining, we were able to identify patients potentially eligible for trial participation. These results are in line with the results found by previous studies. 18,19,26 In participant recruitment, a high positive predictive value using automated EHR participant screening (i.e., most patients screened