Aernoud Fiolet

325 Text-mining in electronic healthcare records can be used for screening and data collection Future perspectives EHR data collection will probably be best used in conjunction with other data collection methods instead of replacing them. In the design of trials, investigators can take automated and manual EHR data collection into account in the design phase of the trial. Our results show that automated EHR screening for eligible patients might result in a somewhat different study population compared to the population currently enrolled. Effects on generalizability should be considered, although the resulting patient population might well reflect a more real-world sample of participants if their characteristics differ from the original study population. 18 Benefits of increased efficiency in the identification of eligible patients might make it easier to enroll patients, and as such, reach the desired number of inclusion faster than with conventional participant recruitment. Limitations of this study This study combined data from multiple medical centers, all using different EHR software vendors, and shows consistent results for the broad range of systems. Yet, three main limitations should be noted on it. First, the accuracy of the information on hypertension, antiplatelet therapy, and beta-blockers deviated, notably from collected trial data. Deviation between EHR and trial data was probably due to hypertension being defined as “using antihypertensive drugs” in the LoDoCo2 trial, which was hard to mirror in the EHR search query. Deviations on drug prescriptions and use variables were mainly attributed to registered timeframes of drugs and insufficient indexing of hospital drug prescription systems by the data extraction tool.Moreover, hospital physicians might not have registered home prescriptions for all patients, adequately deviating results on drugs too. Second, itwas assumed that all patients visiting the out-patient cardiology clinics of the three hospitals were screened conventionally for participation in the LoDoCo2 trial. If this was not the case expected yield of automated participant identification would be overestimated in this study. Third, our Boolean query was not enhanced with natural language processing algorithms because of the limitations of the employed data mining tool and language-specific limitations. Text-mining was, therefore, interpreted broadly as the ability to automatically extract information from unstructured texts.