Background: With the increase of clinical trial costs during the last decades, the design of feasibility studies has become an essential process to reduce avoidable and costly protocol amendments. This design includes timelines, targeted sites and budget, together with a list of eligibility criteria that potential participants need to match. The present work was designed to assess the value of obtaining potential study participant counts using an automated patient count cohort system for large multi-country and multi-site trials: the Electronic Health Records for Clinical Research (EHR4CR) system. Methods: The evaluation focuses on the accuracy of the patient counts and the time invested to obtain these using the EHR4CR platform compared to the current questionnaire based process. This evaluation will assess the patient counts from ten clinical trials at two different sites. In order to assess the accuracy of the results, the numbers obtained following the two processes need to be compared to a baseline number, the “alloyed” gold standard, which was produced by a manual check of patient records. Results: The patient counts obtained using the EHR4CR system were in three evaluated trials more accurate than the ones obtained following the current process whereas in six other trials the current process counts were more accurate. In two of the trials both of the processes had counts within the gold standard’s confidence interval. In terms of efficiency the EHR4CR protocol feasibility system proved to save approximately seven calendar days in the process of obtaining patient counts compared to the current manual process. Conclusions: At the current stage, electronic health record data sources need to be enhanced with better structured data so that these can be re-used for research purposes. With this kind of data, systems such as the EHR4CR are able to provide accurate objective patient counts in a more efficient way than the current methods.Additional research using both structured and unstructured data search technology is needed to assess the value of unstructured data and to compare the amount of efforts needed for data preparation.