The use of routinely-collected healthcare data (also known as electronic health records) in randomised clinical trials offers the potential to deliver more efficient and cost-effective trials. However, it also presents challenges, with a very small proportion (~3%) of clinical trials estimated to be using this data. The Data-Enabled Trials area within the BHF Data Science Centre (BHF DSC) would like to make it easier for researchers and clinicians to safely and securely access electronic health records to support, or replace data that is collected just for a clinical trial. We want to facilitate this transformation in the way clinical trials can be done. The SCORE-CVD project aims to define community-agreed best practices for the derivation, format, and storage of phenotyping algorithms using Electronic Health Records (healthcare systems datasets [HSD]) for commonly used clinical trial outcome measures. Phenotyping algorithms are computable instructions that use the information contained within healthcare systems datasets to identify people with, or who have experienced, a specific clinical event/disease or characteristic. The SCORE-CVD project is led by members of BHF DSC, and delivered by working with a Steering Group and a number of outcome-specific ‘Task and Finish’ Groups focusing on the following areas: Phenotyping algorithm requirements Myocardial Infarction Stroke Major Bleeding Heart failure Death/Mortality This report covers findings from the initial round of workshops from all groups and details plans for the next steps for SCORE-CVD.