Abstract
Computerised Medical Record (CMR) data are widely used for secondary purposes such as service evaluation and epidemiological research. Data are increasingly aggregated from different medical facilities with various CMR vendors over time. It is increasingly difficult to manage the large quantity of data. Experiential learning in diabetes and chronic kidney disease (CKD) suggests simplistic processing can lead to errors. To maximise analytical ability for the Quality Improvement in CKD (QICKD) trial, we developed an agile data management process. By removing the need to import and process data in a relational data-base, we reduced processing and analysis time. We demonstrated usage of our new agile method to rapidly develop complex queries to identify how blood pressure varied between patients included or excluded from Quality and Outcomes Frameworks (QOF) pay-for-performance (P4P) targets in UK primary care. We describe a novel specification language that allows clinicians to focus on identifying variables to extract useful information from CMRs. Data for research questions were available in <1hour instead of longer times previously required through use of an SQL database.