Electronic Patient Records can be interfaced with medical decision support systems and quality of care assessment tools. An easy way of measuring the quality of EPR data is therefore essential. This study identified a number of global quality indicators (tracers) that could be easily calculated and validated them by correlating them with the Sensitivity and Positive Predictive Value (PPV) of data extracted from the EPR. Sensitivity and PPV of automatically extracted data were calculated using a gold standard constructed using answers to questions GPs were asked at the end of each contact with a patient. These properties were measured for extracted diagnoses, drug prescriptions, and certain parameters. Tracers were defined as drug-disease pairs (e.g. insulin-diabetes) with the assumption that if the patient is taking the drug, then the patient is suffering from the disease. Four tracers were identified that could be used for the ResoPrim primary care research database, which includes data from 43 practices, 10,307 patients, and 13,372 contacts. Moderately positive correlations were found between the 4 tracers and between the tracers and the sensitivity of automatically extracted diagnoses. For some purposes, these results may support the potential use of tracers for monitoring the quality of information systems such as EPRs.