Abstract. The quality of data derived from primary healthcare electronic systems has been subjected to little critical systematic analysis, especially in relation to the purported benefits and substantial investment in electronic information systems in primary care. Many indicators of quality of care are based on numbers of certain types of patients as denominators. Consistency of denominator data is vital for comparison of indicators over time and between services. This paper examines the consistency of denominator data extracted from electronic health records (EHRs) for monitoring of access and quality of primary health care. Data collection and analysis were conducted as part of a prospective mixed-methods formative evaluation of the Commonwealth Government’s Indigenous Chronic Disease Package. Twenty-six general practices and 14 Aboriginal Health Services (AHSs) located in all Australian States and Territories and in urban, regional and remote locations were purposively selected within geographically defined locations. Percentage change in reported number of regular patients in general practices ranged between –50% and 453% (average 37%). The corresponding figure for AHSs was 1% to 217% (average 31%). In approximately half of general practices and AHSs, the change was 20%. There were similarly large changes in reported numbers of patients with a diagnosis of diabetes or coronary heart disease (CHD), and Indigenous patients. Inconsistencies in reported numbers were due primarily to limited capability of staff in many general practices and AHSs to accurately enter, manage, and extract data from EHRs. The inconsistencies in data required for the calculation of many key indicators of access and quality of care places serious constraints on the meaningful use of data extracted from EHRs. There is a need for greater attention to quality of denominator data in order to realise the potential benefits of EHRs for patient care, service planning, improvement, and policy. We propose a quality improvement approach for enhancing data quality.