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Background
The increasing use of routinely collected data in electronic health record (EHR) systems for business analytics, quality improvement and research requires an extraction process fit for purpose. Little is known about the quality of EHR data extracts. We examined the accuracy of three data extraction tools (DETs) with two EHR systems in Australia.
Methods
The hardware, software environment and extraction instructions were kept the same for the extraction of relevant demographic and clinical data for all active patients with diabetes. The counts of identified patients and their demographic and clinical information were compared by EHR and DET.
Results
The DETs identified different numbers of diabetics and measures of quality of care under the same conditions.
Discussion
Current DETs are not reliable and potentially unsafe. Proprietary EHRs and DETs must support transparency and independent testing with standardised queries. Quality control within an appropriate policy and legislative environment is essential.
Keywords
electronic health record; quality of health care; medical informatics
Current health reforms promote electronic health records (EHRs)1-3 to monitor the quality and safety of care4 and research.5 Practice-based clinical datasets are increasingly being extracted into data repositories to be mined for business analytics,6 research7 and quality improvement,8 making it possible to measure quality and health outcomes on a scale and at a speed not possible with manual records. However, such data analytics are limited by the quality of the data recorded, and EHRs may impose their own limitations.9 While data have been extracted from EHRs for two decades, we know little about the quality of the EHR data extracts or accuracy of the data extraction tools (DET) used.
Commercial DETs exist, but, like EhRs, they are largely proprietary 'black-box' solutions with intellectual property protection preventing adequate assessment of any design or execution errors or quality of data extracted. Effective assessment and management of data quality (DQ) requires analysis of the whole data cycle: from collection through extraction, cleansing, storage, management, dissemination, presentation and curation.10 Data quality management (DQM) processes and information governance (IG) structures are needed to ensure that data routinely captured within clinical practice is complete, correct, consistent11 and, ultimately, is fit for purpose.12
We examined whether different DETs achieved consistent results. Diabetes was used as the exemplar because it has a known...