Abstract
Core measures are standard metrics to reflect the processes of care provided by hospitals. Hospitals in the United States are expected to extract data from electronic health records, automated computation of core measures, and electronic submission of the quality measures data. Traditional manual calculation processes are time intensive and susceptible to error. Automated calculation has the potential to provide timely, accurate information, which could guide quality-of-care decisions, but this vision has yet to be achieved. In this study, nursing informaticists and data analysts implemented a method to automatically extract data elements from electronic health records to calculate a core measure. We analyzed the sensitivity, specificity, and accuracy of core measure data elements extracted via SQL query and compared the results to manually extracted data elements. This method achieved excellent performance for the structured data elements but was less efficient for semistructured and unstructured elements. We analyzed challenges in automating the calculation of quality measures and proposed a rule-based (hybrid) approach for semistructured and unstructured data elements.