BACKGROUND: The results of many quality improvement (QI) projects are gaining wide-spread attention. Policy-makers, hospital leaders and clinicians make important decisions based on the assumption that QI project results are accurate. However, compared with clinical research, QI projects are typically conducted with substantially fewer resources, potentially impacting data quality. Our objective was to provide a primer on basic data quality control methods appropriate for QI efforts. METHODS: Data quality control methods should be applied throughout all phases of a QI project. In the design phase, project aims should guide data collection decisions, emphasizing quality (rather than quantity) of data and considering resource limitations. In the data collection phase, standardized data collection forms, comprehensive staff training and a well-designed database can help maximize the quality of the data. Clearly defined data elements, quality assurance reviews of both collection and entry and system-based controls reduce the likelihood of error. In the data management phase, missing data should be quickly identified and corrected with system-based controls to minimize the missing data. Finally, in the data analysis phase, appropriate statistical methods and sensitivity analysis aid in managing and understanding the effects of missing data and outliers, in addressing potential confounders and in conveying the precision of results. CONCLUSION: Data quality control is essential to ensure the integrity of results from QI projects. Feasible methods are available and important to help ensure that stakeholder's decisions are based on accurate data.
BACKGROUND: The results of many quality improvement (QI) projects are gaining wide-spread attention. Policy-makers, hospital leaders and clinicians make important decisions based on the assumption that QI project results are accurate. However, compared with clinical research, QI projects are typically conducted with substantially fewer resources, potentially impacting data quality. Our objective was to provide a primer on basic data quality control methods appropriate for QI efforts. METHODS: Data quality control methods should be applied throughout all phases of a QI project. In the design phase, project aims should guide data collection decisions, emphasizing quality (rather than quantity) of data and considering resource limitations. In the data collection phase, standardized data collection forms, comprehensive staff training and a well-designed database can help maximize the quality of the data. Clearly defined data elements, quality assurance reviews of both collection and entry and system-based controls reduce the likelihood of error. In the data management phase, missing data should be quickly identified and corrected with system-based controls to minimize the missing data. Finally, in the data analysis phase, appropriate statistical methods and sensitivity analysis aid in managing and understanding the effects of missing data and outliers, in addressing potential confounders and in conveying the precision of results. CONCLUSION: Data quality control is essential to ensure the integrity of results from QI projects. Feasible methods are available and important to help ensure that stakeholder's decisions are based on accurate data.
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