Data sources are categorized as primary or secondary data. Primary data is the direct description of an occurrence by a person who observed or experienced it. Sources of primary data include observations, experiments, surveys, interview, and questionnaires. On the other hand, secondary data includes any publication written by individuals who were not direct participants or observers in the described event (Gupta & Kaplan, 2020). Secondary data sources include books, scholarly journals, government documents, and periodicals. Secondary data is acquired when statistical methods are used on the primary data. However, data obtained from primary sources are more accurate and reliable than secondary sources.
Data collection is an essential aspect in making improvements and driving CQI. This is because lack of data means that there is no evidence to support that a problem really exists. Primary data obtained during a CQI project can be used to measure processes and outcomes, which are essential for evaluation. It provides answers on whether an identified issue is really a problem and why it needs improvement (Mendlowitz et al., 2020). Through primary data collection and analysis, needs are identified, and goals based on data are created. On the other hand, secondary data drives CQI by providing evidence on the best practices or interventions to employ in the CQI project (Gupta & Kaplan, 2020). Secondary data guides organizations and providers on the best approaches to achieve a particular desired outcome.
Primary data can be used to drive CQI by asking health providers who provide direct patient care, such as nurses and physicians, about the challenges they face in providing quality care. The information they give can be used to identify what the problem is and why it needs to be addressed. Secondary data from peer-reviewed sources can be used to establish the best approach to address the problem identified from the primary data (Gupta & Kaplan, 2020). It can guide an organization on the steps to improve the quality of care related to the problem.
References
Gupta, M., & Kaplan, H. C. (2020). Measurement for quality improvement: using data to drive change. Journal of Perinatology, 40(6), 962-971. http://doi.org/10.1038/s41372-019-0572-x
Mendlowitz, A., Croxford, R., MacLagan, L., Ritcey, G., & Isaranuwatchai, W. (2020). Usage of primary and administrative data to measure the economic impact of quality improvement projects. BMJ open quality, 9(2), e000712. http://dx.doi.org/10.1136/bmjoq-2019-000712
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