Data Quality



Data quality is one of the determinants of ensuring that information system is appropriate. Low quality of data can influence the information confidence. It is essential to ensure high quality of data especially in the healthcare sector. Investigation reveals that poor data has a close relationship with research processes and inappropriate data analysis techniques. Timely, accessible and accurate information is essential in planning, development and delivery of healthcare services (Glass, 2010).

Data Quality Characteristics

The American Health Information Management Association explains the characteristics that indicate the quality of data. These characteristics provide a standard measure for assessing the quality of data. They include data accuracy, data accessibility, data comprehensiveness, and data consistency. In further explanation, these qualities can be divided into three categories. The first cluster of characteristics of data quality includes reliability, appropriateness, and validity, easy, sensitive and specific. The cluster explains that data quality must contain reliable data. Healthcare professionals and other stakeholders must rely on the data. The data must also be appropriate, valid, and easy to understand. The second category of characteristics describes data quality as inclusive of integrity, precision and timeliness. Data quality features an assessment of the integrity. Data integrity can be noted through the review of gap and outlier analysis, level of discrepancies in the data and orphaned units. The data should also be precise and objective. In addition, data quality can be seen through the data timeliness (Broeck, 2007). The third cluster of data quality characteristics includes correctness, completeness, consistency, comparison and the comprehensiveness of the data. Quality data should be correct and a true representation of facts. The data should be credible. Quality data also features completeness and comprehensiveness. Data must be complete and comprehensive in terms of depth, breadth, and range of use. The data must also be consistent and comparable to other similar data. There should not be an outlying fluctuation of similar investigations (Unite for Sight, 2013).

In my capacity as the health information manager, I would use DHIS tool (Data Health Information Systems) to optimize data quality and implement these characteristics. The tool will assist me to assess data quality characteristics through the use of visualization components such as Geographic information systems, pivot tables and charts. In addition, I will use visual scanning to check on data gaps, calculation discrepancies, inconsistencies, unusual fluctuations, duplication and wrong data entry (Voormolen, 2012). As a health information manager, I would also use the American Health Information Management Association data management model to ensure the data meets the characteristics. In addition, I will also apply the Medical Records Institute (MRI) principles of data quality to ensure efficient data management.


Finally, good quality of healthcare data is vital in the efficient operation of the healthcare sector organizations. Healthcare organizations hold essential data. It is of great importance to promote the quality of data because it affects patient care. Quality data can help in reduction of healthcare mistakes. It also ensures that healthcare practitioners have relevant information to make appropriate decisions. It is significant for healthcare organizations to take steps in improving the quality of their data. This can be through carrying out the data assessment.



Broeck, J. (2007). Maintaining data integrity in clinical trial. Journal of Clinical Trials, 572- 582.

Glass, J. (2010). Data quality importance to healthcare. Retrieved from data quality is critically important to healthcare organizations __5653.htm

United for Sight. (2013, June 19th). Quality health data. Retrieved from www.unite for Http://

Voormolen, R. (2012). The importance of data quality. Rosebank: Crowne Plaza.

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