Week 6 Informatics Discussion: Dirty Data Essay

Week 6 Informatics Discussion: Dirty Data Essay

Week 6 Informatics Discussion: Dirty Data

Typically, healthcare organizations store protected health information in multiple databases. Valuable data is usually stored and shared via electronic health records (EHRs) and decision support systems, among other applications. It is crucial for all data to be accurate, complete, and correctly formatted to help improve care outcomes and enable healthcare providers to make informed decisions. Dirty data hampers such processes.


Dirty data does not stem from one thing; many factors combine, some more impacting than others. Data duplication is among the leading causes of dirty data (Turban et al., 2021). For instance, misspelling may cause data duplication, and healthcare systems fail to detect it depending on their parameters. The other potential cause of dirty data is incomplete data. The leading symptom of incomplete data is data where all the appropriate fields have not been filled (Turban et al., 2021). A typical scenario is when patients’ records omit critical preexisting conditions. Dirty data may also stem from inaccuracies. Transposed numbers and lack of updates as appropriate are potential causes of inaccuracies.

Dirty data perils are profoundly impacting. As a result, healthcare organizations must adopt practical measures for ensuring consistency of clean data. One of the most effective measures is data validation, where the accuracy and quality of data sources are thoroughly examined before use (Peng et al., 2020). Verification is also crucial before importing and processing data. The other practical intervention is to allocate time to check health records, particularly during non-busy schedules. Other strategies include conducting scheduled audits of records with protected information and archiving data no longer in use. For instance, data for dead and inactive patients should be archived.

Healthcare databases should be accurate, appropriately structured, and correctly formatted. Data with these critical elements facilitate accurate decision-making and enable healthcare providers to offer quality care. Dirty data is profoundly dangerous and should be avoided. As a result, data cleansing should be a continuous process.

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Peng, M., Lee, S., D’Souza, A. G., Doktorchik, C. T., &Quan, H. (2020). Development and validation of data quality rules in administrative health data using association rule mining. BMC Medical Informatics and Decision Making20(1), 1-10. https://doi.org/10.1186/s12911-020-1089-0

Turban, E., Pollard, C., & Wood, G. (2021). Information Technology for management: Driving digital transformation to increase local and global performance, growth and sustainability. John Wiley & Sons.


W6: Dirty Data
Healthcare technologies can provide an opportunity to improve the quality of the data but does not eliminate them. One of the most important steps in data analytics is to verify that data sources are accurate, in order to produce usable information. Data cleansing is used to identify and correct data discrepancies and inaccurate information – often referred to as “dirty data.” Discuss potential causes of dirty data and key strategies that can be used to ensure the consistency of clean data, while using various healthcare technologies.

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