Assignment: Challenges Associated With Abstracting Normalizing And Reconciling Clinical Data From Multiple Disparate Sources

Assignment: Challenges Associated With Abstracting Normalizing And Reconciling Clinical Data From Multiple Disparate Sources

Assignment: Challenges Associated With Abstracting Normalizing And Reconciling Clinical Data From Multiple Disparate Sources

Data Processing

Data processing is the key to unlocking the value in data. By cleansing, transforming, and enriching a dataset, one can unlock insights that can help improve business performance. Data processing is a critical component of any data-driven strategy. By making sure that data is clean, accurate, and ready for analysis, one can unleash its power to help improve business performance. Data processing involves abstracting, normalizing, and reconciling data. Abstracting means reducing the data to its most basic form so that it can be processed more easily (Wang et al., 2019). Normalizing ensures that all the data is in a consistent format so that it can be accurately compared and analyzed. Reconciling resolves any discrepancies between the data sets. By performing these three tasks, data processing makes it easier to understand and use the data for decision-making purposes. The purpose of this assignment is to explore the challenges associated with abstracting, normalizing, and reconciling clinical data from multiple disparate sources.

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Abstracting, Normalizing, and Reconciling data

Abstracting data means to summarize it in a way that is meaningful and useful. Normalizing data means to make it consistent, so that all the values are within a certain range or fall into specific categories (Roman et al., 2018). Reconciling data means to compare two different sets of data and make sure they match up.

How Data are abstracted from Clinical Records

The process of data abstraction is the systematic process of extracting relevant information from clinical records for the purpose of research or performance improvement. It is a critical step in any research study, as well as in quality improvement initiatives. The abstraction process begins with a review of the record by a trained abstractor (Boehm et al., 2019). The abstractor looks for specific information that has been identified as being relevant to the study or initiative. This may include demographic information, medical history, diagnostic and treatment information, and outcome data. Once the relevant information has been identified, it is extracted and recorded in a standardized format.

Process of Normalizing Data

Normalizing data is the process of adjusting measurements so that all measurements are on the same scale. This is often done by calculating a standard deviation for each measure and then dividing each measure by the standard deviation (Boehm et al., 2019). Normalizing data can be useful when a data analyst want to compare or combine data from different sources, or when they want to compare data over time. It can also help data analysts to spot trends or patterns in the data.

Process of Reconciling Data

The process of reconciling data begins with the identification of discrepancies between two data sets. Once the discrepancies have been identified, the next step is to determine the root cause of the discrepancies. After the root cause has been identified, the affected data sets are corrected accordingly. Lastly, verification procedures are implemented to ensure that the corrected data sets are accurate and consistent with each other (Boehm et al., 2019). The process of reconciling data is the process of ensuring that the data in a system is accurate and consistent. This can be done manually or automatically. Manual reconciliation involves checking each entry in the system against the source data to ensure that they match. Automated reconciliation does the same thing, but uses algorithms to check for inconsistencies and correct them automatically. Both methods have their pros and cons, but automated reconciliation is usually faster and more accurate.

Challenges Associated With Using Data from Different Sources

The challenges associated with using data from different sources include the following: -Accuracy: The accuracy of the data may vary, depending on the source. -Completeness: The data may not be complete, depending on the source. -Timeliness: The data may not be up-to-date, depending on the source (Boehm et al., 2019). And -Consistency: The data may not be consistent across sources, which can lead to discrepancies and inaccuracies.

Conclusion

Data processing involves abstracting, normalizing, and reconciling data. This is done in order to make the data consistent and ready for analysis. Abstracting data means taking a complex set of data and reducing it to a simpler form. Normalizing data ensures that all the values in a dataset are within a certain range, while reconciling data helps to smooth out any discrepancies that may exist between two datasets. By performing these tasks, analysts can obtain accurate insights from their data more quickly and easily.

References

Boehm, M., Kumar, A., & Yang, J. (2019). Data management in machine learning systems. Synthesis Lectures on Data Management, 11(1), 1-173. https://doi.org/10.2200/S00895ED1V01Y201901DTM057

Roman, D., Nikolov, N., Putlier, A., Sukhobok, D., Elvesæter, B., Berre, A., … & Heath, T. (2018). DataGraft: One-stop-shop for open data management. Semantic Web, 9(4), 393-411. 10.3233/SW-170263

Wang, X., Williams, C., Liu, Z. H., & Croghan, J. (2019). Big data management challenges in health research—a literature review. Briefings in bioinformatics, 20(1), 156-167. https://doi.org/10.1093/bib/bbx086

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The purpose of this assignment is to explore the challenges associated with abstracting, normalizing, and reconciling clinical data from multiple disparate sources. In a 500-750-word paper, address the following:

Differentiate between abstracting, normalizing, and reconciling data.
How are data abstracted from clinical records?
Describe the process of normalizing data.
Describe the process of reconciling data.
What are the challenges associated with using data from different sources?
This assignment requires two or three scholarly sources.

Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

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