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Data Processing

Data Processing

Data Processing

QUESTION

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:

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. An abstract is not required.

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Data Processing

ANSWER
Data Processing
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Data Processing
Data processing involves abstracting, normalizing, and reconciling data. Data processing is a crucial tool in healthcare because it enables health care providers to achieve satisfactory clinical settings results(Richesson& Andrews, 2019). In evidence-based health-related settings, data processing gives clinicians a license to personalize patient treatments, make critical decisions towards advancing medical treatments, and improve communication between patients and medics. Medical data processing functions through various methods; this paper entails brief discussions of how the different methods work in clinical settings and their challenges.
Clinical Data Abstraction
Abstracting entails providing information that is essential about the data and concealing the rest of the details.In health care, medical records of patients abstracted are confidential and kept private. Medical data abstraction can either be done manually or through electronic extraction. Manual abstracting is done by checking medical data written down on paper and filling in the information to computers.
Healthcare systems have adopted and implemented electronic abstracting methods such as the electronic health method (EHR). According to Richesson& Andrews 2019: electronic health systems have improved health care settings by making it easier to access patients’ information. For instance, in a case where the medic wants to find out if a patient has been admitted, they use the patient’s code in the EHR. Health care professionals can access vital information about the patient without comprehending the system’s calculations.
Normalizing Data
Data normalization is done by placing data into tables, rows, and columns with relative patient information. The rows and columns have key identifiers regarding the patient’s medical record(Richesson& Andrews, 2019). The identifiers indicate information such as an individual’s name, date of birth, medical prescriptions, and other vital details. Normalization of data includes patient transactions, the number of patients admitted to the hospital, medical procedures, and tests. Normalize medical data guarantees that information is suitably put into storage and kept categorically.
Data Reconciliation
Reconciliation of data is a process through which nurses list down accurate patient medication data. It entails the name of the dosage prescribed, intake frequency followed by comparing it against the patient’s admission transfer charge from the doctors. The reconciliation process has various steps. First, nurses must have a list of medications that the patient is currently using and prescribed medication. The second step is to compare the two and then develop medical-based decisions to formulate a current list that the caregiver can use to attend to the patient(Richesson& Andrews, 2019).
The reconciliation process makes it easy for caregivers to handle patients during various medical transitions. Medical reconciliation is not only done by nurses and physicians but also by patients. Individuals can also help by carrying along with a list of their past or current medications. This information makes the reconciliation process more effective.
Challenges in Data Processing
The integration of data processing is defined as the method of using data from different sources. Challenges associated with data integration are based on the fact that data varies across different healthcare settings. Regardless of the use of (EHR), hospitals have recorded cases of inaccurate data collection. Some medical institutions may lack the infrastructure needed for data processing, thus depending on manual methods, which are equally tiresome to the health care providers. In normalizing data, nurses have encountered difficulty handling big data that cannot be stored in the electronic equipment. This slows down the medical systems.

References
Richesson, R. L., & Andrews, J. E. (2019). Clinical research informatics. Springer.

Hemingway H, Asselbergs F W, Danesh J, Dobson R, Maniadakis N, Maggioni A et al.Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J. 2018;39(16):1481–95.

Sylvestre, E., Bouzillé, G., Chazard, E., His-Mahier, C., Riou, C., & Cuggia, M. (2018). Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records. BMC Medical Informatics and Decision Making, 18(1). https://doi.org/10.1186/s12911-018-0586-x

Data Processing

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