Importance of Advanced Techniques in Data Analytics

Importance of Advanced Techniques in Data Analytics

Data is widely used by the health care industry and the community at large to understand and anticipate trends in diseases and other health issues, treatments, and prevention. Therefore, it is imperative that the data provided to all parties is timely, accurate, and useful.

Write a 350- to 700-word paper in which you explain advanced techniques in data analytics. You will reference your Wk 4 – Using Data to Address Quality Measures assignment to complete this presentation.

Include the following in your paper:

Explain the importance of data quality to the health care facility referenced in your Week 4 assignment.
Explain how sampling and probability affect quality data on public reported sites, such as Hospital Compare.
Explain the importance of data mapping and scrubbing to the health care facility reference in your Week 4 assignment.
Identify 2 possible errors in data that could cause issues for the health care facility in your Week 4 assignment
Describe how the health care facility can ensure their data is clean.

Cite 2 peer-reviewed, scholarly, or similar references to support your assignment.
Importance of Advanced Techniques in Data Analytics

Importance of Advanced Techniques in Data Analytics

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Importance of Advanced Techniques in Data Analytics
Due to many technological advancements, operations of healthcare providers have also revolutionized. Some of the benefits of these advancements include understanding disease trends and their treatments, potential issues, and prevention. Although these benefits are paramount, not all data gets generated hence the need to ensure quality data for a better outcome. This paper discusses the advanced techniques in quality data analytics. It gives detailed importance of data quality to healthcare facilities and explains how sampling and probability impact data quality while providing the volume of data mapping and scrubbing. The paper also identifies two possible errors in data that may create issues in the healthcare facility and describes ways of ensuring the data is clean.
Importance of Data Quality
The benefits associated with data quality in healthcare organizations include accurate patient targeting. Precise information enables marketers to have the knowledge of their clients and keep their focus on them. Their data often suggest that potential clients come from current patients (Hickey et al., 2021). Data quality is also crucial for making informed decisions leading to minimal risks hence creating progress in the outcome. The relationship between the physician and patient improves through data quality since the physician holds better knowledge of the patient. Unlike poor data, data quality enables a more straightforward implementation of data, increasing the efficiency of service delivery in the entire organization (What is the importance of data quality in healthcare? 2021). Generally, data quality leads to healthcare organizations’ higher profitability by decreasing resource wastage.
How Sampling and Probability Affect Quality Data
Sampling and profitability is a technique where researchers select samples of a wider population through an approach incorporating the theory of profitability. Random selections get conducted through which a selectee is considered a profitability sample. The approach affects quality data because it reduces sampling bias leading to higher quality findings. It involves a diverse population representing the entire population from a different socioeconomic background (Ransom et al., 2021). through proven statistical approaches, the method creates an accurate sample for researchers hence data quality.
Importance of Data Mapping and Scrubbing
Data mapping and scrubbing is matching between a target and source with two databases of identical data elements named differently. In the healthcare system, the approach enables systems and other software to undergo a meaningful exchange of patient reimbursement, outcome recording, information, and other data (Christen, 2018). Mapping is essential, especially when transferring patient information from one setting to another, to inform care through policies (Data mapping. n.d.).
Possible Errors in Data
Some of the errors which may arise in data mapping include policy and clinical. During mapping data, there is a healthcare integrity issue at hand. There is no exception of any map being subject to validation and control through the updating process. In cases where data exchange gets done to determine its accuracy, the application of a map through software applications is involved in testing if there are external processes affecting data. If the map gives questionable results, the researcher must trace back the stream.
Ways of Ensuring Clean Data
Cleaning data involves correcting errors through data quality rules while addressing all quality areas. According to researchers, data cleaning best gets done through data quality examination or profiling. The approach involves organizations developing their own standard data cleaning method, which ensures they adhere to the reused and shared data rules. Another method of ensuring clean data is establishing criteria for the occasions causing cleaning efforts. Finally, healthcare organizations should publish their data corrections and make them available for downstream data repositories and stores, saving both costs and efforts.

Christen, P. (2018). Data scrubbing. Encyclopedia of Database Systems, 844-848.
Data mapping. (n.d.). HIM Body of Knowledge.
Hickey, D., Connor, R., McCormack, P., Kearney, P., Rosti, R., & Brennan, R. (2021). The data quality index: Improving data quality in Irish healthcare records. Proceedings of the 23rd International Conference on Enterprise Information Systems.
Ransom, K., Perfors, A., Hayes, B., & Connor Desai, S. (2021). What do our sampling assumptions affect: How we ENCODE data or how we reason from it?
What is the importance of data quality in healthcare? (2021, November 16). Folio3 Digital Health.

Importance of Advanced Techniques in Data Analytics

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