Wk 5 – Summative Assessment: Importance of Advanced Techniques in Data Analytics

Posted: February 15th, 2023

Wk 5 – Summative Assessment: Importance of Advanced Techniques in Data Analytics
Exam Content
1.
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:
o Explain the importance of data quality to the health care facility referenced in your Week 4 assignment.
o Explain how sampling and probability affect quality data on public reported sites, such as Hospital Compare.
o Explain the importance of data mapping and scrubbing to the health care facility reference in your Week 4 assignment.
o Identify 2 possible errors in data that could cause issues for the health care facility in your Week 4 assignment
o Describe how the health care facility can ensure their data is clean.

Cite 2 peer-reviewed, scholarly, or similar references to support your assignment.

Format your assignment according to APA guidelines.

Statistics book – Davis, N., & Shiland, B. J. (2016). Statistics and data analytics for health data management. Saunders.
Statistics and Data Analytics for Health Data Management, Ch. 9
Read Ch. 9, “Scrubbing and Mapping Data.”
Statistics and Data Analytics for Health Data Management, Ch. 10
Read Ch. 10, “Predicting Data.”
University Library Resource
Advanced Techniques in Data Analysis
• Sukumar, S. R., Natarajan, R., & Ferrell, R. J. (2015). Quality of big data in health care. International Journal of Health Care Quality Assurance, 28(6), 621-634.
Note: We do our best to ensure these citations are correct within our system’s constraints. We suggest checking your citations to make sure they meet current APA standards.

Data quality is imperative for health care facilities to understand trends, provide effective treatments, and ensure patient safety. The health care facility referenced in my Week 4 assignment relies on accurate data to address quality measures and improve patient outcomes. Errors in data could negatively impact the facility’s performance on public reporting sites like Hospital Compare and undermine their efforts to enhance care.
Sampling and probability affect the quality of data reported on sites evaluating health care organizations. If a sample is not truly random or large enough, it may not accurately represent the entire population. For example, if a facility only sampled elective surgeries over a short period, the results would not capture issues that may arise during emergency procedures or over a full year. The sample must be carefully selected to eliminate bias and allow for generalizing conclusions to the greater patient population.
Data mapping and scrubbing are important techniques for the referenced health care facility to clean their records prior to analysis and reporting. Mapping involves standardizing data fields like gender, race, and diagnoses to uniform codes and definitions to facilitate comparisons over time and between facilities. Scrubbing identifies and addresses errors, such as missing or invalid values, duplicate records, and incorrect formatting. Both steps are necessary to transform raw clinical data into a consistent, analyzable format suitable for quality improvement and public reporting efforts.
Two possible errors that could impact the referenced health care facility are incorrect ICD diagnosis or procedure codes and duplicate patient records. Inaccurate coding could skew metrics like readmission and complication rates compared to national benchmarks. Duplicate records of the same patient would artificially inflate certain measures like total discharges or procedures performed. These issues could damage the facility’s reputation if detected on public reporting sites.
To ensure clean data, the health care facility should implement ongoing auditing and validation processes. Clinical documentation specialists could regularly review samples of records for coding accuracy. The IT department should monitor the master patient index for potential duplicates. Data quality reports highlighting errors, outliers, or missing values could be routinely generated to target improvement areas. Establishing these quality control measures will help the organization identify and address data issues before results are published or used for care improvement initiatives.
Advanced techniques in data analytics like sampling, mapping, scrubbing, and quality auditing (Sukumar, Natarajan, & Ferrell, 2015) are essential for health care facilities to provide accurate, meaningful data that supports quality improvement and informed decision making. Clean, reliable data is the foundation for utilizing analytics to truly understand outcomes and enhance patient care.