Date of Award


Degree Name

Healthcare Administration


College of Business

Type of Degree


Document Type

Research Paper

First Advisor

Alberto Coustasse


Introduction: Poor quality in healthcare has resulted in avoidable patient complications, including readmission rates. Big data in healthcare can be analyzed and built into a tools, with machine learning, to aid in reduced readmission rates and overall positive patient outcomes.

Purpose of the Study: The intention of this study was to evaluate the ways that big data can be analyzed to improve healthcare, specifically readmissions, patient outcomes, and show cost savings. This study examined different ways that big data could be used in concordance with machine learning, including predictive analysis, to make these improvements.

Methodology: The hypothesis was the use of big data has improved quality categories in inpatient facilities led to improved patient outcomes, decreased readmission rates, and reduced costs. 27 publications were included and limited to the English language and were published between the year(s) of 2010 and 2023.

Results: The study displayed many solutions to different concerns within the scope of readmissions and patient outcomes through machine learning tools alongside big data. Examined were solutions for reducing readmission rates, and patient outcomes which further included addressing appointment no-shows, disease risks, and outcome outlook.

Discussion: The hypothesis of this study was partially conclusive, as improvement to readmission rates and patient outcomes were examined, but financial data was unavailable to confirm cost savings potential. The study limitations included the inability to obtain financial statistics and the potential for machine learning training data to not be clean. An interview with an expert in quality was also conducted and subject opinions were displayed alongside its corresponding subcategory.

Conclusion: The research provided descriptive and valuable data on the abilities of big data and machine learning techniques. Given the study limitations, there has been shown a need for research and data on the common use of these techniques within inpatient facilities.


Health services administration.

Health facilities - Business management.

Big data.