Abstract

Healthcare organizations (HCOs) currently have many information records about their patients. Yet, they cannot make proper, faster, and more thoughtful conclusions in many cases with their information. Much of the information is structured data such as medical records, historical data, and non-clinical information. This data is stored in a central repository called the Data Warehouse (DW). DW provides querying and reporting to different groups within the healthcare organization to support their future strategic initiatives. The generated reports create metrics to measure the organization's performance for post-action plans, not for real-time decisions. Additionally, healthcare organizations seek to benefit from the semi-structured and unstructured data by adopting emerging technology such as big data to aggregate all collected data from different sources obtained from Electronic Medical Record (EMR), scheduling, registration, billing systems, and wearable devices into one volume for better data analytic. For data completeness, big data is an essential element to improve healthcare systems. It is expected to revamp the outlook of the healthcare industry by reducing costs and improving quality. In this research, a framework is developed to utilize big data that interconnects all aspects of healthcare for real-time analytics and performance measurements. It is a comprehensive framework that integrates 41 integrated components in 6 layers: Organization, People, Process, Data, Technology, and Outcomes to ensure successful implementation. Each component in the framework and its linkage with other components are explained to show the coherency. Moreover, the research highlights how data completeness leads to better healthcare quality outcomes, and it is essential for healthcare organization survival. Additionally, the framework offers guidelines for selecting the appropriate technology with the flexibility of implementing the solution on a small or large scale, considering the benefits vs. investment. A case study has been used to validate the framework, and interviews with Subject Matter Experts (SMEs) have been conducted to provide another valuable perspective for a complete picture. The findings revealed that focusing only on big data technology could cause failing implementation without accomplishing the desired value of the data analytics outcomes. It is only applied for one-dimensional, not at the enterprise level. In addition, the framework proposes another 40 components that need to be considered for a successful implementation. Healthcare organizations can design the future of healthcare utilizing big data and analytics toward the fourth revolution in healthcare known as Healthcare 4.0 (H 4.0). This research is a contribution to this effort and a response to the needs.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.

Graduation Date

2021

Semester

Fall

Advisor

Rabelo, Luis

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0008873; DP0026152

Language

English

Release Date

December 2021

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Share

COinS