Development of a Collaborative Data Quality Improvement Approach for Healthcare Organizations
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Date
2018-01-01
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Department
Information Systems
Program
Information Systems
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Abstract
In the United States (US), the large volume of health data maintained by healthcare organizations holds tremendous potentials to support healthcare operations and decision making, which can improve health services, particularly for socioeconomically disadvantaged and under-served populations in an effective and efficient fashion. Unfortunately, for various reasons, there have been substantial problems with the quality of data maintained by healthcare organizations which reduces its usefulness to support day-to-day healthcare operations and serve decision making purposes. This research contributes to the body of knowledge by developing a novel collaborative approach to organizational data quality improvement. It has the following three interconnected aims: (i) identifying a taxonomy of data defects; (ii) identifying the challenges and opportunities for organizational data quality improvement and developing a software prototype which automates defect detection in big data sets and fosters communication among participating actors to correct data problems; (iii) implementing the approach as a pilot for four data quality improvement teams and continuously refining it through various assessments performed during the implementation. The research adopted qualitative methods to collect and analyze rich contextual data and various iterative software development activities resulting in a software prototype, which is a multi-user client-server solution. This prototype played a critical role in implementing and refining the novel data quality improvement approach. By doing so, this dissertations research developed a blueprint for data quality improvement initiatives in healthcare organizations, which can potentially benefit patients and their families through the utilization of high-quality health data in the future.