Date of Award

5-1-2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Information Science

Content Description

1 online resource (vi, 183 pages) : color illustrations

Dissertation/Thesis Chair

Luis Luna-Reyes

Committee Members

Archana Krishnan, William Kelly

Keywords

classification, high utilization, machine learning, Medicare, prediction, super utilization, Machine learning, Medical informatics, Medical care, Health planning

Subject Categories

Bioinformatics | Library and Information Science | Medicine and Health Sciences

Abstract

In this dissertation, I aim to forecast high utilizers of emergency care and inpatient Medicare services (i.e., healthcare visits). Through a literature review, I demonstrate that accurate and reliable prediction of these future high utilizers will not only reduce healthcare costs but will also improve the overall quality of healthcare for patients. By identifying this population at risk before manifestation, I propose that there is still time to reverse undesirable healthcare trajectories (i.e., individuals whose clinical risk increases an excessive healthcare and treatment burden) through timely attention and proper care coordination. My dissertation culminates in the delivery of state-of-the-art predictive models that exploit well-researched clinical, behavioral, and social determinants associated with increased inpatient and emergency care utilization. I discuss my contributions to applied machine learning in healthcare herein, and further examine ethical concerns common to similar machine learning tasks. Finally, I conclude by reviewing how this research can be advanced through future work.

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