Document Type
Thesis - Open Access
Award Date
2019
Degree Name
Master of Science (MS)
Department / School
Mathematics and Statistics
First Advisor
Semhar Michael
Keywords
Finite mixture models, Multiple linear regression, Patient activation measure, Patient engagement
Abstract
Patient activation measure (PAM) is widely adopted by health care providers to access individual's knowledge, skill, and confidence for managing one's health and healthcare. Patient activation measure (PAM), licensed by Insignia Health, is widely adopted by health care providers to access individual's knowledge, skill, and confidence for managing one's health and healthcare. Multiple studies corroborate the effectiveness of activation measure in predicting most health behaviors, including preventive behaviors, healthy behaviors, self-management behaviors, and health information seeking. However, PAM is heavily dependent on subjective patient-reported data, which are often incomplete. The purpose of this study is to develop an objective statistical model to create a score derived from patient behavioral measurements. Ranging from 1 to 3, the score, which we named patient engagement score (PES), was derived entirely from three objective variables-number of immunization, number of missed scheduled visits, and rate of patient adherence to prescription refill using finite mixture model and EM algorithm. Finally, we performed simple and multiple linear regressions for the association between PES and each of the health-related outcomes.
Library of Congress Subject Headings
Patient self-monitoring.
Self-care, Health.
Mixture distributions (Probability theory)
Health behavior.
Format
application/pdf
Number of Pages
71
Publisher
South Dakota State University
Recommended Citation
Bae, Eric, "Development of a Data-driven Patient Engagement Score Using Finite Mixture Models" (2019). Electronic Theses and Dissertations. 3391.
https://openprairie.sdstate.edu/etd/3391
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Rights Statement
