Date of Award
Spring 2021
Degree Type
Dissertation
Degree Name
PhD Health Sciences
Department
Health and Medical Sciences
Advisor
Deborah DeLuca, J.D.
Committee Member
Glenn Beamer, Ph.D.
Committee Member
Genevieve Pinto-Zipp, Ed.D.
Keywords
health interventions, clinical studies, attrition, health technology, Bayesian Modeling, Cardiovascular Disease, Artificial Intelligence, Translational Medicine
Abstract
Background and Purpose: The high prevalence and mortality associated with Cardiovascular Diseases (CVD) is a burden on the United States healthcare system due to the millions of people that have and are at risk for CVD, causing clinical, practical, logistical, geographical, and financial difficulties associated with delivering medical care (Mensah & Brown, 2007). Digital Health Interventions (DHI) reduce CVD outcomes and improve CVD healthcare quality, but DHI clinical studies experience high rates of patient attrition (participant drop out), affecting the reliability of the collected data (Khanji et al., 2019; Santo & Redfern, 2020; Widmer et al., 2017). Achieving improvements in DHI patient attrition requires extensive evidence base on the risk and protective factors that contribute to patient attrition in DHI clinical studies. However, DHI literature presents special challenges. Through the development of a Bayesian Network Model, the purpose of the study was to begin to identify the relationships of the various factors associated with patient attrition in CVD DHI clinical studies.
Methods: A mixed-methods study was conducted and consisted of an examination of DHI literature, retrospective data analysis of a Cardiovascular Disease (CVD) digital health study, and expert elicitation to develop a Bayesian Network model.
Results: This study proposes a translational machine learning (ML) framework for exploring the relationships between various risk and protective factors of patient attrition in Digital Health Interventions (DHI) clinical studies and illustrates its application in medicine at the biological, intrapersonal, interpersonal, organizational and policy levels. These findings provide rationale and guidance for future strategies and interventions to facilitate full participation from sample groups and prevent patients/participants from ceasing use or dropping out of DHI studies.
Conclusion: The description and analysis of the risk and protective factors associated with patient attrition in DHI studies, and the relationships of those factors expands the evidence-base on Digital Health patient attrition, adherence, and engagement. Artificial Intelligence (AI) and Machine Learning are promising areas of development for medical research (Ramesh et al., 2004). This study serves as a framework for investigating complex healthcare phenomena utilizing AI and machine learning through an integrated translational approach.
Recommended Citation
Olaye, Iredia M., "Relationships Between Factors Associated with Patient Attrition in Digital Health Interventions Clinical Studies: An Integrated Translational AI Approach" (2021). Seton Hall University Dissertations and Theses (ETDs). 2863.
https://scholarship.shu.edu/dissertations/2863
Included in
Cardiovascular Diseases Commons, Health Information Technology Commons, Quality Improvement Commons, Translational Medical Research Commons
