Quality Improvement to Assess and Audit Complexity in Translational Research
Open Access
- Author:
- Munoz Soto, David Andres
- Graduate Program:
- Industrial Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- April 20, 2015
- Committee Members:
- Harriet Black Nembhard, Dissertation Advisor/Co-Advisor
Harriet Black Nembhard, Committee Chair/Co-Chair
Paul Griffin, Committee Member
Ling Rothrock, Committee Member
Conrad S Tucker, Committee Member
Deirdre Mccaughey, Committee Member - Keywords:
- quality improvement
translational research
analytic hierarchy process
goal programming
social network analysis - Abstract:
- The large gap between proven clinical knowledge and its implementation in clinical practice is a pressing challenge faced by the health community. It has been estimated that adults in the U.S. receive only about half of their recommended care. This is in part, due to the complexities and current inability of translating knowledge to effectively impact health outcomes. Moreover, the lack of understanding of the complexities involved in translational research have resulted in a poor allocation of resources. As an effort to accelerate the rate at which new discoveries become clinical practice, the National Institutes of Health (NIH) explicitly made translational research a central priority and has invested heavily in developing an infrastructure through the Clinical and Translational Science Awards (CTSAs). The arc of this dissertation is in alignment with this priority. Translational research experts have argued that the existing models in translational research have not been able to fully capture the complexities, dynamisms, and fragmentations of this long process. In response, data-driven tools and robust frameworks are expected to help analyzing, and hence, accelerating this knowledge translation. These frameworks are needed for assuring an efficient and effective decision-making process that support the tactical and strategic allocation of healthcare resources. Although Quality Improvement (QI) approaches have been found to be promising to solve a wide variety of problems in healthcare, their implementation in translational research has not been fully explored. Moreover, in healthcare fields, QI has been mostly associated with Lean and Six Sigma techniques. However, in order for QI techniques to address translational research challenges, a wider QI scope is needed. In response to these challenges, a comprehensive QI research approach is used in this dissertation to provide frameworks that inform healthcare decision makers, and hence, have a positive impact on translational research. The frameworks presented are applied to different case studies that use them to generate evidence for professional applications. The main body of this dissertation is divided into three parts. The first part proposes a combined Quality Function Deployment (QFD) and Analytic Hierarchy Process (AHP) framework for assessing the complexity of translational research. Specifically, this framework is used to identify and quantify the importance of the different operational steps and corresponding technical requirements along the translational research process. This framework was applied to a case study of a primary care-based weight control intervention. The second part proposes a Social Network Analysis (SNA) approach for evaluating collaboration and multidisciplinarity networks. The evaluation includes the identification of collaboration patterns, leaders, influencers, bridgers of knowledge, and research clusters. A case study that analyzes collaboration on obesity research at the intra-institutional level is presented to illustrate the potential benefits and applicability of this framework. Finally, a goal programming (GP) model and a cost-effectiveness analysis (CEA) approach is proposed to guide the proposal selection problem and estimate the potential impact of healthcare interventions respectively. Specifically, a GP model was developed for the proposal selection of a CTSA’s hub from a strategic perspective. Additionally, a model for rapid estimation of impact is applied to an early detection of intervention of Parkinson’s disease. Lastly, a combination of these two techniques is modeled to incorporate cost-effectiveness measures into the proposal selection problem. These studies cover relevant topics that aim to support the understanding of translational research and offer pathways for a more efficient translation of new discoveries into clinical practice through QI research approaches.