Evaluating the cost-effectiveness of an early detection of Parkinson's disease through innovative technology

ABSTRACT

Early detection of Parkinson's disease (PD) is critically important because it can increase patient quality of life and save treatment costs. An innovative approach for early detection of PD is to use nonwearable sensors that are capable of capturing skeletal joint data. This article evaluates the cost-effectiveness of this sensor-based intervention considering the quality-adjusted life years (QALYs) and the associated costs. The results indicate that the intervention would be cost-effective if devices were deployed for community health screening in public places such as health fairs and pharmacies.

Funding

The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000127 and TR002014. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Additional information

Notes on contributors

David A. Muñoz

David A. Muñoz holds a Ph.D. and M.Eng. in industrial engineering and operations research from the Pennsylvania State University. His main interests are the application of advanced analytics techniques to solve complex business problems. Currently, David is a consultant at McKinsey & Company.

Mehmet Serdar Kilinc

Mehmet Serdar Kilinc is a postdoctoral researcher at Oregon State University. He formerly worked as a postdoctoral researcher at the Pennsylvania State University. He obtained his Ph.D. degree in industrial engineering at the University of Arkansas. He graduated with bachelor's and master's degrees from Istanbul Technical University, Turkey. His primary research interest is developing quantitative approaches to design and evaluate health care delivery and IT systems.

Harriet B. Nembhard

Harriet B. Nembhard is the Eric R. Smith Professor of Engineering and Head of the School of Mechanical, Industrial and Manufacturing Engineering at Oregon State University. She was formerly a professor of industrial engineering at the Pennsylvania State University. Her research employs methods of statistics and operations research to improve complex systems and has led to numerous advances in health care delivery—many of which are presented in her textbook, Healthcare Systems Engineering.

Conrad Tucker

Conrad Tucker is an assistant professor of engineering design and industrial and manufacturing engineering at the Pennsylvania State University. He also holds an affiliate faculty position in computer science and engineering. His research focuses on mining large-scale data for insights that lead to better decisions. Toward this end, his research employs machine learning and statistical-based techniques, with applications in systems design and health care.

Xuemei Huang

Xuemei Huang is a board-certified research neurologist whose clinical practice and scholarly concentrations are focused on neurodegenerative disorders. Because of her background and training, she has a strong interest in translational neuroscience. Dr. Huang's major focus is the use of novel imaging modalities and designs to elucidate neurocircuitry (particularly basal ganglia and cerebellum) underlying various signs and symptoms of Parkinson's disease (PD) and the development of MRI biomarkers for early detection and progression of PD and related disorders. In addition, Dr. Huang investigates the interactive role of genetic and environmental factors in the etiology of PD and related disorders.