The primary theme for the Center for Re-engineering Healthcare (CRH) directed by Kenneth E. Wood, DO is to develop partnerships between engineering and healthcare in areas of interdisciplinary research and education that will facilitate the development, implementation and diffusion of Industrial and Systems Engineering ( ISyE) concepts to improve healthcare delivery. The CRH will bring together researchers,clinicians, educators and student from the fields of health sciences, engineering, social and behavorial science to address the challenges of improving healthcare by conducting basic and applied research related to healthcare delivery.
The CHR contains 2 laboratories with distinct areas of focus:
Human Factors Engineering Laboratory
Human Factors Engineering (HFEL) takes an interdisciplinary approach to enhancing patient safety, quality and efficiency by studying and understanding the relationships between people, technology and the system in which they work. HFE research is an essential part of systems analysis in areas such as job task analysis, system design, technology assessment and collaborative team building. Utilizing a sociotechnical approach, researchers are currently evaluating human factors in federally funded projects related to supply chain delivery and designing a system for the prevention, diagnosis and treatment of venous thromboembolism.
Modeling/Simulation and Operations Research Laboratory
Modeling and Simulation are aspects of industrial systems engineering (ISyE) that use advanced computer technology to create synthetic environments that enable complex scenarios to be studied and evaluated with solutions trialed in a virtual environment before undertaking real life implementation. When combined with Operations Research that employs advanced analytic methods to enhance decision making, system optimization is more readily achieved. Current research and clinical projects include supply chain modeling, hospital capacity and patient throughput simulation and Operating Room/Emergency Room simulation models.