Healthcare Systems Engineering (HSyE) will embed proven evidence-based industrial and systems engineering (ISE) improvement methods into local healthcare organizations, similar to as used in other complex industries.
HSyE is funded by the Department of Health and Human Services through the Center for Medicare and Medicaid Innovation (CMMI) program.
HSyE is a university-level regional systems engineering extension center led by Northeastern University.
HSyE has focused on improving healthcare efficiency, quality, logistics, safety, flow, effectiveness, and access for more than 30 years. We have a simple mission: To broadly impact healthcare improvement through education, research, and application in systems engineering methods. We accomplish this mission through our HSyE undergraduate and graduate academic programs, national experiential coop education and summer internship programs, three federally-awarded healthcare IE centers, competitive scholarships, and strategic partnerships. We rely on deep industry-university partnerships to advance the shared missions of healthcare improvement and workforce development.
Healthcare Systems Engineering (HSE) methods range from lean six-sigma tools to advanced mathematical models used in many other industries to study, improve, and optimize process quality, delays, cost, efficiency, and effectiveness - national priorities also identified by the IOM. Recent healthcare applications include improvements in scheduling, readmissions, cost reductions, cancer care, and health services planning.
Systems engineers use a variety of methods to model, analyze, predict, improve, and optimize the performance of complex systems, sometimes supported by informatics to harness information in new and innovative ways. While these methods have a long but periodic history of use within clinical, operations, and administrative healthcare processes, their focus has been insignificantly positioned to dramatically move the U.S. healthcare system forward. Within healthcare, systems engineering applications span the spectrum from local "micro-system" process improvement activities to more global "macro-system" optimization problems, ranging methodologically from simple process improvement methods to advanced mathematical and computer modeling.