Health Care Systems Engineering Section

The Health Care Systems Engineering Section applies engineering expertise and infrastructure to design, evaluate or reengineer healthcare delivery processes and procedures. The section aims to advance Mayo Clinic's practice priorities across multiple domains, including supporting the workforce and optimizing patient flow and resource utilization. Further, its research enhances Mayo's ability to provide consistently safe and highly reliable care.

Much of the section's work aligns to two programs in the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery:

Applied Operations Research team

The Applied Operations Research team, which is part of the Health Care Systems Engineering Section, conducts practical research in mathematical and computational sciences. The team applies industrial engineering and data science principles to improve day-to-day delivery of healthcare systems.

The team's expertise includes operations research, system engineering, statistics, informatics and artificial intelligence (AI). Research focuses on quantitative modeling, designing interventions, and evaluating processes and systems to improve and optimize patient scheduling. Research also focuses on resource allocation and capacity.

By studying ways to schedule patients and streamline workflows and staffing, the Applied Operations Research team's research has positively affected patient care. This includes creating access for patients with serious or complex healthcare needs and staff satisfaction, such as through the use of workload balancing.

Many of the team's projects have been implemented. These projects often spark innovative collaborations in other clinical areas.

These examples illustrate practice-transforming work that enables Mayo to provide safe and reliable care for patients:

  • Scheduling systems. The team develops and implements scheduling systems that use optimization and simulation modeling to improve patient access to care while balancing staffing workloads and maximizing the use of space. These systems have been implemented in all chemotherapy infusion locations across Mayo Clinic.
  • AI-based systems. The team discovers and implements AI-based systems that leverage machine learning to automate intake triage processes for optimal patient scheduling. These systems ensure that the right patient is getting the right treatment in a timely manner. These systems are implemented within pain management and heart rhythm services.
  • Algorithms. The team engineers and implements heuristic and predictive algorithms for medical staff calendar planning in Mayo Clinic units or departments, such as laboratory medicine and pathology, cardiology, and nursing. These tools guide activities across Mayo Clinic, optimizing current and future staffing for the delivery of healthcare services.

Experts on the Applied Operations Research team also pursue solutions based on natural language processing. The team designs and develops natural language processing-driven healthcare analytics, leading to the discovery of actionable knowledge for Mayo's clinical practice. Advanced natural language processing pipelines that enhance patient and healthcare professional experiences are developed through clinical partnerships. These tools leverage unstructured clinical notes and AI systems for automated information extraction, enabling accurate and timely decision-making in healthcare.

Human Factors Engineering team

The Human Factors Engineering team, which is part of the Health Care Systems Engineering Section, collaborates with clinicians, scientists and patients to identify and solve physical, cognitive and organizational stressors. The team employs engineering methodologies and innovative technologies to research and solve complex healthcare problems these stressors cause, thereby improving quality of life for Mayo's patients and staff.

These team's experts can measure physical and cognitive stressors using cutting-edge technology such as wearables. The team focuses on the interaction of human physical and psychological limitations within the clinical system. It develops interventions that support a sustainable and thriving workforce and can provide the reliably safe and high-quality care that patients expect.

This collaborative research results in implementations such as:

  • AI system. The team installed an AI system in operating rooms to understand all aspects of motion, discussion and workflow during surgical procedures. This system has improved efficiency and spurred improvements that will affect the quality of future care.
  • Wearables. The team uses wearables to understand stressors by monitoring physical activity, sleep and other stress points among staff and patients. This work leads to interventions, including the further use of wearables that elicit clinically meaningful improvements in sleep quality, stress and the basic sustainability of daily function for staff or patients.
  • Novel interventions and technologies. The team tests novel interventions and technologies such as exoscopes, anesthesia labeling systems, exoskeletons and microbreaks to reduce mental and physical workload for clinical staff. This work enhances healthcare professional well-being and patient safety.