Health Care Systems Engineering

The Health Care Systems Engineering Program in the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Heath Care Delivery applies engineering principles to improve systems and staff wellness across the enterprise — for example, optimizing patient and clinician scheduling, or using a human factors approach to enable better teamwork to prevent clinician injuries.

The Heath Care Systems and Engineering Program drives partnership between engineering and Mayo Clinic's integrated practice, research and education. The program's goal is to continuously improve health care delivery systems through systems engineering, operations, research tools and complementary methodologies from other fields.

The program engages clinicians, collaborators and patients in identifying and solving relevant problems using scientific engineering methodology, thereby co-creating relevant solutions to complex health care problems and improving quality of life for patients and health care providers.

Focus areas

The Health Care Systems Engineering Program has two main domains: operations research and human factors engineering.

Operations research

Operations research involves multiple theories and techniques, including:

  • Optimization modeling — for example, linear vs. nonlinear, stochastic vs. deterministic and dynamic programming modeling
  • Algorithm developing using programming languages
  • Heuristic modeling
  • Statistical modeling — for example, inference, regression, Bayesian and principle component modeling
  • Mathematical modeling
  • Machine learning — for example, supervised, unsupervised and reinforced learning
  • Simulation optimization modeling

Human factors engineering

The theories and techniques involved in human factors engineering include:

  • Cognitive engineering and neuroergonomics
  • Physical ergonomics
  • Sociotechnical systems engineering
  • Utilizing a mixed method of research design and analysis
  • Employing cognitive and physical ergonomic engineering frameworks, theories and models
  • Applying validated human factors engineering tools and strategies:
    • Workload assessment
    • Cognitive task analysis
    • Wearables
    • Human systems integration
    • Participatory ergonomics
  • Working from the perspective of the humans in a system, such as patients, medical staff, and other users or stakeholders


Operating room of the future

Health care systems engineers in the Mayo Clinic Kern Center for the Science of Heath Care Delivery, along with the Department of Surgery and its affiliated Surgery Research Center, are leading research on the operating room of the future.

This project uses human factors engineering and systems integration approaches to improve teamwork and safety in the operating room. It addresses physical and cognitive workload issues to increase efficiency, quality, and clinician and patient safety and satisfaction.


OR-Stretch is an intervention for the physical discomfort surgery imposes, and which can even shorten surgeons' careers. It was created in the Human Factors Engineering Laboratory of Robert D. and Patricia E. Kern Scientific Director for Health Care Systems Engineering Susan Hallbeck, Ph.D.

The OR-Stretch intervention is being integrated into operating rooms at Mayo Clinic, across the country and around the world.

Learn more:

Family Medicine burnout prevention

The Heath Care Systems Engineering Program is leading the development of a near real-time snapshot dashboard for assessing workload and clinical burden in the Mayo Clinic Department of Family Medicine. The dashboard will help department leaders quickly and frequently assess employees' workloads and clinical burdens and adjust as needed to prevent burnout.

The proof-of-concept study is being conducted at Mayo Clinic Health System — Red Cedar in Menomonie, Wisconsin, with the goal of being scalable across the Mayo Clinic enterprise.

Clinician cognitive load and decision-making

This project studies clinicians' cognitive loads when making decisions about patients' diagnoses, treatments and management strategies. The program team is assessing clinicians' standard decision-making processes or approaches compared with decision-making with additional decision-support tools.

Biometric sensors positive emotions

The purpose of this study is to evaluate the effectiveness of two types of biometric sensors — electroencephalography devices and galvanic skin response sensors — for objectively measuring positive emotions such as joy. Specifically, the project team is testing whether the biometric sensors will show increased output levels in response to positive events and stimuli. The goal of the project is to assess whether the devices can be reliably used to measure the presence of positive emotions in future research — for example, studies on joy in the workplace.

Improving health care for patients and staff

Health care systems engineering projects focused on information and decision-making have deployed award-winning care delivery discoveries and demonstrated improvement in practice. Engineered solutions for patients provide improved safety, better access, and timely and patient-centered care in areas including:

  • Emergency medicine
  • Hospital services
  • Radiology
  • Surgery

The program team develops solutions to ensure reduced staff burden and better planning and resource utilization.

Outpatient scheduling systems

The Health Care Systems Engineering Program team is redesigning outpatient scheduling systems at Mayo Clinic through development of an optimal scheduling template. Project objectives include minimizing patient wait time, provider idle time, overtime and resource violation.

Before implementation, most patients and staff were scheduled between 10:30 a.m. and 2:00 p.m., peaking at 11:00 a.m. After implementation, most patients and staff were distributed evenly between 9:00 a.m. and 3:30 p.m.

Chemotherapy unit patient and staffing levels in the Mayo Clinic outpatient scheduling system before and after implementing optimal scheduling template

While developing optimal scheduling policies — including overbook-override, cancellations and urgent care needs — the team accounts for uncertainties such as patients missing appointments without notice and appointment mixups. The team is also determining optimal appointment duration for various patient groups according to patient characteristics and conditions.

An example application of this research is a new chemotherapy scheduling template design for seven chemotherapy units across the Mayo Clinic enterprise.

Real-time scheduling decision tool

This research involved development of a patient call-back control system that minimizes treatment resource conflict to improve service quality and a search algorithm that minimizes patient procedure lead-time by finding the most appropriate providers based on their availability.

Pairing algorithm for cardiovascular interventionalist and surgeon for transcatheter aortic valve replacement procedure

Example projects include proton beam therapy "gatekeeper" logics development with simulation validation and a search algorithm for optimal pairing of cardiovascular interventionalists and surgeons for transcatheter aortic valve replacement procedures.

Predictive modeling

The Health Care Systems Engineering Program team uses predictive analytics, such as statistical and machine-learning methods, to inform decision-making on staffing, schedule planning and triage appropriateness for scheduling. This work is often performed as elements of scheduling and staffing optimization. Example projects include:

  • Staffing cardiovascular ambulatory care nurses for clinical and nonclinical work time
  • Predicting bone marrow transplant stem cell collection days for level-loading apheresis capacity
  • Classifying scheduling urgency of patients in echocardiography laboratories for better scheduling
  • Enhancing triage decision-making for pain management consultations and procedures

Provider time allotment and patient assignment

The Health Care Systems Engineering Program team has developed a provider assignment time allotment tracking algorithm to ensure fulfillment of work scheduling commitments and determine optimal provider panel size, patient assignment and care team structure. The algorithm helps balance provider workload, reduce provider burnout and improve care continuity.

Example projects include developing and implementing a time allotment tracker for cardiovascular care providers and redesigning family medicine panel sizes and care team structures.


Review publications on PubMed from health care systems engineering researchers at Mayo Clinic.


Susan Hallbeck, Ph.D.

  • Robert D. and Patricia E. Kern Scientific Director for Health Care Systems Engineering
  • Email: