1. ©2011 MFMER | slide-1
Medical Simulation 2.0
Improving value-based healthcare
delivery
Yue Dong, M.D.
Mayo Clinic Multidisciplinary Simulation Center
METRIC (Multidisciplinary Epidemiology and Translational Research in
Intensive Care)
Mayo Clinic Center for Science of Healthcare Delivery

2. Disclosures
• No financial COI

3. ©2011 MFMER | slide-3
Mayo Clinic Multidisciplinary Simulation Center

4. Fellows
Anesthesiologist
Medical
Pulmonologist
Intensivist
Simulation
Medicine
Statistician
Administration
Informatics
ER medicine
Research
Coordinator
Collaborators
Pediatrician
METRIC (Multidisciplinary Epidemiology and Translational
Research in Intensive Care)

5. Multidisciplinary Collaboration
Dr. Hutian Lu
Dr. Susan Lu, Sura K Ak Qudah
Dr. Ashish Gupta
Dr. Mark Van Oyen, Pooyan Kazemian
Bjorn, Berg

6. Objectives
• Challenges facing healthcare professionals
to improve the healthcare delivery: Systems
Thinking and Patient Safety
• Summarize simulation and modeling tools
for systematic analysis and optimization
complex system processes and
interventions
• Describe common computer simulation
applications for quality improvement and
patient safety in ICU.

7. ©2011 MFMER | slide-7
© 2010 Mayo Foundation for Medical
Education and Research

8. Vis-à-vis International Sepsis
Campaign
Institution Compliance, % Mortality, %
Spain
•Pre-intervention 5.3 44
•Post-
intervention
10.0 39.7
International
•Pre-intervention 10.9 37
•Post-
intervention
41.3 30
Mayo
•Baseline 10.5 31.5
•Sepsis QI 58.4 22.0

9. Time, June 22, 2010

10. Health System Safety
• 33.6 million
admissions to U.S.
hospitals in 1997
• 44,000- 98,000
Americans die each
year as a result of
medical errors.
• Total cost $17- $29
billion

11. *Rate of growth declining in recent years, McKinsey 2011

12. U.S. spends most, but lower life
expectancy relative to developed peers
Source: OECD Health Data, 2008
~$3 Trillion (~1/5 GDP)
~ 30% may be waste
USA

13. Green LW. Making research relevant: if it is an evidence-based practice, where's the practice-
based evidence? Family Practice 2008; 25: i20–i24

14. “Blue Highways” on the NIH Roadmap
Practice-based
research
Phase 3 and 4 clinical
trials
Observational studies
Survey research
Basic science
research
Preclinical studies
Animal research
Human clinical
research
Controlled
observational studies
Phase 3 clinical trials
T1
Case series
Phase 1 and 2
clinical trials
Clinical practice
Delivery of recommended
care to right pt at right time
Identification of new clinical
questions and gaps in care
T2
Translation
to humans
T2
Guideline
development
Meta-analyses
Systematic
reviews
Translation
to patients
T3
Dissemination
research
Implementation
research
Translation
to practice
Westfall JM et al: JAMA 297:403, 2007
Bench Bedside Practice

15. The fundamental problem with the quality of
American medicine is that we’ve failed to view
delivery of health care as a science.
• understanding disease
biology
• finding effective therapies
• insuring those therapies are delivered effectively
Peter Pronovost http://www.letstalkhealthcare.org/health-care-costs/how-a-checklist-can-improve-health-care/

17. Temporal Trends in Rates of Patient Harm
Resulting from Medical Care
Temporal Trends in Rates of Patient Harm Resulting from Medical Care. Landrigan, et al, N Engl J Med 2010 ; 363 : 2124 - 2134

18. Complexity in ICU

19. Critical Care at Mayo
Courtesy of Dr. Vitaly Herasevich

21. Health care as a complex
adaptive system
W. B. Rouse. Health care as a complex adaptive system: Implications for design and management. The Bridge, 38(1), Spring 2008.

22. Complex adaptive systems
• nonlinear and dynamic, system behaviors may appear to be
random or chaotic.
• composed of independent agents whose behavior is based on
physical, psychological, or social rules rather than the demands
of system dynamics.
• agents’ needs or desires, their goals and behaviors are likely to
conflict. In response to these conflicts or competitions, agents
tend to adapt to each other’s behaviors.
• agents are intelligent. As they experiment and gain experience.
• adaptation and learning tend to result in self-organization.
Behavior patterns emerge rather than being designed into the
system.
• no single point(s) of control.
Rouse, 2000

23. William Worrall Mayo, MD
“Left open for further thought
and research”

24. The world is a complex system of systems
Communication
$ 3.96 Tn
Transportation
$ 6.95 Tn
Leisure / Recreation /
Clothing
$ 7.80 Tn
Healthcare
$ 4.27 Tn
Food
$ 4.89 Tn
Infrastructure
$ 12.54 Tn
Govt. & Safety
$ 5.21 Tn
Finance
$ 4.58 Tn
Electricity
$ 2.94 Tn
Education
$ 1.36 Tn
Water
$ 0.13 Tn
Global system-of-systems
$54 Trillion
(100% of WW 2008 GDP)
Same Industry
Business Support
IT Systems
Energy Resources
Machinery
Materials
Trade
Legend for system inputs
IBM analysis based on OECD data.
Note: Size of bubbles represents systems’ economic values. Arrows represent the strength of systems’ interaction.
Source: IBM Institute for Business Value analysis of Organisation for Economic Co-operation and Development (OECD) data.

27. ©2011 MFMER | slide-27
System integration

28. Human beings make mistakes because
the systems, tasks and processes they
work in are poorly designed.
Dr. Lucian Leape
Every system is perfectly designed
to get the results it gets.
Dr. Donald M. Berwick
Systems approach to improve
patient safety

29. Transforming healthcare:
a safety imperative
L Leape, D Berwick, C Clancy, et al. Qual Saf Health Care 2009; 18:424-428

30. Swiss Cheeses Model
©2011 MFMER | slide-30

31. Outcome + Safety + Service
Value =
Cost over time
Leveraging for Highest Value
Smoldt RK, Cortese DA. Pay-for-performance or pay for value?
Mayo Clinic Proceedings 2007;82:210-3

32. Systems Approach
to Improve Patient Safety
Martinez, et al. Anesth Analg 2010 110: 307-311

33. “ Simply educating and training more physicians will not be enough to address these
shortages. Complex changes such as improving efficiency, reconfiguring the way some
services are delivered and making better use of our physicians will also be needed.”
The Complexities of Physician Supply and Demand: Projections Through 2025. 2008 AAMC http://www.aamc.org/workforce

34. 2011, Health IT and Patient Safety: Building Safer Systems for Better
Care, Committee on Patient Safety and Health Information Technology; Institute of Medicine

35. Adjust structure and process to eliminate or
minimize risks of health care-associated
injury, before they have an adverse event-
impact on the outcomes of care
Donabedian. Evaluating of Medical Care. The Milbank Memorial Fund Quarterly,
Vol. 44, No. 3, Pt. 2, 1966 (pp. 166–203)

36. System Interventions
Systems Engineering Initiative for Patient Safety (SEIPS) Work system design for patient safety: the SEIPS model.
Carayon P, et al . Qual Saf Health Care. 2006 Dec;15 Suppl 1:i50-8. Review.

37. WHO Global Priorities for Patient Safety Research
Bates DW, et al. Global priorities for patient safety research. BMJ 2009;338:b1775

38. Structure, process or outcome: which
contributes most to patients' overall
assessment of healthcare quality?
• Experiences regarding process aspects explained
most of the variance in the global rating (16.4–
23.3%), followed by structure aspects (8.1–21.0%).
Experiences regarding outcome did not explain
much variance in the global rating in any of the
patient groups (5.3–13.5%).
• What is patient-centered care?
BMJ Qual Saf doi:10.1136/bmjqs.2010.042358

39. “We can’t solve
problems by
using the same
kind of thinking
we used when
we created
them”

40. Delivery System

41. Order (2 lanes !)
Pay
Pickup
System Design Thinking
Service centered = Customer centered

42. Escape Fire, Berwick, 2006
Mistake Proofing/Force Functioning

43. • designing the system to prevent
errors
• designing procedures to make errors
visible when they do occur so that
they may be intercepted
• designing procedures for mitigating
the adverse effects of errors when
they are not detected and intercepted
Nolan, 2000 BMJ Department of Health and the Design Council in England 2003

44. Common patient safety
improvement efforts
• Culture
• Crew resource
management
• Event reporting:
close-claim; near-
miss
• Root cause
analysis
• Human factor
design
• Simulation
• Technology
• Lean, six-sigma
• Etc.

45. Terminology
• Model vs. Simulation (noun)
Model can be used WRT conceptual,
specification, or computational levels
Simulation is rarely used to describe the
conceptual or specification model
Simulation is frequently used to refer to the
computational model (program)
• Model vs. Simulate (verb)
To model can refer to development at any of
the levels
To simulate refers to computational activity
Steve Park and Larry Leemis

46. Clinical Micro-system
Clinical
Delivery System
Patient Providers Processes
Complexity/SOP
Bottleneck/ Waste/
no value added
Education/Training Supply/Demand

47. • Simulation is the imitation or representation of
one act or system by another.
• Healthcare simulations can be said to have four
main purposes – education, assessment,
research, and health system integration to
facilitate patient safety...
• Simulations may also add to our understanding of
human behavior in the true–to–life settings in
which professionals operate.

48. Simulation based medical education

49. The 11 dimensions of simulation applications.
Gaba D M Qual Saf Health Care 2004;13:i2-i10
©2004 by BMJ Publishing Group Ltd
The 11 dimensions of simulation applications

50. Medical Education
• Study the effectiveness of
simulation based medical education
(SBME)
• Developing valid outcome
assessment instrument, stretch
measurement endpoints from the
simulation lab into clinical practice
(association studies)
• Provide highly reliable data for
decision support and high-stakes
testing.

51. ©2011 MFMER | slide-51
Simulation-based objective assessment Discern Clinical Proficiency in Central Line Placement, Dong, et. al, 2010

52. ©2011 MFMER | slide-52
Patient Outcomes
Mastery
n=26
Control
n=24
Adjusted Analysis
OR (95%CI) p-value
# Patients/Repairs 48/72 38/58
Intra-op Complications*
At least one of any type 5 (7) 17 (29) OR 0.15
(0.04, 0.59)
0.006
Post-op Complications*
At least one of any type 4 (9) 15 (26) OR 0.17
(0.04, 0.74)
0.018
Overnight Stay* 5 (7) 12 (21) OR 0.37
(0.08, 1.67)
0.20
*N (%)
Simulation-Based Mastery Learning Improves Patients Outcomes in
Laparoscopic Inguinal Herniorrhaphy, Benjamin Zendejas, MD, MSc

53. Skill Acquisition Curve
Impact of Zero-Risk Training
CP1345275-1
Clinical competence
Metricassessment
(e.g.,compositescore)
Time
Traditional training
Safety standard
Simulation-based training
Dong et al, Chest 2010

54. The First Research Consensus Summit of the
Society for Simulation in Healthcare
• Simulation for Learning and Teaching Procedural Skills: The State of the
Science
• Simulation-Based Team Training in Healthcare
• A Path to Better Healthcare Simulation Systems: Leveraging the Integrated
Systems Design Approach
• The Study of Factors Affecting Human and Systems Performance in
Healthcare using Simulation
• Literature Review: Instructional Design and Pedagogy Science in
Healthcare Simulation
• Evaluating the Impact of Simulation on Translational Patient Outcomes
• Research Regarding Methods of Assessing Learning Outcomes
• Research Regarding Debriefing as Part of the Learning Process
• Simulation-Based Assessment of the Regulation of Healthcare
Professionals
• Reporting Inquiry in Simulation
Simul Healthc. 2011 Aug;6 Suppl:S1-9.

55. ALL MODELS ARE WRONG
BUT SOME ARE USEFUL
George Box

56. Simulation in Healthcare
Simulation 1.0
• Simulation as subject
• At simulation center
• Education
Training effectiveness
Psychometric qualities
Ecological validity
Simulation 2.0
• Simulation as tool
• Everywhere
• Daily practices
System integration
Human factors
Usability of device,
process, etc.

57. Military Simulation Spectrum
J G Taylor, Modeling and Simulation of Land Combat, ed L G Callahan, Georgia Institute of Technology,
Atlanta, GA, 1983

58. Human factor and Usability research
• Using simulation as a tool to study human
performance variation under different “stress
conditions” (fatigue, cognition, workload, etc.)
• Investigating provider behaviors/tasks
Observation “in the wild” (Ethnography)
Simulation environment
• Conduct usability testing of devices instrument
and processes, using information driven
approach for new system design
• Evaluation of the impact on clinical practices

59. The effect of drug concentration expression on
epinephrine dosing errors: a randomized trial
Wheeler DW, Carter JJ, Murray LJ, Degnan BA, Dunling CP, Salvador R, et al.. Ann Intern Med 2008;148:11-4.
(1 mg in 1 mL) (1 mL of a
1:1000 solution)

60. Ahmed, et al. Critical Care Medicine, 39(7) 1626-1634
The effect of two different electronic health record user interfaces on
intensive care provider task load, errors of cognition, and performance

62. Complexity of Sepsis Resuscitation in ICU
Adopted from: Network medicine--from obesity to the "disease". Barabási AL., N Engl J Med. 2007 Jul 26;357(4):404-7.
SHOCK
DIC AKI
ALI
Physician RT
Pharmacist
Nurse
Time 
Baseline
PatientOutcome,
ProviderSatisfactions

63. Trial and error
©2011 MFMER | slide-63
http://www.economist.com/node/174411 http://www.wired.com/magazine/2011/12/ff_causation/all/1

64. How about the population at risk

65. Modeling &
Simulation

66. Computer Simulation
R. P. Science, New Series, Vol. 256, No. 5053 (Apr. 3, 1992)

67. Simulation in manufacturing and business: A review
M. Jahangirian, T. Eldabi, A. Naseer, L.K. Stergioulas and T. Young, Simulation in manufacturing and business: a review, European Journal of
Operational Research 203 (2010), pp. 1–13

68. Simulation-based Engineering and Science

69. ©2011 MFMER | slide-69

70. Simulation and Healthcare
Delivery

71. System Engineering Tools for Healthcare Delivery
Proctor P. Reid, W. Dale Compton, Jerome H. Grossman, and Gary Fanjiang, Editors, Committee on Engineering and the Health
Care System, Institute of Medicine and National Academy of Engineering, 2005

73. ©2011 MFMER | slide-73
Systems Engineering: Modeling
and Simulation
• Using system engineering/operation
research approach and readily available
software(discreet event simulation, etc.)
build a “test and learn” capacity to study
system performance and identify the
bottleneck,
• provide re-designed alternatives to
improve safety and efficiency of
healthcare delivery system.
• conduct a valid test of quality
improvement innovations before clinical
implementation

74. ©2011 MFMER | slide-74
Project 1: Sepsis Workflow Redesign

75. Sepsis Care Optimization by Discrete Event Simulation
(S-CODES)
Place Central
Line
Central
Line
Approval
Etc, etc,
etc
Dong Y, Lu H, Rotz J, et al. Simulation Modeling of Healthcare Delivery During Sepsis
Resuscitation. Critical Care Medicine 2009;37:A334

76. Project 2: Scheduling for Critical Care Fellows using Modeling and
Simulation: The Trade Off Between Duty Hours and Hand-offs
Fellow A Fellow B Fellow C
7 am 7 pm
Patient 1
Patient 2
Patient 3
Patient 4
Patient 5
Handoffs
0
2
1
0
1
4
Provider
Transfer
Patient
Handoff

77. Comparison of Provider Scheduling
©2011 MFMER | slide-77
Provider
Transfers (H/L)
per month
Patient
Handoffs
(avg./mo)
ICU Coverage
(hrs/wk)
Average Duty
Hours
(hrs/wk)
Old Schedule 84 (84/0) 650 ± 4 294 73.5
New Schedule
112 (67/45)
(+25%)
860 ± 5
(+33%)
312
(+6%)
62.4
(-15%)
Janish, Dong, SCCM, 2011

78. Project 3: Time-motion observational study of
multidisciplinary ICU rounding in a teaching hospital
• To describe the current practice, and structure of the morning
multidisciplinary round in the ICU practices (MICU, SICU)
• Prospective field observation of ICU provides task (consultant,
fellow, resident/intern, nurse, pharmacist) based on systems
engineering approach
• Task categories defined based on provider survey
• Purpose strategies (work-flow redesign, new EMR interface) to
improve
the efficiency of ICU round, reduce MEOW
patient outcome
provider satisfaction

79. ©2011 MFMER | slide-79
Project 4: Education Game:
The Friday Night at ER ™

80. Professional Society

83. Challenges and opportunities
• Fragmentation of
care delivery
• Access
information from
various sources
• Clinical
implementation
• System integration
• Health IT (mobile,
cloud, social
networking, big
data)
• Provider education
and change culture

84. • 1920’: BME, Biophysics, Medical Physics
• 1943: German Biophysical Society
• 1948: Annual Conference of Engineering in
Medicine and Biology/Radiation Research
Society
• 1961: International Federation of Medical
and Biological Engineering
• 1968:Biomedical Engineering Society

85. Road map for
better healthcare delivery

86. Road map for
better healthcare delivery
Dong Y, et al. ICU Operational Modeling and Analysis. In: Kolker A, Story P, eds. Management Engineering for
Effective Healthcare Delivery: Principles and Applications. Hershey, Pennsylvania, USA: IGI Global; 2011.

87. Key Messages
• The complexity of healthcare delivery
systems contributes to preventable
medical error and insufficient quality
• Computer modeling/simulatio coupled with
realistic patient simulation represents a
potent catalyst in adapting systems
engineering principles to healthcare
• The medical community needs partnership
with the systems engineering community
to best deliver high value care

89. ©2011 MFMER | slide-89
Medicine: Human interactions

90. • email: dong.yue@mayo.edu
• Blog: simdoc.wordpress.com
• Twitter: dongyue
• LinkedIn:
• CiteUlike: simdoc