Mannequins, Simulators and
E-learning in Medicine
Sem Lampotang, PhD
Professor of Anesthesiology
Center for Simulation, Advanced Learning and Technology
Department of Anesthesiology
Medical Update
University of Mauritius
July 25, 2007
Disclosure
• Co-inventor of the Human Patient
Simulator
• Developer of the simulations on the Virtual
Anesthesia Machine web site
http://vam.anest.ufl.edu/wip.html
Acknowledgements
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Thomas H. Maren Foundation - USA
Anesthesia Patient Safety Foundation - USA
Novo Nordisk - Denmark
IBM Thomas J. Watson Research Center - USA
GE Healthcare / Ohmeda - USA
GaleMed – Taiwan
Prodol/AirTraq - Spain
Enturia - USA
Molecular Products – United Kingdom
Karl Storz - Germany
The VAM team
Outline
• Simulation in Healthcare
• Mannequin simulators
• Web simulation and e-learning
Deaths from medical error
• Institute of Medicine’s 1999 report “To err
is human” estimates that medical error
causes between 44,000 to 98,000 deaths
each year in the United States
• Equivalent on the low end to 2 jumbo jets
full of passengers crashing every week!
Reliable Performance is Elusive….
Troglitazone LFT
monitoring
IRS Tax Advice
ACE-I for EF <40%
and yearly HbA1C for DM
B blockers after AMI
1,000,000
Restaurant bill mistakes
100,000
Six Sigma
Sigma Values
+/- 1 = 68.26%
+/- 2 = 95.44%
+/- 3 = 99.73%
10,000
Point of Inflection
Airline baggage handling
p(d)
Negligence in hospitals
ABX for Viral URI
1,000
p(d)
s1
Defects per Million
6
100
5
4
3
2
1
LSL
A 6 Sigma Process includes
6 standard deviations between
the mean and the spec limit
10
1
M
2
3
4
5
6
USL
6 sigma accuracy = 3.4 defects per million
Anesthesia deaths
Airline safety
1
0
1
2
3
4
5
6
Sigma Level
6
Rene Alamberti, Ann Intern Med. 2005;142:756
Criteria for justifying
the expense of simulation
(in any field)
• Errors are expensive
• Reality is dangerous
• Events are rare
Why simulation?
• Learning in clinical medicine has
traditionally followed an apprenticeship
model: “see one, do one, teach one”
• Rate of discovery and creation of new
knowledge keeps on accelerating,
including in healthcare – apprenticeship
model no longer tenable
• Learning by doing
• Hands-on learning
Mannequin Simulators
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Consumes O2, produces CO2
Clinical signs
Monitored physiological signs
Mathematical models of
pharmacokinetics/pharmacodynamics
• Cardiopulmonary model
• Can simulate different disease states
Gainesville Anesthesia
Simulator
Human Patient Simulator
Respiratory System
Respiratory System
Respiratory System
O2
CO2
N2
N2O
Invasive and non-invasive blood
pressure
Electrocardiogram
Multi-compartment model
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Left atrium
Left ventricle
Intrathoracic artery
Extrathoracic artery
Vessel rich group tissue
Muscle group tissue
Fat group tissue
Extrathoracic vein
Intrathoracic vein
Right atrium
Right Ventricle
Pulmonary artery
Ventilated lung tissue
Shunted lung tissue
Pulmonary vein
Nervous System
Nervous System
Urinary System
Drug Recognition
Drug Recognition
Installations worldwide
• http://www.meti.com
• HPS Installations
Some problems spanning the entire
healthcare system…
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Industry education
Education of regulatory body personnel
User education and training
Patient education issues
Patient safety issues involving healthcare
systems
Transparent Reality (TR)
Simulation
• Invented at UF
• “Transparent reality simulation” coined at
UF
• Identified as 4 – 5 years away from
general adoption by Educause Horizon
Report 2006
Some problems …
• Industry education
– Basic science and R&D
– Engineering/production/pre-market approval
(Mannequin Simulator-Based Usability
studies)
– Marketing/Sales force training
• Education of regulatory body personnel
(FDA)
Some problems …
• User education and training
– Reality is opaque and complex and can get in the way
of learning
– Incompatible international standards
– Medical error
• Human error 3 times more common than equipment failure
for anesthesia machines (Closed claims study)
• Failure to check, failure to detect, failure to teach
– “Black hole” users/ Difficult users
• Are clinicians really using the training material?
• How much are they really getting? What do they find hard?
– Credentialing
• Can clinicians really use a given product safely?
– Equitable access to essential patient safety materials
Some problems …
• Patient education issues
– Patient compliance
• Non-compliance major reason for organ transplant
rejection
Some problems …
• Patient safety issues; Systems issues
– Defining the problem
• Identifying the problem
• Quantifying the problem
• Investigating causal factors and possible solutions
– A FMEA (Failure Mode Effects Analysis)
exercise has to take into account the entire
system including user training, competency,
vigilance and fatigue
UF Virtual Anesthesia Machine
Web Site
http://vam.anest.ufl.edu/wip.html
The UF VAM web site will be used as a
concrete example of the different forms of
web applications that address some of the
previously identified problems.
Transparent reality simulation
Blackbox opaque simulation
TR Provides Better Learning
3
Quality Score (max. 4.0)
Transparent VAM
2.5
Opaque VAM
2
1.5
1
0.5
0
Component
Identity
Component
Function
System
Dynamics
Some problems …
• Patient education issues
– Patient compliance
• Non-compliance major reason for organ transplant
rejection
Some problems …
• Patient safety issues; Systems issues
– Defining the problem (anesthesia machine
pre-use check survey)
• Identifying the problem
• Quantifying the problem
• Investigating causal factors and solutions
– Survey results
• 20% check before every case, 50% only first case
of the day, what about remaining 30%?
Does this really work?
• The web provides democratic “peer review”
where everyone votes with their mouse.
• #1 on Google for “anesthesia machine”
• #1 on Google for “airway device”
• #1 on Google for “fospropofol simulation”
• #1 on many more terms and search engines
• Webalizer stats
• AwStats stats
Equitable access to essential
patient safety materials
Questions?
• Email: sem@anest.ufl.edu
• Simulation portfolio URL:
http://vam.anest.ufl.edu/wip.html