Mannequins, Simulators and E-learning in Medicine Sem

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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

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Disclosure

• Co-inventor of the Human Patient Simulator
• Developer of the simulations on the Virtual
  Anesthesia Machine web site http//vam.anest.ufl.e
  du/wip.html

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Acknowledgements

• 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

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Outline

• Simulation in Healthcare
• Mannequin simulators
• Web simulation and e-learning

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Deaths from medical error

• Institute of Medicines 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!

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Reliable Performance is Elusive.
Troglitazone LFT monitoring
IRS Tax Advice
ACE-I for EF lt40 and yearly HbA1C for DM
B blockers after AMI
1,000,000
Restaurant bill mistakes
100,000
10,000
Airline baggage handling
Negligence in hospitals
ABX for Viral URI
1,000
100
10
Defects per Million
Anesthesia deaths
1
Airline safety
0
1
2
3
4
5
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Sigma Level
6
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Rene Alamberti, Ann Intern Med. 2005142756
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Criteria for justifying the expense of
simulation (in any field)

• Errors are expensive
• Reality is dangerous
• Events are rare

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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

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Mannequin Simulators

• Consumes O2, produces CO2
• Clinical signs
• Monitored physiological signs
• Mathematical models of pharmacokinetics/pharmacody
  namics
• Cardiopulmonary model
• Can simulate different disease states

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Gainesville Anesthesia Simulator
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Human Patient Simulator
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Respiratory System
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Respiratory System
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Respiratory System
O2
CO2
N2
N2O
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Invasive and non-invasive blood pressure
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Electrocardiogram
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Multi-compartment model

• 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

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Nervous System
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Nervous System
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Urinary System
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Drug Recognition
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Drug Recognition
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Installations worldwide

• http//www.meti.com
• HPS Installations

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Some problems spanning the entire healthcare
system

• Industry education
• Education of regulatory body personnel
• User education and training
• Patient education issues
• Patient safety issues involving healthcare
  systems

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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

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Some problems

• Industry education
• Basic science and RD
• Engineering/production/pre-market approval
  (Mannequin Simulator-Based Usability studies)
• Marketing/Sales force training
• Education of regulatory body personnel (FDA)

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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

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Some problems

• Patient education issues
• Patient compliance
• Non-compliance major reason for organ transplant
  rejection

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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

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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.

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Transparent reality simulation
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Blackbox opaque simulation
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TR Provides Better Learning
3
Transparent VAM
2.5
Opaque VAM
2
Quality Score (max. 4.0)
1.5
1
0.5
0
System Dynamics
Component Function
Component Identity
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Some problems

• Patient education issues
• Patient compliance
• Non-compliance major reason for organ transplant
  rejection

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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?

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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

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Equitable access to essential patient safety
materials
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Questions?

• Email sem_at_anest.ufl.edu
• Simulation portfolio URL http//vam.anest.ufl.ed
  u/wip.html