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Mannequins, Simulators and E-learning in Medicine

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... Advanced Learning and Technology. Department of Anesthesiology ... Engineering/production/pre-market approval (Mannequin Simulator-Based Usability studies) ... – PowerPoint PPT presentation

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Title: Mannequins, Simulators and E-learning in Medicine


1
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

2
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

3
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

4
Outline
  • Simulation in Healthcare
  • Mannequin simulators
  • Web simulation and e-learning

5
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!

6
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
6
Sigma Level
6
7
Rene Alamberti, Ann Intern Med. 2005142756
8
Criteria for justifying the expense of
simulation (in any field)
  • Errors are expensive
  • Reality is dangerous
  • Events are rare

9
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

10
Mannequin Simulators
  • Consumes O2, produces CO2
  • Clinical signs
  • Monitored physiological signs
  • Mathematical models of pharmacokinetics/pharmacody
    namics
  • Cardiopulmonary model
  • Can simulate different disease states

11
Gainesville Anesthesia Simulator
12
Human Patient Simulator
13
Respiratory System
14
Respiratory System
15
Respiratory System
O2
CO2
N2
N2O
16
Invasive and non-invasive blood pressure
17
Electrocardiogram
18
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

19
Nervous System
20
Nervous System
21
Urinary System
22
Drug Recognition
23
Drug Recognition
24
Installations worldwide
  • http//www.meti.com
  • HPS Installations

25
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

26
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

27
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)

28
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

29
Some problems
  • Patient education issues
  • Patient compliance
  • Non-compliance major reason for organ transplant
    rejection

30
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

31
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.

32
Transparent reality simulation
33
Blackbox opaque simulation
34
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
35
Some problems
  • Patient education issues
  • Patient compliance
  • Non-compliance major reason for organ transplant
    rejection

36
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?

37
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

38
Equitable access to essential patient safety
materials
39
Questions?
  • Email sem_at_anest.ufl.edu
  • Simulation portfolio URL http//vam.anest.ufl.ed
    u/wip.html
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