1 / 39

Mannequins, Simulators and E-learning in Medicine

Explore the impact of mannequins, simulators, and e-learning in medicine. Discover how simulation technologies are revolutionizing medical training worldwide.

brackenj
Download Presentation

Mannequins, Simulators and E-learning in Medicine

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.edu/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 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!

  6. Six Sigma Sigma Values +/ - 1 = 68.26% Point of Inflection +/ - 2 = 95.44% +/ - 3 = 99.73% p(d) p(d) s 1 6 5 4 3 2 1 1 2 3 4 5 6 LSL USL M A 6 Sigma Process includes 6 standard deviations between 6 sigma accuracy = 3.4 defects per million the mean and the spec limit 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 10,000 Airline baggage handling Negligence in hospitals ABX for Viral URI 1,000 100 10 Defects per Million Anesthesia deaths Airline safety 1 0 1 2 3 4 5 6 Sigma Level 6

  7. Rene Alamberti, Ann Intern Med. 2005;142:756

  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/pharmacodynamics • 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 • Patientsafety 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 R&D • 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 Component Identity Component Function System Dynamics

  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@anest.ufl.edu • Simulation portfolio URL: http://vam.anest.ufl.edu/wip.html

More Related