Distributed Immersive Virtual Reality Simulation Development for Medical Education

Training professionals for real-world application of required knowledge and skills and assessing their competence are major challenges. Simulations are being used in education and training to enhance understanding, improve performance, and assess competence. Validated virtual reality (VR) simulations provide a means of making experiential learning reproducible and reusable. Advanced communication networks, such as Internet2 Access Grid, allow dissemination of these simulations and collaborative learning independent of distance. The prior experiences of our three universities led to an interdisciplinary collaboration to further develop and evaluate an integrated, fully immersive, interactive VR based system. This environment employs simulations that are visually three-dimensional and are driven dynamically by a rules-based artificial intelligence engine within Flatland, a virtual environments development software tool, and associated commodity hardware. Studies include usability and validation, deployment for distributed testing over Internet2, and evaluation of impact on training and performance using concept mapping and knowledge structure methods. Subject matter experts found face and content validity in our closed head injury simulation. Seven pairs of medical students participated collaboratively in problem solving and managing of the simulated patient in VR. Students stated that opportunities to make mistakes and repeat actions in VR were extremely helpful in learning specific principles and they felt more engaged than in standard text-based scenarios. 48 students participated in knowledge structure experiments pre and post simulation experiences. Knowledge structure relatedness ratings were significantly improved in those students with lower pre-VR relatedness ratings indicating a potential value of VR simulation in learning. This research cuts across the integration of computing, networking, human-computer interfaces, learning, and knowledge acquisition. VR creates a safe environment to make mistakes and could allow rapid deployment for just-in-time training or performance assessment.

[1]  Rick Stevens,et al.  ActiveSpaces on the grid: The construction of advanced visualization and interaction environments , 2000 .

[2]  J. Bransford How people learn , 2000 .

[3]  C. Marano,et al.  To err is human. Building a safer health system , 2005 .

[4]  S. C. Johnson Hierarchical clustering schemes , 1967, Psychometrika.

[5]  Roger W. Schvaneveldt,et al.  Pathfinder associative networks: studies in knowledge organization , 1990 .

[6]  William C. McGaghie,et al.  Assessing Knowledge and Skills in the Health Professions: A Continuum of Simulation Fidelity , 1999 .

[7]  J. Samet,et al.  The New Mexico experiment: educational innovation and institutional change , 1989, Academic medicine : journal of the Association of American Medical Colleges.

[8]  William C. McGaghie,et al.  Effectiveness of a Cardiology Review Course for Internal Medicine Residents Using Simulation Technology and Deliberate Practice , 2002, Teaching and learning in medicine.

[9]  A. Tversky Features of Similarity , 1977 .

[10]  A. Ziv,et al.  Simulation-based medical education: an ethical imperative. , 2006, Simulation in healthcare : journal of the Society for Simulation in Healthcare.

[11]  T. Goldsmith,et al.  Assessing structural similarity of graphs , 1990 .

[12]  William Winn,et al.  Research into Practice: Current Trends in Educational Technology Research: The Study of Learning Environments , 2002 .

[13]  F Sturmans,et al.  Assessment of the performance of general practitioners by the use of standardized (simulated) patients. , 1991, The British journal of general practice : the journal of the Royal College of General Practitioners.

[14]  Timothy E. Goldsmith,et al.  Structural knowledge assessment: comparison of referent structures , 1994 .

[15]  Eric B. Bass,et al.  Do Clerkship Directors Think Medical Students Are Prepared for the Clerkship Years? , 2004, Academic medicine : journal of the Association of American Medical Colleges.

[16]  Scott Lozanoff,et al.  Integration of advanced technologies to enhance problem-based learning over distance: Project TOUCH. , 2003, Anatomical record. Part B, New anatomist.

[17]  Timothy E. Goldsmith,et al.  Locus of the Predictive Advantage in Pathfinder-Based Representations of Classroom Knowledge. , 1994 .

[18]  C. Griffith,et al.  A Clinical Performance Exercise for Medicine—Pediatrics Residents Emphasizing Complex Psychosocial Skills , 2001, Academic medicine : journal of the Association of American Medical Colleges.

[19]  S. Kay On the Nature of Expertise. , 1992 .

[20]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[21]  Gerald A. Higgins,et al.  Meta-analysis and Planning of SIMTRAUMA: Medical Simulation for Combat Trauma , 2000 .

[22]  Marcus F Keep,et al.  Distributed interactive virtual environments for collaborative experiential learning and training independent of distance over Internet2. , 2004, Studies in health technology and informatics.

[23]  Elizabeth Kachur,et al.  Guidelines for Preclerkship Bioterrorism Curricula , 2004, Academic medicine : journal of the Association of American Medical Colleges.

[24]  Richard J. Shavelson,et al.  CONSTRUCT VALIDATION: METHODOLOGY AND APPLICATION TO THREE MEASURES OF COGNITIVE STRUCTURE , 1975 .

[25]  L. Kohn,et al.  COMMITTEE ON QUALITY OF HEALTH CARE IN AMERICA , 2000 .

[26]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[27]  D. Spalding The Principles of Psychology , 1873, Nature.

[28]  Geoff Norman,et al.  Innovative Simulations for Assessing Professional Competence: From Paper and Pencil to Virtual Reality , 2000 .

[29]  S. Woolgar,et al.  Representation in Scientific Practice , 1990 .

[30]  Scott T. Meier,et al.  Improving Design Sensitivity through Intervention-Sensitive Measures , 2004 .

[31]  P. Feltovich,et al.  A survey of medical school teachers to identify basic biomedical concepts medical students should understand , 1990, Academic medicine : journal of the Association of American Medical Colleges.

[32]  Michelene T. H. Chi,et al.  Expertise in Problem Solving. , 1981 .

[33]  W. McGaghie,et al.  Simulation technology for health care professional skills training and assessment. , 1999, JAMA.

[34]  Stanley Saiki,et al.  Distributed interactive virtual environments for collaborative medical education and training: design and characterization. , 2004, Studies in health technology and informatics.

[35]  G. Loyd,et al.  Practical Health Care Simulations , 2004 .

[36]  D. Kolb Experiential Learning: Experience as the Source of Learning and Development , 1983 .

[37]  J. Rethans,et al.  Assessment of practicing family physicians: comparison of observation in a multiple-station examination using standardized patients with observation of consultations in daily practice. , 1999, Academic medicine : journal of the Association of American Medical Colleges.

[38]  J. Kruskal Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[39]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[40]  Richard J. Shavelson,et al.  Comparison of Content Structure and Cognitive Structure in High School Students' Learning of Probability. , 1975 .

[41]  Marlene Scardamalia,et al.  Process and Product in PBL Research , 2000 .

[42]  Scott Lozanoff,et al.  Virtual patient simulator for distributed collaborative medical education. , 2003, Anatomical record. Part B, New anatomist.