Designing a Virtual Patient for Communication Training

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					Designing a Virtual Patient for
  Communication Training
             April Barnes, M.S., Ph.D. Candidate1,2
           Jennifer Cloud-Buckner., Ph.D. Candidate1,2
                    Jennie Gallimore, Ph.D.1,2,3
                      Phani Kidambi, Ph.D.1
                Rosalyn Scott, M.D., M.S.H.A.1,2,3
      Ohio Center of Excellence in Human-Centered Innovation1
 Department of Biomedical, Industrial, and Human Factors Engineering2
        Department of Surgery, Boonshoft School of Medicine3
      Wright State University, Dayton, OH, USA
         Source: http://research.bidmc.harvard.edu/VPtutorials/default.htm




                                                                                    Picture/video
                      Navigation                                                    of patient
                      Menu




•Information presented to user through video or text
•Best for training clinical reasoning/decision-making skills (Cook & Triola 2009)
•Develop high–fidelity, interactive VP
  •Realistic appearance (3D, animated, full body, non-verbal
  behavior)
  •Speech recognition
    •Natural, conversational capability
  •Animated facial expressions, gestures
  •Adaptive responses
  •Emotion Detection
• Develop training related to communication skill
performance
  •Evaluate learner performance
  •Provide constructive feedback
               INPUT


                Speech        Evaluation/     Communication
              Recognition      Coding of        Analysis for
                               Context in         Learner
                            Communication        Feedback
                                Model


  Learning      Signal
 Objective,   Processing
  Scenario      (tone,
Development   inflection)
                              Selection of      VP Output
                               Responses
                            (emotion, non-
                             verbal, verbal
               Key word
              Processing
               (learning
              algorithm)
• Multidisciplinary team of subject-matter
  experts and experienced clinicians
• Extensive literature review
• Observation of real SP training and
  performance for iterative VP design
• Prototype VP
  •   Most software is Freeware
  •   Speech recognition
  •   Script matching based on keyword in user query
  •   Randomly selects 1 of 3 responses to each question
• Develop rubric for performance evaluation
Communication models

 • Cognitive-Affective Model of Organizational
   Communication Systems (CAMOCS)

 • Roter Interaction Analysis System (RIAS)
• Extensive
  research article                    Process
  cites 301                         goals, media,
  sources in                         strategies,
                      Inputs        message form       Impact
  business                                              mutual
  organizational task, distance,                    understanding,
                   values, norms
  communication                                      relationship
• Main factors of
  communication
  complexity:                      Communication
  inputs,                            Complexity
  process,
  impact
• Commonly used measurement framework of
  healthcare communication
• Classifies task-focused communication and social-
  emotional communication
• Coding scheme for video or audio of physician-
  patient interactions
• Utterances divided into >40 classifications, plus 12
  global dimensions of socio-emotional affect
(D. Roter, 2006; D. Roter & Larson, 2002).
Personal remarks, social   Shows concern or worry
  conversation             Reassures, encourages
Laughs, tells jokes          or shows optimism
Shows approval             Legitimizes
Gives compliment           Partnership
Shows agreement or         Self-Disclosure
  understanding            Shows disapproval
Empathy                    Shows criticism
Shows concern or worry     Asks for reassurance
Transition words         Asks questions
Gives orientation,         Closed/open-ended
  instructions             Medical condition
Paraphrase/Checks for      Therapy
  understanding            Lifestyle
Bid for repetition         Psychosocial-Feelings
Requests for services    Gives information
Asks for understanding   Counsels or directs
Asks for opinion           behavior
Design the system to support the
  needed impact, goals, strategies,
  media characteristics, inputs and       Analysis Components
  learning outcomes.
                                          Affective distance
                                          Adjusting to feedback
Representative case of Mr. Y and Dr. X:
                                          Interactivity
                                          Tasks
  65-year-old white male with no
    significant past medical history      Shared understanding
  Coughing for 3 months (no fever,        Contextualized content
    infection, chills)                    Explicit directions
  Former smoker                           Goals
  Possible mass on chest x-ray
                                          Cognitive distance
Adjusting to feedback in
  communicating a difficult
  diagnosis
                                        Analysis Components
Physician must be sensitive to body     Affective distance
  language and patient’s reactions to   Adjusting to feedback
  moderate how much information is      Interactivity
  delivered in the initial diagnosis.
                                        Tasks
For example, if Mr. Y dismisses the
                                        Shared understanding
  urgency of the news, Dr. X may give
                                        Contextualized content
  a more explicit explanation of why
  these tests are needed and why the    Explicit directions
  timing of them is important.          Goals
                                        Cognitive distance
Shared understanding
Need shared knowledge between
 participants to improve dialogue            Analysis Components
                                             Affective distance
                                             Adjusting to feedback
Contextualized, explicit content             Interactivity
The surgeon may want to explicitly present   Tasks
 treatment options, with various risks and
                                             Shared understanding
 percentages associated with them.
                                             Contextualized content
                                             Explicit directions
                                             Goals
                                             Cognitive distance
Message Goal

• In a follow-up appointment, oncologist      Analysis Components
  discovers that Mr. Y has not been getting
                                              Affective distance
  all of his chemo pills; Mrs. Y had
  postponed a couple of doses because it      Adjusting to feedback
  was making her husband too sick.            Interactivity
• When physicians want to instruct or         Tasks
  influence difficult patients, they may      Shared understanding
  want to use highly formal language with
  explicit instructions so that they can      Contextualized content
  better convey the importance to the         Explicit directions
  patient of a particular course of           Goals
  treatment.
                                              Cognitive distance
Cognitive distance

• A physician explaining a complex       Analysis Components
  diagnosis to a patient with limited    Affective distance
  medical understanding will require     Adjusting to feedback
  more explicit explanations, more       Interactivity
  formal information, and probably       Tasks
  multiple methods of presenting         Shared understanding
  information (visual, verbal) for the
                                         Contextualized content
  patient to get then and to reference
  later.                                 Explicit directions
                                         Goals
                                         Cognitive distance
• Conduct study: comparison of training with SP
  alone to training with VP and SP
• Measures: same used to evaluate performance
  using SP
• Move from prototype to build a VP in a gaming
  environment with more realistic non-verbal
  movements
• Development of the virtual human is being
  created in an Army project to develop learning
  for cross-cultural competencies focusing on non-
  verbal behaviors
•Prototype Proof of Concept

 •Haptek SDK and body models from Haptek
 •Free speech recognition – Microsoft Speech
 •Free synthetic speech generation
 •JAVA
•New System Under Development
 • Unreal Tournament SDK game engine for virtual
   environment
 • Stereoscopic 3D display
 • Maya 3D object editing software for body and object
   creation
 • FaceFX for visual expressions and matching speech
   phonemes with mouth movements.
 • Custom creation of different looks and custom developed
   facial action movements not available in FaceFX.
 • Natural Speaking Professional for speech recognition.
 • Ipisoft and Playstation video cameras (6) for creating natural
   body movements into characters.
•Future adds
  •Learning software development for interpreting
  speech and providing feedback vs discrete
  scripted feedback.
  •Measures of learner interaction
    •Eye tracking (when not using 3D stereo)
    •Face tracking
    •Speech context
    •Emotion detection (facial and verbal)
Thank You!

 Questions?
•   Accreditation Council for Graduate Medical Education (ACGME) (2005). Advancing Education in Interpersonal and
    Communication Skills: An educational resource from the ACGME Outcome Project. Retrieved from
    http://www.acgme.org/outcome/implement/interperComSkills.pdf.
•   Association of American Medical Colleges. (1999). Contemporary Issues in Medicine: Communication in Medicine.
    Report 3 of the Medical School Objectives Project. Washington, DC
•   Cook, D.A. & Triola, M. M. (2009). Virtual patients: a critical literature review and proposed next steps. Medical
    Education, 43(4), 303-311.
•    Stone, M. & Silen, W. A 50 year-old woman with Lower Abdominal Pain. Retrieved from
    http://research.bidmc.harvard.edu/VPtutorials/default.htm.
•   Issenberg, S.B., McGaghie, W.C., Petrusa, E.R., Gordon, D.L. & Scalese, R.J. (2005). Features and uses of high-fidelity
    medical simulations that lead to effective learning: a BEME systematic review*. Medical Teacher, 27(1), 10-28.
•   Makoul , G. (2001). Essential elements of communication in medical encounters: the Kalamazoo consensus statement.
    Academic Medicine. 76:390-393.
•   Paul, D. L. (2006). Collaborative activities in virtual settings: A knowledge management perspective of telemedicine.
    Journal of Management Information Systems, 22(4), 143-176.
•   Roter, D., & Larson, S. (2002). The Roter Interaction Analysis System (RIAS): Utility and flexibility for analysis of
    medical interactions. Patient Education and Counseling, 46(4), 243-251.
•   Roter, D. (2006). The Roter Method of Interaction Process Analysis. Retrieved May 1, 2009, from
    http://rias.org/manual.pdf
•   Smothers, V., Azan, B., Ellaway, R.(2010). MedBiquitous Virtual Patient Specifications and Description Document.
    Retrieved from http://www.medbiq.org/working_groups/virtual_patient/VirtualPatientDataSpecification.pdf
•    Stone, M. & Silen, W. A 50 year-old woman with Lower Abdominal Pain. Retrieved from
    http://research.bidmc.harvard.edu/VPtutorials/default.htm.
•   Te'eni, D. (2001). Review: A cognitive-affective model of organizational communication for designing IT. MIS Quarterly,
    25(2), 251-312.
•   Toussaint, P., Verhoef, J., Vliet Vlieland, T., & Zwetsloot-Schonk, J. (2004). The impact of ICT on communication in
    healthcare. Proceedings of MEDINFO’04

				
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posted:11/30/2012
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