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Designing a Virtual Patient for Communication Training

Develop a high-fidelity interactive Virtual Patient (VP) for training in clinical reasoning and decision-making skills, incorporating realistic appearance, speech recognition, natural conversational capabilities, emotion detection, and adaptive responses. The system aims to enhance communication skill performance, evaluate learner performance, and provide constructive feedback through a multidisciplinary framework.

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Designing a Virtual Patient for Communication Training

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  1. 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

  2. Attributes of Conventional Virtual Patient Source: http://research.bidmc.harvard.edu/VPtutorials/default.htm • Picture/video • of patient Navigation Menu • Information presented to user through video or text • Best for training clinical reasoning/decision-making skills (Cook & Triola 2009)

  3. ResearchGoals • 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

  4. Virtual Patient Framework Speech Recognition Evaluation/ Coding of Context in Communication Model INPUT Communication Analysis for Learner Feedback Learning Objective, Scenario Development Signal Processing (tone, inflection) VP Output Selection of Responses (emotion, non-verbal, verbal Key word Processing (learning algorithm)

  5. Video

  6. Process • 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

  7. Toward Development of Objective Measures • Communication models • Cognitive-Affective Model of Organizational Communication Systems (CAMOCS) • Roter Interaction Analysis System (RIAS)

  8. Cognitive-Affective Model of Organizational Communication Systems (CAMOCS) (Te’eni 2001) • Extensive research article cites 301 sources in business organizational communication • Main factors of communication complexity: inputs, process, impact

  9. Roter Interaction Analysis System (RIAS) • 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).

  10. RIAS: Socioemotional Exchange • Personal remarks, social conversation • Laughs, tells jokes • Shows approval • Gives compliment • Shows agreement or understanding • Empathy • Shows concern or worry • Shows concern or worry • Reassures, encourages or shows optimism • Legitimizes • Partnership • Self-Disclosure • Shows disapproval • Shows criticism • Asks for reassurance

  11. RIAS: Task-Focused Exchange • Transition words • Gives orientation, instructions • Paraphrase/Checks for understanding • Bid for repetition • Requests for services • Asks for understanding • Asks for opinion • Asks questions • Closed/open-ended • Medical condition • Therapy • Lifestyle • Psychosocial-Feelings • Gives information • Counsels or directs behavior

  12. Design the Technology to Match the Communication Requirements • Design thesystemto support the needed impact, goals, strategies, media characteristics, inputs and learning outcomes. • Representative case of Mr. Y and Dr. X: • 65-year-old white male with no significant past medical history • Coughing for 3 months (no fever, infection, chills) • Former smoker • Possible mass on chest x-ray

  13. Example: Initial Primary Care Visit • Adjusting to feedback in communicating a difficult diagnosis • Physician must be sensitive to body language and patient’s reactions to moderate how much information is delivered in the initial diagnosis. • For example, if Mr. Y dismisses the urgency of the news, Dr. X may give a more explicit explanation of why these tests are needed and why the timing of them is important.

  14. Example: Surgery Consult • Shared understanding • Need shared knowledge between participants to improve dialogue • Contextualized, explicit content • The surgeon may want to explicitly present treatment options, with various risks and percentages associated with them.

  15. Example: Post-Surgical Follow-Up • Message Goal • In a follow-up appointment, oncologist discovers that Mr. Y has not been getting all of his chemo pills; Mrs. Y had postponed a couple of doses because it was making her husband too sick. • When physicians want to instruct or influence difficult patients, they may want to use highly formal language with explicit instructions so that they can better convey the importance to the patient of a particular course of treatment.

  16. Example: Post-Surgical Follow-Up • Cognitive distance • A physician explaining a complex diagnosis to a patient with limited medical understanding will require more explicit explanations, more formal information, and probably multiple methods of presenting information (visual, verbal) for the patient to get then and to reference later.

  17. Continuing Work • 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

  18. Example from Culture Training

  19. Hardware/Software • Prototype Proof of Concept • Haptek SDK and body models from Haptek • Free speech recognition – Microsoft Speech • Free synthetic speech generation • JAVA

  20. Hardware/Software • 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. • IpisoftandPlaystationvideo cameras (6) for creating natural body movements into characters.

  21. Hardware/Software • 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)

  22. Thank You! • Questions?

  23. References • 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., VlietVlieland, T., & Zwetsloot-Schonk, J. (2004). The impact of ICT on communication in healthcare. Proceedings of MEDINFO’04

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