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

Attributes of Conventional Virtual PatientSource: http://research.bidmc.harvard.edu/VPtutorials/default.htm

•Information presented to user through video or text• Best for training clinical reasoning/decision-making skills (Cook & Triola 2009)

Picture/video of patientNavigation

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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

Virtual Patient Framework

Speech Recognition

Signal Processing

(tone, inflection)

Key word Processing (learning

algorithm)

Evaluation/ Coding of Context in

Communication Model

Selection of Responses

(emotion, non-verbal, verbal

Communication Analysis for

Learner Feedback

VP Output

Learning Objective, Scenario

Development

INPUT

Video

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

Toward Development of Objective Measures

Communication models

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

• Roter Interaction Analysis System (RIAS)

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

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).

RIAS: Socioemotional Exchange

Personal remarks, social conversation

Laughs, tells jokes Shows approvalGives complimentShows agreement or

understanding Empathy Shows concern or worry

Shows concern or worry Reassures, encourages

or shows optimismLegitimizes Partnership Self-Disclosure Shows disapprovalShows criticismAsks for reassurance

RIAS: Task-Focused Exchange

Transition words Gives orientation,

instructionsParaphrase/Checks for

understanding Bid for repetitionRequests for servicesAsks for understandingAsks for opinion

Asks questions Closed/open-endedMedical conditionTherapyLifestylePsychosocial-Feelings

Gives informationCounsels or directs

behavior

Design the Technology to Match the Communication Requirements

Design the system to 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

Analysis Components

Affective distance

Adjusting to feedback

Interactivity

Tasks

Shared understanding

Contextualized content

Explicit directions

Goals

Cognitive distance

Example: Initial Primary Care VisitAdjusting 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.

Analysis Components

Affective distance

Adjusting to feedback

Interactivity

Tasks

Shared understanding

Contextualized content

Explicit directions

Goals

Cognitive distance

Example: Surgery ConsultShared 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.

Analysis Components

Affective distance

Adjusting to feedback

Interactivity

Tasks

Shared understanding

Contextualized content

Explicit directions

Goals

Cognitive distance

Example: Post-Surgical Follow-UpMessage 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.

Analysis Components

Affective distance

Adjusting to feedback

Interactivity

Tasks

Shared understanding

Contextualized content

Explicit directions

Goals

Cognitive distance

Example: Post-Surgical Follow-UpCognitive 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.

Analysis Components

Affective distance

Adjusting to feedback

Interactivity

Tasks

Shared understanding

Contextualized content

Explicit directions

Goals

Cognitive distance

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

Example from Culture Training

Hardware/Software

•Prototype Proof of Concept

•Haptek SDK and body models from Haptek•Free speech recognition – Microsoft Speech•Free synthetic speech generation•JAVA

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.• Ipisoft and Playstation video cameras (6) for creating natural

body movements into characters.

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)

Thank You!

Questions?

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., Vliet Vlieland, T., & Zwetsloot-Schonk, J. (2004). The impact of ICT on communication in healthcare. Proceedings of MEDINFO’04