Virtual Patient for Training Doctors to Break Bad News

Magalie Ochs (1) , Brice Donval (2) , Philippe Blache (3)

1 LSIS - Laboratoire des Sciences de l'Information et des Systèmes
2 LTCI - Laboratoire Traitement et Communication de l'Information
3 LPL - Laboratoire Parole et Langage

The way doctors deliver bad news has a significant impact on the therapeutic process: disease evolution, adherence with treatment recommendations, litigation possibilities (Andrade et al., 2010). However, both experienced clinicians and medical trainees consider this task as difficult, daunting, and stressful. Nowadays, training health care professional to break bad news, recommended by the French Haute Autorité de la Santé (HAS), is organized as workshops during which doctors disclose bad news to actors playing the role of patient. In our project, we are developing an embodied conversational agent simulating a patient to train doctors to break bad news. The embodied conversational agent is incorporated in an immersive virtual reality environment (a CAVE) integrating several sensors to detect and recognize in real time the verbal and non-verbal behavior of the doctors interacting with the virtual patient. The virtual patient will adapt its behavior depending on the doctor's verbal and non-verbal behavior. The methodology used to construct the virtual patient behavior model is based on a quantitative and qualitative analysis of corpus of doctors training sessions.

Mots clés: Embodied conversational agent; Virtual reality; Virtual patient; Training platform

Domaines: Interface homme-machine [cs.HC]; Intelligence artificielle [cs.AI]; Sciences cognitives

Magalie Ochs, Brice Donval, Philippe Blache. Virtual Patient for Training Doctors to Break Bad News. Workshop, Affect, Compagnon Artificiel, Interaction, 2016, Brest, France. non paginé. ⟨hal-01462159⟩