Effective doctor-patient communication is a crucial element of health care, influencing patients’ personal and medical outcomes following the interview.
The set of skills used in interpersonal interaction is complex, involving verbal
and non-verbal behaviour. Precise attributes of good non-verbal behaviour
are difficult to characterise, but models and studies offer insight on relevant
factors. In this PhD, I studied how the attributes of non-verbal behaviour can
be automatically extracted and assessed, focusing on turn-taking patterns of
and the prosody of patient-clinician dialogues.
I described clinician-patient communication and the tools and methods used to
train and assess communication during the consultation. I then proceeded to
a review of the literature on the existing efforts to automate assessment, depicting an emerging domain focused on the semantic content of the exchange
and a lack of investigation on interaction dynamics, notably on the structure of
turns and prosody.
To undertake the study of these aspects, I initially planned the collection of
data. I underlined the need for a system that follows the requirements of sensitive data collection regarding data quality and security. I went on to design a
secure system which records participants’ speech as well as the body posture
of the clinician. I provided an open-source implementation and I supported its
use by the scientific community.
I investigated the automatic extraction and analysis of some non-verbal components of the clinician-patient communication on an existing corpus of GP
consultations. I outlined different patterns in the clinician-patient interaction
and I further developed explanations of known consulting behaviours, such as
the general imbalance of the doctor-patient interaction and differences in the
control of the conversation.
I compared behaviours present in face to face, telephone, and video consultations, finding overall similarities alongside noticeable differences in patterns of
overlapping speech and switching behaviour.
I further studied non-verbal signals by analysing speech prosodic features, investigating differences in participants’ behaviour and relations between the assessment of the clinician-patient communication and prosodic features. While
limited in their interpretative power on the explored dataset, these signals
nonetheless provide additional metrics to identify and characterise variations
in the non-verbal behaviour of the participants.
Analysing clinician-patient communication is difficult even for human experts.
Automating that process in this work has been particularly challenging. I demonstrated the capacity of automated processing of non-verbal behaviours to analyse clinician-patient communication. I outlined the ability to explore new aspects, interaction dynamics, and objectively describe how patients and clinicians interact. I further explained known aspects such as clinician dominance
in more detail. I also provided a methodology to characterise participants’ turns
taking behaviour and speech prosody for the objective appraisal of the quality of non-verbal communication. This methodology is aimed at further use in
research and education
