Reference
Paper
Modeling Eye Gaze Patterns in
Clinician-Patient Interaction With Lag Sequential Analysis
Enid Montague, Jie Xu,
Ping-yu Chen, Onur Asan, Bruce P. Barrett, and Betty Chewning
(Digital
Object Identifier: 10.1177/0018720811405986)
Overview of the Paper
Non-verbal
communication is very important in Clinician-Patient interaction. It is a very
important aspect which may affect patient outcomes. In this paper the authors
tried to examine whether lag sequential analysis could be used to describe the
eye gaze orientation of the clinician and patient in a medical settings. This
study topic is especially important, because new technologies are implemented
in multiuser settings where trust is a very critical point. And, nonverbal cues
are very critical to achieve the trust. So, this study method can be used in
future technologies.
Study
showed that, during health care encounters, if there was a moderate level of
mutual gaze between the clinician and the patient, then patient perceived it as
empathy, clinician’s interest in patient and warmth. Also, positive
interactions and communication of the patient were correlated with the measure
of satisfaction of the patients. In this study, the authors try to find out the
answers of two research questions:
1. How was the clinician’s gaze
related to the patient’s gaze?
a.
Did
the patient follow where the clinician gazed?
b.
If
so, what was the timing of this behavior relative to the clinician’s behavior?
2. How was the patient’s gaze
related to the clinician’s gaze?
a.
Did
the clinician follow where the patient gazed?
b.
If
so, what was the timing of this behavior relative to the patient’s behavior?
Evaluation and Validity of the Paper
In
this study, the eye gaze behavior of clinicians and patients were recorded. They
recorded 110 videos in medical encounters. Then they analyzed the eye gaze
behavior by lag sequential analysis method to find out significant behavior. They
performed both event-based and time-based lag sequential analysis in there
study. They performed the event-based lag sequential analysis to check whether
there was any event lag between their behaviors. And, the time-based lag
sequential analysis was to find out the time lag between the events, if there
was any.
From
there event-based lag analysis, the authors found that the patient’s gaze
followed gaze of clinicians. But, the clinician’s gaze did not follow patient’s
gaze. And, the time-based lag analyses showed that, the patient’s behavior
usually followed clinician’s behavior within 2s of the initial behavior.
Improvement Scopes
One
extension of this paper will be to implement an automated gaze detection method
in their system. In their experiment, the authors used human coders to code the
gaze of clinicians and also patients. From the videos it is sometimes very hard
for the human coders to detect the gaze perfectly, which may lead to some
erroneous result.
Further Reading
One of the interesting articles, which are cited by this paper, is “Meaning of
five patterns of gaze”, by Michael Argyle, Luc Lefebvre, and Mark Cook [2] (Digital
Object Identifier: 10.1002/ejsp.2420040202).
In this paper, the authors used a conversation-setting where the participants
were talked with different trained confederates, who displayed five patterns of
gaze with different subjects.
The gaze patters were: zero, looking while talking, looking while listening,
normal and continuous. Then, after the conversation the participants rated the
confederates. The authors wanted to see how the scores of the confederate
depended on the gaze patterns.
References
[1]
E. Montague, J. Xu, P. Chen, O. Asan, B. Barrett, and B. Chewning, “Modeling
eye gaze patterns in clinician–patient interaction with lag sequential
analysis,” Human Factors: The Journal of the Human Factors and Ergonomics
Society, vol. 53, no. 5, pp. 502–516, 2011.
[2]
M. Argyle, L. Lefebvre, and M. Cook, “The meaning of five patterns of gaze,”
European journal of social psychology, vol. 4, no. 2, pp. 125–136, 1974.
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