In this paper, we report the design and evaluation of an automated virtual counselor,
that provides multidimensional adaptive breast cancer genetic counseling to women.
Genetic counseling is essential to cancer prevention and treatment.
For women who may be at high risk for hereditary breast cancer,
getting genetic counseling, understanding their risk, and adhering to the recommended medical
guidelines such as doing regular screening, is the most effective way for cancer prevention,
and for getting better treatment if they do get cancer.
However, there are several problems in the current practice that motivate our research.
First, most people, even those at high risk for cancer,
do not always have access to effective human genetic counseling.
Second, information regarding cancer genetic risk is very difficult to understand.
And last, poor comprehension may lead to low adherence rates
to the recommended cancer screening and prevention guidelines.
Prior work has shown that pedagogical embodied conversational agents
can be very effective when applied to tutoring and health education.
They are more intuitive and accessible when compared with chatbots or other
traditional interfaces, particularly with individuals of limited health literacy.
Therefore, we propose to use a pedagogical ECA to provide automated breast cancer
genetic counseling, offering both adaptive risk education, and adaptive adherence motivation,
to motivate users to follow the applicable medical guidelines.
Here is a video showing the implemented adaptive virtual counselor.
This is a fully automated web based system.
So welcome, today I want to talk to you about breast cancer,
especially breast cancer which is inherited or happens within families,
and we will discuss if you might be at risk for hereditary breast cancer.
First, let me give you an overview of some of the topics I'm going to cover today.
I would like to first talk about breast cancer, and in particular hereditary breast cancer.
Then I will talk about genes and gene mutations that are related to hereditary breast cancer.
The entire session should take about 30 minutes, I hope that's okay with you.
As you can see in the video, users can talk with the virtual counselor
Tanya by clicking on the buttons shown on the side of the screen.
And Tanya is capable of showing different types of visuals, using different types of hand gestures,
and she’s also able to collect user responses through different UI widgets, such as a slider.
Our virtual counselor design adopts the standard architecture of an intelligent tutoring system.
First, it has a domain knowledge model that contains the educational content
required to teach breast cancer genetic risk and the applicable medical guidelines.
Second, a user model that maintains a dynamic model of the users’ knowledge state,
their preferred information processing method, and other user traits.
Third, a pedagogical model that chooses the virtual
counselor’s teaching strategies based on variables stored in the user model.
And last, an embodied conversational agent interface that simulates face-to-face counseling.
This diagram here illustrates the proposed framework
for adaptive breast cancer genetic counseling.
First, the virtual counselor offers educational modules related to breast cancer risk education.
It also provides personalized risk calculation based on a user’s age, gender, and risk level.
The counseling dialogues also cover topics motivating users
to follow the recommended medical guidelines, including modules about genetic testing,
screening guidelines, or breast cancer risk factors.
This framework obtains user evaluations both before and during the interaction.
The user model dynamically updates the user’s knowledge state, and also stores other variables,
such as risk level, health literacy, user preferences,
and preferred information processing method.
The pedagogical model then tailors its teaching
strategies based on variables stored in the user model.
The design of the pedagogical model is informed by intelligent tutoring system theories,
risk communication principles,
and the Heuristic-Systematic Model of information processing.
We conducted a between-subject evaluation study with 30 women,
comparing our adaptive virtual counselor to a non-adaptive counselor and a control one.
In the adaptive condition, participants interacted with the adaptive virtual counselor.
Adaptations were implemented on multiple dimensions,
including user comprehension, information processing method, and other user traits.
Everyone in this condition received different counseling based on these tailoring factors.
In the non-adaptive condition, participants interacted with a similar virtual counselor,
although without any of the adaptation mechanisms.
Everyone had the same conversation with the virtual counselor.
In the last control condition, participants were shown educational videos and brochures
modified from standard patient education materials.
All participants first completed questionnaires for pre-treatment
assessment, then they were randomly assigned to one of the three study conditions
and had a single counseling session for about 30 minutes.
After the interaction, participants completed additional questionnaires for post-treatment
assessment, and a semi-structured interview was conducted at the end.
We have two hypotheses for this study.
First, genetic counseling with an adaptive counselor will lead to greater knowledge gain.
And second, counseling with an adaptive virtual counselor will also lead to
higher intention to adhere to the recommended medical guidelines.
Next I will discuss our main findings.
As a manipulation check, participants’ perceived adaptation was measured after the interaction.
A one-way ANOVA and post hoc analysis showed that participants in the adaptive condition rated
significantly higher than participants in the non-adaptive and the control, meaning participants
indeed felt the adaptive condition was more personalized compared to the other conditions.
Participants’ breast cancer genetics knowledge
were measured both before and after the interaction.
We conducted a repeated measures ANOVA and found a significant interaction effect,
meaning knowledge gain in the three conditions were not the same.
Based on post hoc analysis, before the interaction,
participants’ knowledge were not significantly different
across the three conditions, all participants had the same starting point.
But after the interaction, participants in the adaptive condition scored the highest.
The non-adaptive condition came in the middle, and participants in control scored the lowest.
This showed that interacting with the adaptive counselor
did lead to the highest knowledge gain, which supported our first hypothesis.
Similarly, after the interaction,
participants scored the highest for medical guidelines knowledge in the adaptive condition,
which was significantly higher than scores in the control condition based on post hoc analysis.
To conclude…
Overall, our adaptive virtual counselor was well accepted and
liked by participants in the adaptive condition.
Our first hypothesis was supported,
counseling with an adaptive counselor did lead to the highest knowledge gain.
However, we did not find evidence to support our second hypothesis.
We discussed potential explanations and future directions in the paper.
Our work extends traditional intelligent tutoring system
design by incorporating adaptation mechanisms on multiple dimensions.
We demonstrate the effectiveness of the proposed framework,
which may accommodate users with diverse needs such as a more complicated risk profile.
Our automated approach provides a personalized,
easily accessible alternative to human genetic counseling.
Potentially this approach may reach more underserved populations
who tend to have lower health literacy and also less resources.
Thank you for watching! And you may contact us if you have any questions.