Gathering Information and Engaging the User C om B ot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions (2021)

Abstract: We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.

## introduction current work on human-machine interaction focuses on three main types of dialogs: task-based, open domain and question answering conversational dialogs. the goal of task-based models is to gather the information needed for a given task e.g., gathering the price, location and type of a restaurant needed to recommend this restaurant. usually trained on social media data @xcite ) @xcite , open domain conversational models aim to mimick open domain conversation between two humans. finally, question answering conversational models seek to model dialogs where a series of inter-connected questions is asked about a text passage. in this paper, we consider dialog models in the context of clinical studies i.e., dialog models which are used to collect the information needed by the medical body to assess the impact of the clinical trial on a cohort of patients (e.g., information about their mood, their activity, their sleeping patterns). in the context of these clinical studies, the goal of the dialog model is two-fold. a first goal is to collect a set of pre-defined data points i.e., answers to a set of pre-defined questions specified in a questionnaire. a second goal is to gather relevant serendipitous information i.e., health related information that is not addressed by the questionnaire but that is provided by the user during the interaction and which may be relevant to understand the impact of the therapy investigated by the clinical study. this requires keeping the user engaged and prompting him/her with relevant follow-up questions. to model these three goals (collecting a predefined set of data points, keeping the user engaged and gathering more informal information about the state of the patient), we introduce an ensemble model which combines three bots: a task-based bot (medbot) whose goal is to collect information about the mood, the daily life, the sleeping pattern, the anxiety level and the leisure activities of the patients; a follow-up bot (followupbot) designed to extend the task-based exchanges with health-related, follow-up questions based on the user input; and an empathy bot (empathybot) whose task is to reinforce the patient engagement by providing empathetic and socially driven feedback. our work makes the following contributions. ## related work several approaches have explored the use of ensemble models for dialog. while @xcite proposed an ensemble model for human-machine dialog which combines a generative and a retrieval model, further ensemble models for dialog have focused on combining agents/bots designed to model different conversation strategies. @xcite focus on open domain conversation and combines three agents, two to improve dialog coherence (ensuring that pronouns can be resolved and maximising semantic similarity with the current context) and one to handle topic switch (moving to a new topic when the retrieval confidence score is low). the alana ensemble model @xcite , developed for the amazon alexa challenge i.e., for open domain chitchat, combines domain specific bots used to provide information from different sources with social bots to smooth the interactions (by asking for clarification, expressing personal views or handling profanities). similarly, @xcite introduces a dialog model which interleaves a social and a task-based bot. conversely, @xcite showed that success of interleaving depends on the context and that in a public setting, users either prefer purely task-based systems or fail to see a difference between task-based and a richer ensemble model combining task-based and social bots. our work differs from these previous approaches in that we combine a standard, task-based model with both a social bot and a domain specific, followup bot. this allows both for more natural dialogs (by following up on the user input rather than systematically asking about an item in the predefined set of topics) and for additional relevant, health related information to be gathered. ## combot, an ensemble model for repeated task-based interactions we introduce the three bots making up our ensemble model and the ensemble model combining them. ## results and discussion we compare four models using automatic metric and absolute satisfaction scores. based on this first evaluation, we compare two of these models using the acute-eval human evaluation framework. we display an example dialog and discuss the respec-tive use of each bot in the combot model. ## conclusion a qualitative analysis of the collected dialogs indicates several directions for further research. negation is often not recognised leading to interactions in which the model continues discussing a topic which was declared as irrelevant by the user. another difficulty is knowing when to end the conversation. long ones are good to complete the task, but bad for people who are ready to finish conversation but feel forced to continue. to improve user engagement, a possibility would be to explore whether the information provided by sentiment analysers could be exploited to help maintain a positive interaction. by detecting polarity, it could also help improve negation handling. another key issue concerns the emotional impact of the dialog on the user. an interaction with the bot might highlight a health issue the user was not aware of resulting in increased user stress. in such a situation, a good policy would be to provide the user with some notion of solution, some piece of information or advice which can help her face the situation and if possible, incite her to act to improve her health. indeed some of the dialogs collected with combot show that users sometimes ask for help. here a knowledge-based agent could be useful either to provide facts that are related to the topic at hand or to highlight the connections between facts that have been mentioned in the dialog.