Follow-up Question Generation For Enhanced Patient-Provider Conversations

Sarah Masud Preum, Joseph Gatto, Parker Seegmiller

Abstract:

Follow-up question generation is an essential
feature of dialogue systems as it can reduce
conversational ambiguity and enhance modeling complex interactions. Conversational contexts often pose core NLP challenges such as (i)
extracting relevant information buried in fragmented data sources, and (ii) modeling parallel
thought processes. These two challenges occur
frequently in medical dialogue as a doctor asks
questions based not only on patient utterances
but also their prior EHR data and current diagnostic hypotheses. Asking medical questions inasynchronous conversations compounds these
issues as doctors can only rely on static EHR
information to motivate follow-up questions.
To address these challenges, we introduceFollowupQ, a novel framework for enhancing asynchronous medical conversation. FollowupQ is a multi-agent framework that processes patient messages and EHR data to generate personalized follow-up questions, clarifying patient-reported medical conditions. FollowupQ reduces requisite provider follow-up
communications by 34%. It also improves performance by 17% and 5% on real and synthetic
data, respectively. We also release the first public dataset of asynchronous medical messages
with linked EHR data alongside 2,300 followup questions written by clinical experts for the
wider NLP research community.