Papers by Timothy E. Burdick
How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting (2026.acl-long)
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Parker Seegmiller, Joseph Gatto, Sarah E. Greer, Ganza Belise Isingizwe, Rohan Ray, Timothy E. Burdick, Sarah Masud Preum
| Challenge: | Large language models (LLMs) have been shown to be effective in drafting patient portal responses, yet their integration into clinical workflows raises various concerns. |
| Approach: | They propose a taxonomy of thematic elements in clinician responses and a framework for assessing clinician editing load of LLM-drafted responses at both content and theme levels. |
| Outcome: | The proposed framework assesses the editing load of LLM-drafted responses at both content and theme levels. |
Follow-up Question Generation For Enhanced Patient-Provider Conversations (2025.acl-long)
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Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Inas S. Khayal, Sarah DeLozier, Sarah Masud Preum
| Challenge: | Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. |
| Approach: | They propose a framework that generates personalized follow-up questions based on patient utterances and prior EHR data. |
| Outcome: | The framework reduces follow-up communications by 34% and improves performance by 17% and 5% on real and synthetic data. |