Electronic health records (EHRs) have improved data accessibility but have also introduced cognitive burden for physicians, given the sheer volume and complexity of the data involved. Advances in large language models (LLMs) create new opportunities to rethink how clinicians interact with medical data through dynamic, adaptive interfaces. In this position paper, we explore how generative AI can support physicians' information needs by enabling more dynamic interactions with patient data. Through semi-structured interviews with internal physicians at Microsoft, we identify key challenges in data navigation and synthesis, and characterize clinicians' information needs during diagnostic workflows. We further examine how physicians conceptualize AI can help their work process and how these mental models shape expectations for interaction and trust. Based on these insights, we discuss design considerations for generative user interfaces that support clinician-centered workflows.
@article{arxiv.2604.21933,
title = {Not Another EHR: Reimagining Physician Information Needs with Generative AI Technology},
author = {Ruican Zhong and Jiachen Li and Gary Hsieh and David W. McDonald and Selin S. Everett and Alyssa Unell and Jonathan Carlson and Katie Claveau and Noel Codella and Khalil Malik and Scott Mackie and Eduardo Olvera and Scott Saponas and Eric Horvitz and David Rhew and Jim Weinstein and Jacob Gross and Amanda K. Hall},
journal= {arXiv preprint arXiv:2604.21933},
year = {2026}
}