Not Another EHR: Reimagining Physician Information Needs with Generative AI Technology

TL;DR

This paper discusses how generative AI and large language models can transform physician interaction with electronic health records by addressing data complexity and cognitive load.

Contribution

It provides insights from physician interviews to inform design considerations for AI-driven, clinician-centered EHR interfaces.

Findings

Physicians face significant cognitive burden due to complex EHR data.

Clinicians see potential for AI to support data navigation and synthesis.

Designing AI interfaces requires understanding clinicians' mental models and workflow needs.

Abstract

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.