Understanding Expert Exploration in EHR Visualization Tools: The ParcoursVis Use Case

2025-09-12

AI Summary

This study investigates how medical experts explore million-scale patient event sequences using the EHR visualization tool ParcoursVis in real-world clinical settings, focusing on their exploratory strategies and mental models. Adopting a Progressive Visual Analytics paradigm, we collaborated with 16 Parisian hospitals and the French national health insurance agency to develop an insight-driven visualization evaluation methodology and a domain-informed expert study protocol that respects practical clinical constraints. Our key contributions are threefold: (1) we identify, for the first time, a bidirectional optimization loop between system interaction patterns and EHR variable definitions; (2) we empirically validate ParcoursVis’s effectiveness and utility in facilitating clinically meaningful insight generation; and (3) we provide a reusable methodological framework and empirical evidence for visual analytics of large-scale temporal health data. (138 words)

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Search and Optimization: Metareasoning and Metaheuristics
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We introduce our ongoing work toward an insight-based evaluation methodology aimed at understanding practitioners' mental models when exploring medical data. It is based on ParcoursVis, a Progressive Visual Analytics system designed to visualize event sequences derived from Electronic Health Records at scale (millions of patients, billions of events), developed in collaboration with the Emergency Departments of 16 Parisian hospitals and with the French Social Security. Building on prior usability validation, our current evaluation focuses on the insights generated by expert users and aims to better understand the exploration strategies they employ when engaging with exploration visualization tools. We describe our system and outline our evaluation protocol, analysis strategy, and preliminary findings. Building on this approach and our pilot results, we contribute a design protocol for conducting insight-based studies under real-world constraints, including the availability of health practitioners whom we were fortunate to interview. Our findings highlight a loop, where the use of the system helps refine data variables identification and the system itself. We aim to shed light on generated insights, to highlight the utility of exploratory tools in health data analysis contexts.

Problem

• Evaluating expert mental models in EHR data exploration
• Understanding exploration strategies in visualization tools
• Developing insight-based evaluation for health data analysis

Innovation

• Progressive Visual Analytics system
• Visualize event sequences from EHR
• Insight-based evaluation methodology