IDMVis: Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support

TLDR

Type 1 diabetes is a chronic autoimmune disease that requires intensive management to lower blood glucose, yet patients’ manual logs and device data are presented in disparate visualizations that hinder clinicians’ ability to infer temporal patterns. The study introduces IDMVis, a visualization tool built on a new data abstraction and hierarchical task framework to support temporal event sequence analysis in type 1 diabetes care. Over an 18‑month design study, the authors developed IDMVis, which folds and aligns patient records by dual sentinel events and scales intermediate timelines, and validated the design through domain abstractions, best practices, and a qualitative evaluation with six clinicians. Clinicians reported that IDMVis accurately reflects their workflow, enabling them to detect data quality problems, reconstruct missing records, distinguish daily patterns, and facilitate educational interventions.

Abstract

Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans in which the body stops producing insulin and blood glucose levels rise. The goal of intensive diabetes management is to lower average blood glucose through frequent adjustments to insulin protocol, diet, and behavior. Manual logs and medical device data are collected by patients, but these multiple sources are presented in disparate visualization designs to the clinician—making temporal inference difficult. We conducted a design study over 18 months with clinicians performing intensive diabetes management. We present a data abstraction and novel hierarchical task abstraction for this domain. We also contribute IDMVis: a visualization tool for temporal event sequences with multidimensional, interrelated data. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. We validate our design decisions based on our domain abstractions, best practices, and through a qualitative evaluation with six clinicians. The results of this study indicate that IDMVis accurately reflects the workflow of clinicians. Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.