A Visual Analytics Framework for Emergency Room Clinical Encounters

Zhiyuan Zhang, Faisal Ahmed, Bing Wang, Arun Nampally, IV Ramakrishnan, Asa Viccellio*, Rong Zhao, Klaus Mueller

Computer Science Department and Center of Excellence in Wireless and Information Technology (CEWIT), Stony Brook University
* Department of Emergency Medicine, Stony Brook University

High costs, lack of speed, non-intuitive interfaces, and inefficient, fragmented display of patient information have hindered the adoption of the Electronic Health Record (EHR, EMR). Critical factors inhibiting adoption of the EMR include the time spent by the health care providers in accessing and also documenting patient information during clinical encounters. We are developing an emerging visual analytics system dedicated to clinical encounters in emergency room scenarios. It unifies all EMR information fragments, such as current symptoms, history of present illness, previous treatments, available data, current medications, past history, family history, and others into a single interactive visual framework. Based on this information the physician can then follow through a medical diagnostics chain that includes requests for further data, diagnosis, treatment, follow-up, and eventually a report of treatment outcome. As patients often have rather complex medical histories, we believe that this visualization and visual analytics framework can offer large benefits for the navigation and reasoning with this information.

Overall framework

In our system, the patient is represented as a radial sunburst visualization that captures all health conditions of the past and present to serve as a quick overview to the interrogating physician. The patient’s body is represented as a stylized body map that can be zoomed into for further anatomical detail. On the other hand, the reasoning chain is represented as a multi-stage flow chart, composed of date, symptom, data, diagnosis, treatment, and outcome.

Watch these videos to get a quick overview: