TY - GEN
T1 - Echo
T2 - 8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
AU - Thomas, Manu Mathew
AU - Kannampallil, Thomas
AU - Abraham, Joanna
AU - Marai, G. Elisabeta
N1 - Funding Information:
This work has been partially supported by UIC College of Engineering Award "Collaborative Dashboard for Patient Care Transitions", and by National Science Foundation Awards NSF IIS-1541277 and NSF CNS-1625941. We gratefully acknowledge the support of the Electronic Visualization Lab, in particular J. Trelles, A. Burks, and L. Long, and the generous assistance of Prof. Min J. Joo, University of Illinois Hospital.
Funding Information:
This work has been partially supported by UIC College of Engineering Award “Collaborative Dashboard for Patient Care Transitions”, and by National Science Foundation Awards NSF IIS-1541277 and NSF CNS-1625941. We gratefully acknowledge the support of the Electronic Visualization Lab, in particular J. Trelles, A. Burks, and L. Long, and the generous assistance of Prof. Min J. Joo, University of Illinois Hospital.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/15
Y1 - 2018/6/15
N2 - One of the significant challenges of care transitions in Intensive Care Units (ICUs) is the lack of effective support tools for outgoing clinicians to find, filter, organize, and annotate information that can be effectively handed off to the incoming team. We present a large display interactive multivariate visual approach, aimed towards supporting clinicians during the transition of care. We first provide a characterization of the problem domain in terms of data and tasks, based on an observation session at the University of Illinois Hospital, and on interviews with several biomedical researchers and ICU clinicians. Informed by this experience, we design a scalable, interactive visual approach that supports both overview and detail views of ICU patient data, as well as anomaly detection, comparison, and annotation of the data. We demonstrate a large-display implementation of the visualization on an existing anonymized ICU dataset. Feedback from domain experts indicates this approach successfully meets the requirements of effective care transitions.
AB - One of the significant challenges of care transitions in Intensive Care Units (ICUs) is the lack of effective support tools for outgoing clinicians to find, filter, organize, and annotate information that can be effectively handed off to the incoming team. We present a large display interactive multivariate visual approach, aimed towards supporting clinicians during the transition of care. We first provide a characterization of the problem domain in terms of data and tasks, based on an observation session at the University of Illinois Hospital, and on interviews with several biomedical researchers and ICU clinicians. Informed by this experience, we design a scalable, interactive visual approach that supports both overview and detail views of ICU patient data, as well as anomaly detection, comparison, and annotation of the data. We demonstrate a large-display implementation of the visualization on an existing anonymized ICU dataset. Feedback from domain experts indicates this approach successfully meets the requirements of effective care transitions.
KW - ICU care transition
KW - collaborative decision making
KW - large display visualization
KW - shared cognition
UR - http://www.scopus.com/inward/record.url?scp=85039047025&partnerID=8YFLogxK
U2 - 10.1109/VAHC.2017.8387500
DO - 10.1109/VAHC.2017.8387500
M3 - Conference contribution
AN - SCOPUS:85039047025
T3 - 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
SP - 47
EP - 54
BT - 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 October 2017
ER -