TY - GEN
T1 - Augmenting EHR interfaces for enhanced nurse communication and decision making
AU - Chetta, Alessandro
AU - Carrington, Jane M.
AU - Forbes, Angus Graeme
N1 - Funding Information:
This work is funded in part by the National Institutes of Health, award #R01EB020395 (part of the NSF/NIH Smart and Connected Health Program).
Publisher Copyright:
© 2015 ACM.
PY - 2015/10/25
Y1 - 2015/10/25
N2 - The use of electronic health records (EHRs) in clinical environments provides new opportunities for nurses to integrate data analyses into their practice. While having access to these records has many benefits, the act of recording, retrieving, and analyzing this data can nonetheless introduce communication issues, as navigating and interpreting large amounts of heterogeneous data can be difficult, and conclusions can be hard to validate. In this paper, we describe a series of integrated visual interfaces to help nurses document and reason about patient data and about clinicians' understanding of patient data. The interfaces present the output of a predictive algorithm that makes use of historical EHR data, patient vital signs, and nurse handoff reports in order to classify a patient in terms of their likelihood of experiencing clinical events. Furthermore, the interfaces enable the nurses to quickly explore the original data and to examine other nurses' interpretation of patient activity during previous shifts. We present a series of usage scenarios that introduce our interactive visualization tools in the context of real-world healthcare situations.
AB - The use of electronic health records (EHRs) in clinical environments provides new opportunities for nurses to integrate data analyses into their practice. While having access to these records has many benefits, the act of recording, retrieving, and analyzing this data can nonetheless introduce communication issues, as navigating and interpreting large amounts of heterogeneous data can be difficult, and conclusions can be hard to validate. In this paper, we describe a series of integrated visual interfaces to help nurses document and reason about patient data and about clinicians' understanding of patient data. The interfaces present the output of a predictive algorithm that makes use of historical EHR data, patient vital signs, and nurse handoff reports in order to classify a patient in terms of their likelihood of experiencing clinical events. Furthermore, the interfaces enable the nurses to quickly explore the original data and to examine other nurses' interpretation of patient activity during previous shifts. We present a series of usage scenarios that introduce our interactive visualization tools in the context of real-world healthcare situations.
UR - http://www.scopus.com/inward/record.url?scp=84997418162&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84997418162&partnerID=8YFLogxK
U2 - 10.1145/2836034.2836038
DO - 10.1145/2836034.2836038
M3 - Conference contribution
AN - SCOPUS:84997418162
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 2015 Workshop on Visual Analytics in Healthcare, VAHC 2015 - in conjunction with IEEE VIS 2015
PB - Association for Computing Machinery
T2 - 2015 Workshop on Visual Analytics in Healthcare, VAHC 2015
Y2 - 25 October 2015
ER -