TY - GEN
T1 - A clinical decision support system for preventing adverse reactions to blood transfusion
AU - Murphree, Dennis H.
AU - Clifford, Leanne
AU - Lin, Yaxiong
AU - Madde, Nagesh
AU - Ngufor, Che
AU - Upadhyaya, Sudhindra
AU - Pathak, Jyotishman
AU - Kor, Daryl J.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/8
Y1 - 2015/12/8
N2 - During 2011 approximately 21 million blood components were transfused in the United States, with roughly 1 in 414 resulting in complication. For Americans, the two leading causes of transfusion-related death are the respiratory complications Transfusion-related acute lung injury (TRALI) and Transfusion-associated circulatory overload (TACO). Each of these complications results in significantly longer ICU and hospital stays as well as significantly greater rates of mortality. We have developed a set of machine learning models for predicting the likelihood of these adverse reactions in surgical populations. Here we describe deploying these models into a perioperative critical care environment via a continuous monitoring and alerting clinical decision support system. The goal of this system, which directly integrates our suite of machine learning models running in the R statistical environment into a traditional health information system, is to improve transfusion-related outcomes in the perioperative environment. By identifying high-risk patients prior to transfusion, the clinical team may be able to choose a more appropriate therapy or therapeutic course. Identifying high-risk patients for increased observation after transfusion may also allow for a more timely intervention, thereby potentially improving care delivery and resulting patient outcome. An early prototype of this system is currently running in two Mayo Clinic perioperative environments.
AB - During 2011 approximately 21 million blood components were transfused in the United States, with roughly 1 in 414 resulting in complication. For Americans, the two leading causes of transfusion-related death are the respiratory complications Transfusion-related acute lung injury (TRALI) and Transfusion-associated circulatory overload (TACO). Each of these complications results in significantly longer ICU and hospital stays as well as significantly greater rates of mortality. We have developed a set of machine learning models for predicting the likelihood of these adverse reactions in surgical populations. Here we describe deploying these models into a perioperative critical care environment via a continuous monitoring and alerting clinical decision support system. The goal of this system, which directly integrates our suite of machine learning models running in the R statistical environment into a traditional health information system, is to improve transfusion-related outcomes in the perioperative environment. By identifying high-risk patients prior to transfusion, the clinical team may be able to choose a more appropriate therapy or therapeutic course. Identifying high-risk patients for increased observation after transfusion may also allow for a more timely intervention, thereby potentially improving care delivery and resulting patient outcome. An early prototype of this system is currently running in two Mayo Clinic perioperative environments.
KW - Clinical decision support
KW - Continuous monitoring
KW - Prediction
KW - System integration
KW - Transfusion
UR - http://www.scopus.com/inward/record.url?scp=84966350805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966350805&partnerID=8YFLogxK
U2 - 10.1109/ICHI.2015.19
DO - 10.1109/ICHI.2015.19
M3 - Conference contribution
AN - SCOPUS:84966350805
T3 - Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015
SP - 100
EP - 104
BT - Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015
A2 - Fu, Wai-Tat
A2 - Balakrishnan, Prabhakaran
A2 - Harabagiu, Sanda
A2 - Wang, Fei
A2 - Srivatsava, Jaideep
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Healthcare Informatics, ICHI 2015
Y2 - 21 October 2015 through 23 October 2015
ER -