Reducing Surgical Patient Costs Through Use of an Artificial Neural Network to Predict Transfusion Requirements
Document Type
Article
Publication Date
12-2000
Keywords
neural networks, radial basis function, transfusion, cost reduction
Digital Object Identifier (DOI)
https://doi.org/10.1016/S0167-9236(00)00093-2
Abstract
Transfusion and blood bank services have long been identified as a source of potential cost savings. The implementation and use of maximum surgical blood ordering schedules (MSBOS) and type and screen practices have already succeeded in reducing overall waste and costs associated with transfusion services, but further reductions in waste and cost are still realizable. An artificial neural network (ANN) is trained to predict the quantity of transfusion units that are required by surgical patients for a specific operation. The ANNs produce a significant reduction in the quantity of blood ordered and a subsequent reduction in costs to the hospital and patients. ANNs offer a means to reduce patient costs while maintaining a high level of patient care.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Decision Support Systems, v. 30, issue 2, p. 125-138
Scholar Commons Citation
Walczak, Steven and Scharf, John E., "Reducing Surgical Patient Costs Through Use of an Artificial Neural Network to Predict Transfusion Requirements" (2000). School of Information Faculty Publications. 207.
https://digitalcommons.usf.edu/si_facpub/207
