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dc.contributor.advisorLi, Linen_US
dc.contributor.authorReese, Joshuaen_US
dc.date.accessioned2013-10-24T20:40:16Z
dc.date.available2013-10-24T20:40:16Z
dc.date.created2013-08en_US
dc.date.issued2013-10-24
dc.date.submitted2013-08en_US
dc.identifier.urihttp://hdl.handle.net/10027/10175
dc.description.abstractMethods to potentially reduce pain management and anesthesia drug costs were analyzed in this study. A non-linear programming model was developed to accept intravenous, transdermal patch, and oral pain medications as inputs to generate a cost-minimized dosing recommendation. The non-linear programming model was constrained by the maximum non-lethal dose for each drug and a popular pharmacodynamic equation to determine the probability of no response. An autoregressive moving average (ARMA) technique was used to develop a forecasting model for one patient's historical bispectral index data. The ARMA technique fits the bispectral index data well and generated accurate forecasts of patient sedation five minutes ahead; however, it requires a significant amount of data collection. A moving average technique is proposed to generate forecasts while the autoregressive technique collects sufficient historical data points. The non-linear programming model successfully identified local cost-minimized dosing recommendations assuming a set of hypothetical medication properties. Further blood-work analysis must be conducted to ascertain true drug property values for drugs used in pain management and to permit implementation.en_US
dc.language.isoenen_US
dc.rightsen_US
dc.rightsCopyright 2013 Joshua Reeseen_US
dc.subjectbispectral indexen_US
dc.subjectnon-linear programen_US
dc.subjectautoregressive moving averageen_US
dc.subjectpain managementen_US
dc.titleAnesthesia and Pain Management Drug Cost Reduction while Maintaining Adequate Patient Careen_US
thesis.degree.departmentMechanical and Industrial Engineeringen_US
thesis.degree.disciplineIndustrial Engineeringen_US
thesis.degree.grantorUniversity of Illinois at Chicagoen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMS, Master of Scienceen_US
dc.type.genrethesisen_US
dc.contributor.committeeMemberDarabi, Houshangen_US
dc.contributor.committeeMemberEdelman, Guyen_US
dc.type.materialtexten_US


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