General anesthesia is an integral part of most surgical operations but anesthesia decisions are very challenging. Anesthetic requirements and agent dosages depend critically on the patient’s medical condition, surgical procedure, drug interaction, coordinated levels of anesthesia depth, along with physiological variables.
As a result, drug impact is very difficult to predict subjectively and manually. Satisfactory anesthesia decisions require extensive clinical experience. Errors in anesthesia decision occur even with experienced personnel and the resulting impact range from minor consequences to serious morbidity and mortality.
Researchers at Wayne State University have developed a smart anesthesia monitoring system (Case ID 02-621) to assist anesthesiologists using real-time monitoring, helps to make outcome predictions, and as a result, provides better anesthesia decision support in operating rooms.
The monitoring system enables the physician to look into the near future and make decisions that are objective, timely, and accurate. The technology used is a novel information processing methodology that is able to measure drug rates, physiological signals, and real-time data analysis to establish and update individual patient models.
Specifically, this anesthesia monitoring system is to be able to predict drug impact, optimal drug dosage, make real-time recommendations, increase decision accuracy, reduce clinical workload, and provide critical condition warnings.
The initial prototype has been developed and needs clinical data to be collected from patients to verify the utility of the system, response time, and model accuracy. The patent (US10/561.074) “System for Identifying Patient Response to Anesthesia Infusion” is pending.
For more information, go to http://wayne.technologypublisher.com/technology/15821 or email Nicole Grynaviski, Commercialization Principal at Wayne State University at Nicole.firstname.lastname@example.org.