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Radiation Therapy Treatment Deviations Potentially Prevented by a Novel Combined Radio-Frequency Identification (RFID), Biometric and Surface Matching Technology

H Zhao

H Zhao*, Y Huang , V Sarkar , P Rassiah-Szegedi , F Su , M Szegedi , L Huang , A Paxton , B Salter , University of Utah, Huntsman Cancer Institute, Salt Lake City, UT


WE-RAM1-GePD-J(B)-2 (Wednesday, August 2, 2017) 9:30 AM - 10:00 AM Room: Joint Imaging-Therapy ePoster Lounge - B

Purpose: To evaluate the impact of a novel combined RFID, biometric and surface matching technology (HumediQ Identify) on potential prevention of radiation therapy treatment deviations.

Methods: 108 radiation therapy treatment deviations at our facility over the past 8 years were analyzed. Three major categories are defined: Physician Order deviations (19.4%), treatment planning deviations (24.2%), and machine treatment deviations (56.5%). The impact of HumediQ Identify on potential prevention of machine-related treatment deviations was analyzed.

Results: Combined face photo and biometric palm identification was observed to be efficient and very effective at ensuring treatment of the correct patient. RFID verification of patient-specific setup devices included the verification of immobilization and accessory devices and their correct position on the treatment couch. This approach was seen as capable of eliminating Treatment Accessory deviations. Accurate initial patient position and orientation at loading position was accomplished by patient self-positioning according to video feedback agreement of real-time surface with Simulation surface. This approach was observed to eliminate deviations of patient orientation. Patient treatment-surface monitoring was seen as capable of preventing day to day treatment position deviations. Of the 61 machine-related treatment deviations, 36 (59%) were interpreted as being LIKELY PREVENTED by using the current version of HumediQ Identify (version 1.8). One major function of initial patient treatment position based on treatment planning isocenter is under development and expected to implement very soon. With the application of this function, 12 additional deviations (19.7%) were seen as Likely to be prevented. Overall, 78.7% of the Machine-related treatment deviations were seen as Likely to be prevented by the impending version of HumediQ Identify.

Conclusion: 79% of our previous Machine-related treatment deviations were seen as preventable by the impending version of HumediQ Identify.

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