(P-IT-2) Does Antibody Rule-Out Software Actually Save Time?

Antibody Identification (ABID) plays a significant role in transfusion medicine. Some technologists loved it and considered it fun while others dreaded a positive antibody detection (AD) result. This has been especially evident with more generalists rotating into the blood bank. Staffing challenges and complicated antibody workups with less experience techs have led to a desire to employ more software programs to assist with ABID, with the goal to reduce send out cost, improve turn-around times (TAT) while maintaining the highest degree of accuracy for patient safety. This study was based on TAT for ABID when techs perform manual ABID compared to using an antibody rule out algorithm software (ROS) including the potential labor savings

Study

Design/Methods:

We currently employ 2 automated analyzers, Echo Lumenas (Werfen, Norcross, GA) with their previous data management software, ImmuLINK 2.2 (Werfen, Norcross, GA). In 2024, we implemented ImmuLINK 3.1 with ROS to assist our techs with ABID by offering an exclusion software to rule out clinically significant antibodies based on the AABB reference lab standards. We included a time study to compare the TAT from the time the positive AD result became available from the analyzer till the time the result was completed. Five different techs performed a different ABID workup, using our manual method criteria compared  to the ROS.

Results/Findings:

We demonstrated an improved TAT, on average of 73% and  20-minute labor savings with ROS (Table 1). In addition, the ROS ensured results are digitally downloaded & recorded on the correct master list from the analyzer, then guided the tech to determine if additional testing, such as a secondary panel maybe needed.  ROS collated the data for all the instrument panels and antibody detection, to obtain an ABID exclusion result. The tech still needed to confirm the result and determine if it met the internal criteria.

Conclusions:

Even though the data management rule-out algorithm software was rather new, and the techs were not as familiar with the interpretation, they did easily adapt.  This allowed for a turn-around reduction of 73% to obtain antibody identification results.  The TAT & labor savings for electronic review and approval was not included in this study.  The techs gave positive feedback regarding the ability to have all master panel lists and AD on one page for review.  The supervisor noticed how quickly the tech was able to accept the ROS results rather than languish over the review process, resulting in a more streamline progression.   This data supported rule out software will aid in better patient care, faster accurate results, less stress for the techs and an easier review process. We believe using an antibody rule out algorithm software will decrease our potential for sent out cost and plan to monitor this in the future.