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Quality Check: Reducing Deviations with Digital Lab Assistants

In this blog post, we will explore how digital lab assistants, specifically LabVoice, can create or establish automated quality control measures to reduce deviations, enhance process efficiency, and improve animal welfare outcomes.

One of the key benefits of incorporating validation steps into a process is that it ensures scientists are using the correct materials, samples, and amounts. Using the wrong resources can significantly impact the quality of a study, leading to study deviations and potentially requiring a restart of the study or rendering it worthless. The cost of duplicating an experiment, considering the employee and instrument time, wasted materials, and project delays, can run into tens of thousands of dollars. Experimental materials are limited in quantity and often very challenging to recreate. For these reasons, it is critical for researchers to use the correct materials and samples during their study. Automated, digital quality checks ensure that the study is performed as intended the first time, leading to greater cost and time savings.

From an animal welfare perspective, as in this use case where an animal model is being studied, it is important to ensure that the animals are maintained with care. When working with animals, it is imperative that all needless deviations that could lead to the unnecessary inclusion of additional animals. This is in line with the “3 R’s of Animal Research” to reduce the number of animals used. The presence of a digital lab assistant, with its ability to validate that the operator is following the proper steps and using the right materials, can reduce any unnecessary irregularities or divergences.

The video highlights several features of LabVoice, the digital lab assistant. The LabFlow is run using an invocation phrase and instructs the user to scan the cage, retrieving relevant information from an ELN or study software. For this video, we are using RockStep Solutions’ Climb 2.0. If the cage is not barcoded, the user can recite the Cage ID.

After retrieving information about the cage, LabVoice displays the details and prompts the user to confirm they are working with the correct study. This study confirmation is the first quality step to ensure the animal selection and task being performed align.

For demonstration purposes, the video includes both a "right" and "wrong" barcode at each step. After scanning a correct animalID, the digital assistant displays the stored animal metadata from Climb.

LabVoice then prompts the user to scan the barcode of the compound they are using and verifies that it is the correct one for the animal. This verification is the third quality step, ensuring that the compound being used aligns with the animal that was previously scanned and confirmed.

These three quality check steps are an example of how a digital assistant can be used to confirm that the study, animal, and experimental material being used are correct. With effective quality checks, the likelihood of error is decreased substantially, reducing the need to repeat experiments and enabling efficient use of experimental resources. Moreover, the quality check steps are performed in real time along with the researcher’s experiment execution, eliminating the need to visually refer back to the ELN or spreadsheet they previously were copying data to.

As users become more familiar with LabVoice, workflows can be sped up, further enhancing efficiency. Further, additional quality measures can be added easily to allow for quick responses to changes in experimental protocol or laboratory needs.

In conclusion, digital lab assistants like LabVoice offer a range of benefits, from improving process efficiency to enhancing animal welfare outcomes. By incorporating automated quality control measures, these tools can help reduce the occurrence of errors and ensure the effective use of animals and materials during scientific studies.

Interested in learning more about our platform and how it can help reduce deviations in your lab? Request to speak with LabVoice here:



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