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Development of an Automated and Scalable Virtual Assistant to Aid in PPE Adherence: A Study with Implications for Applications within Anesthesiology

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Abstract

Virtual assistants (VAs) are conversational agents that are able to provide cognitive aid. We developed a VA device for donning and doffing personal protective equipment (PPE) procedures and compared it to live human coaching to explore the feasibility of using VAs in the anesthesiology setting. An automated, scalable, voice-enabled VA was built using the Amazon Alexa device and Alexa Skills application. The device utilized voice-recognition technology to allow a touch-free interactive user experience. Audio and video step-by-step instructions for proper donning and doffing of PPE were programmed and displayed on an Echo Show device. The effectiveness of VA in aiding adherence to PPE protocols was compared to traditional human coaching in a randomized, controlled, single-blinded crossover design. 70 anesthesiologists, anesthesia assistants, respiratory therapists, and operating room nurses performed both donning and doffing procedures, once under step-by-step VA instructional guidance and once with human coaching. Performance was assessed using objective performance evaluation donning and doffing checklists. More participants in the VA group correctly performed the step of “Wash hands for 20 seconds” during both donning and doffing tests. Fewer participants in the VA group correctly performed the steps of “Put cap on and ensure covers hair and ears” and “Tie gown on back and around neck”. The mean doffing total score was higher in the VA group; however, the donning score was similar in both groups. Our study demonstrates that it is feasible to use commercially available technology to create a voice-enabled VA that provides effective step-by-step instructions to healthcare professionals.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This project was supported by the AHSC AFP Innovation Fund.

Author information

Authors and Affiliations

  1. Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

    Eric Plitman & Michael Dinsmore

  2. Department of Anesthesia and Pain Medicine, Toronto Western Hospital, University of Toronto, Toronto, ON, Canada

    Eric Plitman, Rajesh Patel, Seema Kohout, Rongyu Jin, Vincent Chan & Michael Dinsmore

  3. Department of Computer Sciences, Drexel University, Philadelphia, USA

    Edward Kim

Authors
  1. Eric Plitman
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  2. Edward Kim
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  3. Rajesh Patel
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  4. Seema Kohout
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  5. Rongyu Jin
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  6. Vincent Chan
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  7. Michael Dinsmore
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Contributions

E.P. performed statistical analyses and wrote the manuscript. R.P., S.K., C.J. and V.C. contributed to data collection and helped to write the manuscript. E.K. contributed to data acquisition and writing the manuscript. M.D. supervised the work, contributed to the study concept and design, performed statistical analyses, and wrote the manuscript. All authors critically reviewed and approved the final manuscript.

Corresponding author

Correspondence to Michael Dinsmore.

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The authors have no relevant financial or non-financial interests to disclose.

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Plitman, E., Kim, E., Patel, R. et al. Development of an Automated and Scalable Virtual Assistant to Aid in PPE Adherence: A Study with Implications for Applications within Anesthesiology. J Med Syst 48, 7 (2024). https://doi.org/10.1007/s10916-023-02028-w

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  • DOI: https://doi.org/10.1007/s10916-023-02028-w

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