Abstract
Wrong-site surgeries can occur due to the absence of an appropriate surgical time-out. However, during a time-out, surgical participants are unable to review the patient’s charts due to their aseptic hands. To improve the conditions in surgical time-outs, we introduce a deep learning-based smart speaker to confirm the surgical information prior to cataract surgeries. This pilot study utilized the publicly available audio vocabulary dataset and recorded audio data published by the authors. The audio clips of the target words, such as left, right, cataract, phacoemulsification, and intraocular lens, were selected to determine and confirm surgical information in the time-out speech. A deep convolutional neural network model was trained and implemented in the smart speaker that was developed using a mini development board and commercial speakerphone. To validate our model in the consecutive speeches during time-outs, we generated 200 time-out speeches for cataract surgeries by randomly selecting the surgical statuses of the surgical participants. After the training process, the deep learning model achieved an accuracy of 96.3% for the validation dataset of short-word audio clips. Our deep learning-based smart speaker achieved an accuracy of 93.5% for the 200 time-out speeches. The surgical and procedural accuracy was 100%. Additionally, on validating the deep learning model by using web-generated time-out speeches and video clips for general surgery, the model exhibited a robust and good performance. In this pilot study, the proposed deep learning-based smart speaker was able to successfully confirm the surgical information during the time-out speech. Future studies should focus on collecting real-world time-out data and automatically connecting the device to electronic health records. Adopting smart speaker-assisted time-out phases will improve the patients’ safety during cataract surgeries, particularly in relation to wrong-site surgeries.
Highlights
Medical errors, such as wrong-site surgeries, can be significantly devastating patients as well as surgeons
This paper presents a pilot study designed for the deep learning-based assessment of speech recognition during a time-out in a cataract surgery and the development of a smart speaker to assist with a hands-free time-out
We evaluated the accuracy of our deep learning model across multiple generated time-out speeches, using the smart speaker without a desktop computer
Summary
Medical errors, such as wrong-site surgeries, can be significantly devastating patients as well as surgeons. Operating on an incorrect surgical site is the most common medical error [1]. Ophthalmic surgeries on the wrong eye could occur owing to the carelessness of surgical participants. Wrong-site surgeries still continue to occur in the field of ophthalmology [2]. Recent studies suggests that a preoperative discussion, known as a surgical time-out, can significantly assist in decreasing the risk of wrong-site surgeries [3]. During a surgical time-out, the surgical team can confirm the patient’s identity, surgical site, and name of the procedure. Time-outs are not always conducted accurately, and surgical errors continue to occur [4]
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