Speech Recognition Technology in Prehospital Documentation: A Scoping Review

Speech Recognition Technology in Prehospital Documentation: A Scoping Review
Objectives The nature of paramedic workflows, where paramedics are responsible to provide care and chart concurrently, can result in incomplete or non-existent patient care reports on patient handover to the emergency department (ED). Charting delays and retrospective recollection of care may lead to patient information gaps, which can increase ED workload, cause care delays, and increase the risk of adverse events. Speech recognition documentation technology has the potential to produce complete patient care reports quicker and improve paramedic-to-ED handover. We performed a scoping review to determine paramedic perceptions and user requirements for speech recognition documentation technology.

Methods MEDLINE, Google Scholar, IEEE Explore, ProQuest, and CINAHL were searched from 2014 to March 2024. Criteria included studies focused on paramedics' use or perceptions of speech recognition documentation technology. This review included studies conducted in the prehospital environment and adjacent agencies (i.e., ED, fire, police, military).

Results The review identified eight articles that met inclusion criteria. All eight articles were small focus group-based studies in laboratory settings published on or after 2020. Five studies were conducted in the United States, two in Switzerland, and one in Japan. Of the eight studies, five recommended further live environment testing of the technology examined, and three underscored the importance of a user-centred design. The top user requirements for speech recognition adoption was hands-free use, noise reduction technology, battery life, and word accuracy. All eight studies recommended further research and development of speech recognition documentation technology in the prehospital workflow.

Conclusion This scoping review has highlighted that while there is a growing interest in speech recognition documentation technology in the paramedicine workflow, more research is needed, especially with larger samples in a live environment. The user requirements and perceptions of speech recognition documentation technology in paramedicine must be better understood to design systems with high adoption rates.

Poster References:
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3.    Blackley SV, Schubert VD, Goss FR, Al Assad W, Garabedian PM, Zhou L. Physician use of speech recognition versus typing in clinical documentation: A controlled observational study. Int J Med Inf. 2020 Sep;141:104178. 

4.    Shagoury J. Dr. “Multi-Task”: Using Speech to Build Up Electronic Medical Records While Caring for Patients. In: Neustein A, editor. Advances in Speech Recognition [Internet]. Boston, MA: Springer US; 2010 [cited 2023 Jun 6]. p. 247–73. Available from: http://link.springer.com/10.1007/978-1-4419-5951-5_11

5.    Tanberk S, Dağlı V, Gürkan MK. Deep Learning for Videoconferencing: A Brief Examination of Speech to Text and Speech Synthesis. In: 2021 6th International Conference on Computer Science and Engineering (UBMK). 2021. p. 506–11. 

6.    Zuchowski M, Göller A. Speech recognition for medical documentation: an analysis of time, cost efficiency and acceptance in a clinical setting. Br J Healthc Manag. 2022 Jan 2;28(1):30–6. 

7.    Joseph J, Moore ZEH, Patton D, O’Connor T, Nugent LE. The impact of implementing speech recognition technology on the accuracy and efficiency (time to complete) clinical documentation by nurses: A systematic review. J Clin Nurs. 2020 Jul;29(13–14):2125–37.