Automatic speech recognition in the operating room – An essential contemporary tool or a redundant gadget? A survey evaluation among physicians in form of a qualitative study

Abstract

Introduction For decades, automatic speech recognition (ASR) has been the subject of research and its range of applications broadened. Presently, ASR among physicians is mainly used to convert speech into text but not to implement instructions in the operating room (OR). This study aimed to evaluate physicians of different surgical professions on their personal experience and posture towards ASR. Methods A 16-item survey was distributed electronically to hospitals and outpatient clinics in southern Germany addressing physicians on the potential applications of ASR in the OR. Results The survey was responded by 185 of 2693 physicians (response rate: 6.9%) with a mean age of 41.8 ± 9.8 years. ASR is desirable in the OR regardless of the field of speciality (93.7%). While only 2.7% have used ASR, 87.9% evaluate its future potential as high. 91.0% of those working in a university hospital would consider testing ASR, while 67.5% of those in non-university hospitals and practices (p = 0.001). 90.1% of responders of strictly surgical specialities see potential in ASR while 73.7% in non-surgical specialities evaluate its future potential as high (p = 0.01). 58.3% of those over the age of 60 consider the use of ASR without a headset to be imaginable, while 96.3% among those under the age of 60. There were no statistically significant differences regarding sex and professional position. Conclusion Foreseeably, ASR is anticipated to be integrated into ORs and valued at a high market potential. Our study provides information about physicians’ individual preferences from various surgical disciplines regarding ASR.

An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals

“…Only three studies investigated whether there was an effect of gender on acceptance. None of them found significant effects 50 – 52 .…”

“…With respect to age, three studies investigated whether there was an effect of age on the use of AI. Two studies did not observe an effect 50,52 . Walter et al 53 found that 55.8% of younger participants claimed that they would use automated pain recognition.…”

“… Physicians 20 Qualitative semi-structured Interviews 5 Systems may favour errors Sandhu et al (2020) 45 Machine learning n.a. Physicians and nurses 15 Qualitative Semi-structured Interviews 5 Unfamiliarity with the system resulted in confusion and misunderstanding Most useful for residents still developing clinical skills or low-resource community settings Schulte et al (2020) 50 Automatic speech recognition Mean age of 41.8 ± 9.8 years Physicians 185 Quantitative Survey 4 Voice recognizer without headset Stifter et al (2018) 51 CDSS 21–71 years old Nurses 60 Quantitative Survey 4 Higher acceptability among participants with less than one year of experience than those with 1 or more years of experience Walter et al (2020) 53 Automated pain recognition Mean age of 40.31 years ± 11.5 Physicians and nurses 102 Quantitative Survey 5 Pain detection accuracy of > 80% Yurdaisik and Aksoy (2021) 30 AI n.a. Physicians, technicians and medical students 204 Quantitative Survey 4 Only 5.3% of participants stated that they will assume the legal responsibility of imaging results Among the participants, 51.9% think that AI applications will save time for radiologists Zheng et al (2021) 33 AI in ophtalmology Less than 25 to older than 45 years old Physicians and technicians 562 Quantitative Survey 4 56.4% said that in the current ophthalmic AI practice, medical responsibilities are unclear Aljarboa et al (2019) 18 CDSS ...…”

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

“…In addition, patients often require equipment to assist in treatment and rehabilitation. Integrating voice technology into medical equipment can bring great convenience to doctors and patients in many medical scenarios [ 199 ]. For example, smart medicine boxes remind patients to take medicines on time, intelligent ward round systems help doctors collect patient information [ 200 ], and voice systems perform automatic postoperative follow-up visits [ 201 , 202 ].…”

Revolutionizing Radiological Analysis: The Future of French Language Automatic Speech Recognition in Healthcare

“…In long-term care for older adults, ASR models have been used to facilitate interview data transcription, saving time and resources [17]. Even in operating rooms, ASR techniques can be used to improve the dialogue between the surgeon and their human (e.g., surgical nurses) or digital (e.g., robotic arms) assistants [18,19]. Additionally, in pediatric care, ASR and voice interaction technologies have been explored for remote care management, demonstrating feasibility and effectiveness in tracking symptoms and health events [20].…”