Nam Kyoung Hyup, Kim Da Young, Kim Dong Hwan, Lee Jung Hwan, Lee Jae Il, Kim Mi Jeong, Park Joo Young, Hwang Jae Hyun, Yun Sang Seok, Choi Byung Kwan, Kim Min Gyu, Han In Ho
Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Human-Robot Interaction Center, Korea Institute of Robotics and Technology Convergence, Pohang, Korea.
Neurospine. 2022 Jun;19(2):348-356. doi: 10.14245/ns.2143080.540. Epub 2022 May 12.
The purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance accuracy of the SDS in medical staff and patients.
The SDS was developed to investigate pain and related psychological issues in patients with spinal diseases based on the pain questionnaire protocol. We recognized patients' various answers, summarized important information, and documented them. User satisfaction and performance accuracy were evaluated in 30 potential users of SDS, including doctors, nurses, and patients and statistically analyzed.
The overall satisfaction score of 30 patients was 5.5 ± 1.4 out of 7 points. Satisfaction scores were 5.3 ± 0.8 for doctors, 6.0 ± 0.6 for nurses, and 5.3 ± 0.5 for patients. In terms of performance accuracy, the number of repetitions of the same question was 13, 16, and 33 (13.5%, 16.8%, and 34.7%) for doctors, nurses, and patients, respectively. The number of errors in the summarized comment by the SDS was 5, 0, and 11 (5.2%, 0.0%, and 11.6 %), respectively. The number of summarization omissions was 7, 5, and 7 (7.3%, 5.3%, and 7.4%), respectively.
This is the first study in which voice-based conversational artificial intelligence (AI) was developed for a spinal pain questionnaire and validated by medical staff and patients. The conversational AI showed favorable results in terms of user satisfaction and performance accuracy. Conversational AI can be useful for the diagnosis and remote monitoring of various patients as well as for pain questionnaires in the future.
本研究旨在开发一种用于脊柱疾病患者疼痛问卷的口语对话系统（SDS）。我们评估了用户满意度，并验证了该SDS在医护人员和患者中的性能准确性。
基于疼痛问卷协议开发了SDS，以调查脊柱疾病患者的疼痛及相关心理问题。我们识别患者的各种回答，总结重要信息并记录下来。对30名SDS的潜在用户（包括医生、护士和患者）进行了用户满意度和性能准确性评估，并进行了统计分析。
30名患者的总体满意度评分为5.5±1.4（满分7分）。医生的满意度评分为5.3±0.8，护士为6.0±0.6，患者为5.3±0.5。在性能准确性方面，医生、护士和患者对同一问题的重复次数分别为13次、16次和33次（分别占13.5%、16.8%和34.7%）。SDS总结评论中的错误数量分别为5次、0次和11次（分别占5.2%、0.0%和11.6%）。总结遗漏的数量分别为7次、5次和7次（分别占7.3%、5.3%和7.4%）。
这是第一项为脊柱疼痛问卷开发基于语音的对话式人工智能（AI）并由医护人员和患者进行验证的研究。该对话式AI在用户满意度和性能准确性方面显示出良好的结果。对话式AI未来可用于各种患者的诊断和远程监测以及疼痛问卷。
