Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction
Objective 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. Methods The SDS was developed to investigate pain and...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Korean Spinal Neurosurgery Society
2022-06-01
|
| Series: | Neurospine |
| Subjects: | |
| Online Access: | http://e-neurospine.org/upload/pdf/ns-2143080-540.pdf |
| _version_ | 1797334027393302528 |
|---|---|
| author | Kyoung Hyup Nam Da Young Kim Dong Hwan Kim Jung Hwan Lee Jae Il Lee Mi Jeong Kim Joo Young Park Jae Hyun Hwang Sang Seok Yun Byung Kwan Choi Min Gyu Kim In Ho Han |
| author_facet | Kyoung Hyup Nam Da Young Kim Dong Hwan Kim Jung Hwan Lee Jae Il Lee Mi Jeong Kim Joo Young Park Jae Hyun Hwang Sang Seok Yun Byung Kwan Choi Min Gyu Kim In Ho Han |
| author_sort | Kyoung Hyup Nam |
| collection | DOAJ |
| description | Objective 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. Methods 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. Results 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. Conclusion 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. |
| first_indexed | 2024-03-08T08:13:55Z |
| format | Article |
| id | doaj.art-dcc3833de7314aa1a02b4e193b11663c |
| institution | Directory Open Access Journal |
| issn | 2586-6583 2586-6591 |
| language | English |
| last_indexed | 2024-03-08T08:13:55Z |
| publishDate | 2022-06-01 |
| publisher | Korean Spinal Neurosurgery Society |
| record_format | Article |
| series | Neurospine |
| spelling | doaj.art-dcc3833de7314aa1a02b4e193b11663c2024-02-02T08:01:00ZengKorean Spinal Neurosurgery SocietyNeurospine2586-65832586-65912022-06-0119234835610.14245/ns.2143080.5401262Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User SatisfactionKyoung Hyup Nam0Da Young Kim1Dong Hwan Kim2Jung Hwan Lee3Jae Il Lee4Mi Jeong Kim5Joo Young Park6Jae Hyun Hwang7Sang Seok Yun8Byung Kwan Choi9Min Gyu Kim10In Ho Han11 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 Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea Division of Mechanical Convergence Engineering, Silla University, Busan, Korea 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 Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, KoreaObjective 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. Methods 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. Results 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. Conclusion 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.http://e-neurospine.org/upload/pdf/ns-2143080-540.pdfconversational artificial intelligencepain questionnairespoken dialogue systemnatural language processchatbotspine |
| spellingShingle | Kyoung Hyup Nam Da Young Kim Dong Hwan Kim Jung Hwan Lee Jae Il Lee Mi Jeong Kim Joo Young Park Jae Hyun Hwang Sang Seok Yun Byung Kwan Choi Min Gyu Kim In Ho Han Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction Neurospine conversational artificial intelligence pain questionnaire spoken dialogue system natural language process chatbot spine |
| title | Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction |
| title_full | Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction |
| title_fullStr | Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction |
| title_full_unstemmed | Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction |
| title_short | Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction |
| title_sort | conversational artificial intelligence for spinal pain questionnaire validation and user satisfaction |
| topic | conversational artificial intelligence pain questionnaire spoken dialogue system natural language process chatbot spine |
| url | http://e-neurospine.org/upload/pdf/ns-2143080-540.pdf |
| work_keys_str_mv | AT kyounghyupnam conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT dayoungkim conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT donghwankim conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT junghwanlee conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT jaeillee conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT mijeongkim conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT jooyoungpark conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT jaehyunhwang conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT sangseokyun conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT byungkwanchoi conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT mingyukim conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction AT inhohan conversationalartificialintelligenceforspinalpainquestionnairevalidationandusersatisfaction |