Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial
BackgroundUreteral stents, such as double-J stents, have become indispensable in urologic procedures but are associated with complications like hematuria and pain. While the advancement of artificial intelligence (AI) technology has led to its increasing application in the he...
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JMIR Publications
2025-01-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e56039 |
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| author | Ukrae Cho Yong Nam Gwon Seung Ryong Chong Ji Yeon Han Do Kyung Kim Seung Whan Doo Won Jae Yang Kyeongmin Kim Sung Ryul Shim Jaehun Jung Jae Heon Kim |
| author_facet | Ukrae Cho Yong Nam Gwon Seung Ryong Chong Ji Yeon Han Do Kyung Kim Seung Whan Doo Won Jae Yang Kyeongmin Kim Sung Ryul Shim Jaehun Jung Jae Heon Kim |
| author_sort | Ukrae Cho |
| collection | DOAJ |
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BackgroundUreteral stents, such as double-J stents, have become indispensable in urologic procedures but are associated with complications like hematuria and pain. While the advancement of artificial intelligence (AI) technology has led to its increasing application in the health sector, AI has not been used to provide information on potential complications and to facilitate subsequent measures in the event of such complications.
ObjectiveThis study aimed to assess the effectiveness of an AI-based prediction tool in providing patients with information about potential complications from ureteroscopy and ureteric stent placement and indicating the need for early additional therapy.
MethodsOverall, 28 patients (aged 20-70 years) who underwent ureteral stent insertion for the first time without a history of psychological illness were consecutively included. A “reassurance-call” service was set up to equip patients with details about the procedure and postprocedure care, to monitor for complications and their severity. Patients were randomly allocated into 2 groups, reassurance-call by AI (group 1) and reassurance-call by humans (group 2). The primary outcome was the level of satisfaction with the reassurance-call service itself, measured using a Likert scale. Secondary outcomes included satisfaction with the AI-assisted reassurance-call service, also measured using a Likert scale, and the level of satisfaction (Likert scale and Visual Analogue Scale [VAS]) and anxiety (State-Trait Anxiety Inventory and VAS) related to managing complications for both groups.
ResultsOf the 28 recruited patients (14 in each group), 1 patient in group 2 dropped out. Baseline characteristics of patients showed no significant differences (all P>.05). Satisfaction with reassurance-call averaged 4.14 (SD 0.66; group 1) and 4.54 (SD 0.52; group 2), with no significant difference between AI and humans (P=.11). AI-assisted reassurance-call satisfaction averaged 3.43 (SD 0.94). Satisfaction about the management of complications using the Likert scale averaged 3.79 (SD 0.70) and 4.23 (SD 0.83), respectively, showing no significant difference (P=.14), but a significant difference was observed when using the VAS (P=.01), with 6.64 (SD 2.13) in group 1 and 8.69 (SD 1.80) in group 2. Anxiety about complications using the State-Trait Anxiety Inventory averaged 36.43 (SD 9.17) and 39.23 (SD 8.51; P=.33), while anxiety assessed with VAS averaged 4.86 (SD 2.28) and 3.46 (SD 3.38; P=.18), respectively, showing no significant differences. Multiple regression analysis was performed on all outcomes, and humans showed superior satisfaction than AI in the management of complications. Otherwise, most of the other variables showed no significant differences (P.>05).
ConclusionsThis is the first study to use AI for patient reassurance regarding complications after ureteric stent placement. The study found that patients were similarly satisfied for reassurance calls conducted by AI or humans. Further research in larger populations is warranted to confirm these findings.
Trial RegistrationClinical Research Information System KCT0008062; https://tinyurl.com/4s8725w2 |
| format | Article |
| id | doaj-art-d195796502bf49adbb9a92543ddc4ba5 |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-d195796502bf49adbb9a92543ddc4ba52025-01-21T18:00:56ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-01-0127e5603910.2196/56039Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled TrialUkrae Chohttps://orcid.org/0009-0000-0242-303XYong Nam Gwonhttps://orcid.org/0000-0002-8732-327XSeung Ryong Chonghttps://orcid.org/0009-0001-7148-0860Ji Yeon Hanhttps://orcid.org/0009-0001-0984-0714Do Kyung Kimhttps://orcid.org/0000-0002-3696-8756Seung Whan Doohttps://orcid.org/0000-0002-5924-6908Won Jae Yanghttps://orcid.org/0000-0002-7356-4312Kyeongmin Kimhttps://orcid.org/0000-0002-4814-9264Sung Ryul Shimhttps://orcid.org/0000-0003-4143-7383Jaehun Junghttps://orcid.org/0000-0002-4856-3668Jae Heon Kimhttps://orcid.org/0000-0002-4490-3610 BackgroundUreteral stents, such as double-J stents, have become indispensable in urologic procedures but are associated with complications like hematuria and pain. While the advancement of artificial intelligence (AI) technology has led to its increasing application in the health sector, AI has not been used to provide information on potential complications and to facilitate subsequent measures in the event of such complications. ObjectiveThis study aimed to assess the effectiveness of an AI-based prediction tool in providing patients with information about potential complications from ureteroscopy and ureteric stent placement and indicating the need for early additional therapy. MethodsOverall, 28 patients (aged 20-70 years) who underwent ureteral stent insertion for the first time without a history of psychological illness were consecutively included. A “reassurance-call” service was set up to equip patients with details about the procedure and postprocedure care, to monitor for complications and their severity. Patients were randomly allocated into 2 groups, reassurance-call by AI (group 1) and reassurance-call by humans (group 2). The primary outcome was the level of satisfaction with the reassurance-call service itself, measured using a Likert scale. Secondary outcomes included satisfaction with the AI-assisted reassurance-call service, also measured using a Likert scale, and the level of satisfaction (Likert scale and Visual Analogue Scale [VAS]) and anxiety (State-Trait Anxiety Inventory and VAS) related to managing complications for both groups. ResultsOf the 28 recruited patients (14 in each group), 1 patient in group 2 dropped out. Baseline characteristics of patients showed no significant differences (all P>.05). Satisfaction with reassurance-call averaged 4.14 (SD 0.66; group 1) and 4.54 (SD 0.52; group 2), with no significant difference between AI and humans (P=.11). AI-assisted reassurance-call satisfaction averaged 3.43 (SD 0.94). Satisfaction about the management of complications using the Likert scale averaged 3.79 (SD 0.70) and 4.23 (SD 0.83), respectively, showing no significant difference (P=.14), but a significant difference was observed when using the VAS (P=.01), with 6.64 (SD 2.13) in group 1 and 8.69 (SD 1.80) in group 2. Anxiety about complications using the State-Trait Anxiety Inventory averaged 36.43 (SD 9.17) and 39.23 (SD 8.51; P=.33), while anxiety assessed with VAS averaged 4.86 (SD 2.28) and 3.46 (SD 3.38; P=.18), respectively, showing no significant differences. Multiple regression analysis was performed on all outcomes, and humans showed superior satisfaction than AI in the management of complications. Otherwise, most of the other variables showed no significant differences (P.>05). ConclusionsThis is the first study to use AI for patient reassurance regarding complications after ureteric stent placement. The study found that patients were similarly satisfied for reassurance calls conducted by AI or humans. Further research in larger populations is warranted to confirm these findings. Trial RegistrationClinical Research Information System KCT0008062; https://tinyurl.com/4s8725w2https://www.jmir.org/2025/1/e56039 |
| spellingShingle | Ukrae Cho Yong Nam Gwon Seung Ryong Chong Ji Yeon Han Do Kyung Kim Seung Whan Doo Won Jae Yang Kyeongmin Kim Sung Ryul Shim Jaehun Jung Jae Heon Kim Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial Journal of Medical Internet Research |
| title | Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial |
| title_full | Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial |
| title_fullStr | Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial |
| title_full_unstemmed | Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial |
| title_short | Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial |
| title_sort | satisfactory evaluation of call service using ai after ureteral stent insertion randomized controlled trial |
| url | https://www.jmir.org/2025/1/e56039 |
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