Satisfactory evaluation of call service using artificial intelligence after ureteral stent insertion: A randomized controlled trial (Preprint)

Background Ureteral 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. Objective This 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. Methods Overall, 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. Results Of 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). Conclusions This 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 Registration Clinical Research Information System KCT0008062; https://tinyurl.com/4s8725w2