Wdrożenie narzędzia AI do wspomagania zarządzania wiadomościami w portalu pacjenta: automatyczna kategoryzacja i kierowanie
AI Tool Implementation to Assist with Management of Patient Portal Messages: Auto-Categorization and Routing
W skrócie
[Preprint - wstępne wyniki] Badacze przetestowali narzędzie sztucznej inteligencji, które automatycznie sortuje i kieruje wiadomości od pacjentów w poradni neurologicznej. Okazało się, że AI znacznie przyspieszył podjęcie pierwszego działania przez personel medyczny - czas zmniejszył się z około 11-16 godzin do poniżej 1 godziny. Wyniki sugerują, że AI może wspomóc pracę lekarzy i przyczynić się do szybszej opieki nad pacjentami z epilepsją.
Oryginalny abstract (angielski)
Background: and Objective: The volume of patient portal messages has grown substantially and artificial intelligence (AI) can support clinical staff and improve the timeliness of patient care. Our objective is to test if an AI tool for managing the electronic inbox In-Basket reduces response times to patient portal messages in neurology. Methods: We conducted an implementation study from January to May 2026 in the neurology department at two clinical sites within a regional medical system. A single AI tool was implemented to auto-categorize and auto-route all patient messages received in the electronic inbox. The tool also generates optional draft replies to messages. Primary process measures were the median weekly time (hours) from message creation to the first action and to closure. Impact was analyzed using a controlled interrupted time-series (ITS) design, comparing message-volume-matched control divisions. Results: Among 24,142 messages analyzed, pre-intervention median weekly time to first action was 11.3 hours [IQR: 5.6, 15.1] (Site A) and 16.1 hours [IQR: 13.8, 19.0] (Site B), falling post-intervention to 1.0 hour [IQR: 0, 3.1] (Site A) and 0.6 hours [IQR: 0.2, 2.4] (Site B). Controlled interrupted time-series analysis showed a significant immediate differential level change in median weekly hours to first action at Site A (-92.5%, 95% CI: -97.5% to -78.0%), and a significant downward trend at Site B (-43.5%, 95% CI: -65.2% to -8.2%). Changes in time to closure were directionally favorable but not statistically significant at either site. There was low use of the generated draft replies, only 0.5% at Site A and 3% at Site B. Discussion: Implementation of AI-assisted message categorization and routing was associated with a substantially reduced initial action time for patient In-Basket messages in Epilepsy. The utilization of AI-generated draft-replies to messages was low. These findings support the feasibility and effectiveness of AI-augmented workflows to improve inbox management.