An AI-Powered Precision Patient Tracking System for Interdepartmental Patient Identification and Recall: A Real-World Longitudinal Study

Background: During specialty care, clinical assessments may identify abnormalities requiring evaluation by another department. However, gaps in cross-department follow-up may delay subsequent specialty evaluation. This study evaluated the application of an AI-Powered Precision Patient Tracking System (APTS) for cross-department patient identification, recall, and return-to-care tracking and examined its association with changes in departmental outpatient volume. Methods: We conducted a single-center longitudinal study using monthly data from nine clinical departments at Shenzhen Longgang Central Hospital between January 2025 and September 2026, comprising 189 department-month observations. APTS integrates multisource clinical information to support patient identification, task assignment, recall management, and return-to-care tracking. The Callaway–Sant’Anna staggered difference-in-differences approach was used to estimate changes in monthly unique outpatients and outpatient visits following APTS implementation. APTS operational measures and the departmental origins of patients returning to outpatient care following recall were also analyzed. Results: Following APTS implementation, 109,620 patients were assigned for subsequent management, of whom 50,429 (46.0%) entered recall management and 9525 returned to outpatient care following APTS recall. Of these 9525 patients, 8593 (90.2%) originated from departments other than the target department, with such patients constituting the majority in all nine departments. APTS implementation was not significantly associated with changes in monthly unique outpatients (−2.5%; 95% CI, −11.2% to 7.0%) or outpatient visits (−1.9%; 95% CI, −12.1% to 9.5%). Conclusions: The potential value of APTS may lie in supporting the identification and management of patients across departmental boundaries. No statistically significant change in departmental outpatient volume was detected within the available identification window, although potentially meaningful changes could not be excluded.