Abstract
Sedative-hypnotic drugs (SHDs) carry significant risks of misuse and dependence. However, current institutional pharmacovigilance relies heavily on passive reporting, leading to substantial underreporting and data gaps. This study aimed to develop and conduct a pilot evaluation of an electronic trigger-based active surveillance model integrated into a Hospital Information System (HIS) to proactively identify high-risk prescription patterns and suspected SHD dependence. This two-phase study was conducted at a tertiary hospital. First, a retrospective analysis of 16,609 outpatient prescriptions was performed to characterize anomalous utilization. Second, a "Trigger Dictionary" based on the Global Trigger Tool (GTT) was developed to flag suspicious cases for a 10-day prospective pilot evaluation. Patients with suspected dependence were screened through structured telephone interviews using ICD-10 diagnostic criteria among flagged patients with ≥ 1-year SHD use. Retrospective analysis revealed that 30.03% of patients (2,616/8,710) received at least one off-label prescription. It also identified 1,567 clusters in which multiple patient IDs shared identical residential addresses, serving as a potential, hypothesis-generating signal for drug diversion. During the prospective pilot, the system generated 200 automated alerts. Among the 63 high-risk patients interviewed, 17 were identified as meeting the screening criteria for suspected SHD dependence, yielding a Positive Predictive Value (PPV) of 26.98% within this selected high-risk interviewed cohort. In contrast, zero cases were reported via the passive system during the same period. Univariate analysis identified benzodiazepine use (OR 33.07, 95% CI: 3.95-276.81, P < 0.001), advanced age, and prolonged therapy duration as exploratory factors associated with suspected misuse. This 10-day prospective pilot evaluation suggests that the electronic trigger model can identify cases of suspected drug dependence that were not captured by traditional passive reporting during the same period. This automated framework offers a scalable approach for clinical pharmacists to triage high-risk patients and mitigate potential drug diversion risks, potentially enhancing medication safety in high-volume outpatient settings.