Comparison of 3 Surveillance Methods to Detect Potential Controlled Substance Diversion in an Academic Medical Center

Abstract

Objectives: To compare 3 methods of detecting potential diversion of controlled substances (CS) by health care personnel from inpatient units in a large, academic medical center. Methods: Three different reports were retrospectively analyzed and evaluated to determine which employees are “high-risk” for diversion over a 30-day period using defined criteria. Reports were derived from automated dispensing machines (ADMs), purchased third-party software (TPS), and the electronic health record (EHR). The primary outcome was the percentage of employees in each report who were deemed to be high-risk for CS diversion (positive predictive value [PPV]). Secondary outcomes included the number of false positives and description of high-risk users on each report. Descriptive statistics were used to analyze differences between methods. Results: The PPV was highly variable between reports. The PPVs among the ADM, TPS, and EHR reports were 3.28%, 6.82%, and 23.88%, respectively. False positives were high among all reports (96.72%, 93.18%, and 76.12% for the ADM, TPS, and EHR reports, respectively). Conclusions: A report from the EHR has the highest PPV to detect high-risk employees who may be diverting CS. However, false positives were high for all reports, indicating that significant improvements are needed in the development of accurate and reliable software to detect potential and actual CS diversion.