Healthcare organizations lack reliable metrics to measure true drug diversion risk, with confirmed diversion cases reflecting detection capability rather than actual prevalence, according to commentary published in MedCity News. While health system leaders invest heavily in diversion safeguards based on reported cases and enforcement actions, confirmed diversion numbers do not accurately represent how much diversion is actually occurring.
The fundamental challenge lies in the national data landscape, which is dominated by underreporting due to reputational concerns surrounding confirmed cases. Diversion teams analyze dispensing cabinet discrepancies, waste documentation inconsistencies, peer-group outliers, and behavioral red flags, relying on reports from co-workers and patients. However, these are signals indicating where to investigate, not definitive evidence of diversion. Too often, healthcare organizations equate “low confirmed cases” with low risk, when in reality “low confirmed cases” usually reflects low detection maturity rather than actual low diversion rates.
According to Lauren Forni, PharmD, MBA, Senior Director of Clinical Strategy at Bluesight, “Confirmed diversion cases do not equal true prevalence—they are directly dependent on investigative proficiency, tooling, and the bandwidth of the teams doing the work.” She notes that without transparency and consistency in available data, diversion is often discussed as if it were a well-measured problem with reliable benchmarks, when in fact there are no widely available metrics to validate how often suspicious activity is triaged, escalated, substantiated, or dismissed.
Improving Investigation Infrastructure
Healthcare organizations must shift from treating diversion data as an output to treating it as a strategic engine for operational change. Forni recommends that organizations establish behavioral baselines to improve accountability to policy and procedures, track signal lineage to trace every investigation back to its original signal, implement case documentation capturing what was reviewed and what evidence was considered, and create continuous feedback loops ensuring every investigation contributes to stronger monitoring.
Teams should normalize uncertainty by establishing a transparent, reproducible methodology for all reviews, start with testable hypotheses about observed patterns, and create learning loops through structured oversight reviews and root cause analysis. Successful diversion programs will not be defined by having the most or fewest confirmed cases, but rather by their ability to demonstrate confidence in their approach to preventing patient and provider harm through improved investigation infrastructure and detection maturity.
This article is an AI-assisted summary. All facts and figures are drawn from the original report: https://medcitynews.com/2026/02/the-illusion-of-diversion-data-why-confirmed-diversion-counts-misrepresent-true-risk/