For years, healthcare organizations have diligently tracked, reported, and scrutinized a single, seemingly definitive number to gauge the success of their drug diversion prevention programs: the count of confirmed cases. This figure, often low, has been a source of reassurance for leadership and a benchmark for compliance, suggesting that sophisticated systems and vigilant oversight are effectively keeping a dangerous problem at bay. However, a growing consensus among clinical experts suggests this comfort is misplaced, built upon a foundation of data that reveals more about detection capabilities than it does about the actual prevalence of diversion. This disparity creates a critical blind spot, where the absence of alarms is mistaken for absolute safety, leaving patients, staff, and the organization itself unknowingly exposed to significant risk. The crucial question is no longer how many cases are found, but how many are being missed entirely.
When No Findings Is a Red Flag
In the high-stakes environment of healthcare, a report of “no findings” following a potential drug diversion signal is typically met with relief. It implies that controls are working and that the initial anomaly was nothing more than a documentation error or a statistical fluke. Yet, this conclusion overlooks a more unsettling possibility: that the investigative tools and processes in place are not mature enough to uncover the subtle, complex patterns of a sophisticated diverter. The paradox is that while health systems invest heavily in prevention technology, most cannot accurately quantify their true diversion risk, operating instead on assumptions derived from incomplete data.
This prompts a fundamental re-evaluation of what success looks like in a diversion prevention program. What if a consistently low number of confirmed cases is not a sign of a secure facility but a symptom of an underdeveloped detection and investigation infrastructure? An organization that rarely substantiates a case may not be free of diversion; it may simply be ill-equipped to prove it. This uncomfortable truth challenges the industry’s long-held beliefs, suggesting that the real red flag is not a high case count but a system that produces deceptively clean reports.
The Illusion of Safety
The healthcare industry’s reliance on confirmed case counts as the primary metric for diversion risk is a widespread and dangerous practice. This approach incorrectly conflates the ability to detect and prove diversion with its actual occurrence. When an organization compares its one or two confirmed cases per year to another’s, it is not comparing risk; it is comparing the effectiveness, bandwidth, and expertise of their respective investigation teams. The number is an output of a process, not a direct measure of the problem itself.
Fueling this illusion is the inherently sensitive nature of drug diversion. The severe professional and legal consequences of a confirmed case create immense pressure to resolve issues quietly, often leading to a culture of underreporting that skews national data. Because diversion is still treated as a taboo subject, a reliable, transparent benchmark for “normal” is nonexistent. This forces organizations to operate in a vacuum, guided by the flawed principle that “the absence of evidence is evidence of absence.” This mindset not only compromises institutional safety but also leaves health systems unprepared for the rigorous scrutiny of a regulatory audit.
Deconstructing the Data
Confirmed diversion cases represent only the narrowest end of a wide investigative funnel. For every substantiated incident, there are dozens, if not hundreds, of initial signals—dispensing cabinet discrepancies, waste documentation inconsistencies, and behavioral red flags—that are reviewed and ultimately dismissed or left unresolved. To equate a low number of confirmed cases with low risk is a fundamental error in judgment. It ignores the vast amount of data that exists below the surface, data that could reveal systemic vulnerabilities, workflow inefficiencies, and emerging threats that have yet to crystallize into a provable event.
This flaw is magnified when organizations attempt to benchmark themselves against national averages. Without a robust internal data foundation, such comparisons are exercises in futility. External benchmarks offer little more than context-free numbers if an organization has not first established its own consistent definitions, structured case documentation, and longitudinal signal tracking. Before looking outward, health systems must first build the capacity to understand their own internal data landscape. Better diversion data is not a static report; it is a dynamic engine for change built on four pillars: strong behavioral baselines for peer comparisons, clear signal lineage to trace investigations, comprehensive case documentation for systemic analysis, and a continuous feedback loop that ensures every investigation improves the system.
An Expert’s Perspective
The true measure of a successful diversion program is not how many people are caught, but the strength and reliability of the systems used for investigation. This is the central argument of Lauren Forni, PharmD, MBA, a clinical strategist who advocates for a paradigm shift in how healthcare evaluates diversion risk. According to this perspective, the goal is not to maximize captures but to build a defensible process that can confidently and accurately distinguish between a true threat and a false alarm. A mature program is one that can prove a negative—demonstrating with certainty that diversion did not occur in a specific instance.
Even the most highly skilled and dedicated diversion teams face immense challenges: investigator fatigue from sorting through massive data volumes, inconsistent workflows that create analytical noise, and the pervasive influence of unconscious bias. These are not signs of a weak team but symptoms of an immature detection infrastructure that places an unsustainable burden on human analysis. Forni emphasizes the untapped value found within inconclusive cases. These investigations, which are often closed without a definitive answer, are rich with insights. They can reveal silent control failures, confusing workflows, and emerging patterns of risk that a focus on confirmed cases alone will always miss.
From Reactive to Proactive
To build a more mature detection framework, investigation teams must move beyond a reactive, case-focused model. This begins by normalizing uncertainty and documenting all findings, even inconclusive ones, to build a longitudinal risk profile that reveals patterns over time. Investigations should start not with a broad search for wrongdoing but with a specific, testable hypothesis that connects disparate signals and focuses the analysis. Crucially, a learning loop must be created, implementing structured oversight and root cause analysis for all case types—not just confirmed ones—to ensure that every review strengthens the overall system.
This operational shift must be supported by a strategic reframe at the leadership level. Healthcare leaders must learn to view transparency as a form of protection, not exposure. A willingness to openly examine inconclusive cases and potential system weaknesses builds a stronger, more resilient organization. The diversion program itself must evolve from a punitive, reactive function into a strategic, proactive one that anticipates risk and reinforces a culture of safety. By embedding regulatory readiness into daily operations rather than treating it as an episodic event, health systems can achieve a state of continuous compliance and genuine security.
The greatest limitation in achieving true diversion transparency was never the absence of data, but rather, the maturity of the investigation frameworks designed to interpret it. The organizations that invested in strengthening their internal systems did more than just reduce their regulatory risk; they created their own meaningful benchmarks and improved institutional trust. Ultimately, the most successful diversion programs were not defined by having the most or the fewest confirmed cases. They were defined by something far more defensible: their demonstrated ability to confidently and consistently protect both patients and providers from harm.
