The Illusion of Diversion Data: Why Confirmed Diversion Counts Misrepresent True Risk

For years, healthcare organizations have been told that drug diversion is a persistent and growing problem. Health system leaders, pharmacy teams, and diversion committees read reports of egregious confirmed cases, see headlines of the financial fines, and invest heavily to safeguard our organizations. Yet, despite all this focus, one uncomfortable truth remains: We do not know how much diversion is actually happening in our health systems.

Unfortunately, there are no widely available metrics to validate how often suspicious activity is triaged, escalated, substantiated, or dismissed. While some vendors offer insight based on the data they have available to them, those views are inherently limited. Perhaps most critically, 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.

Despite the lack of transparency and consistency with available data, diversion is often discussed as if it were a well-measured problem with reliable benchmarks. Diversion committees compare case counts, reference enforcement actions, and take comfort in “no findings,” without fully acknowledging that the numbers we rely on reflect detection capability more than actual risk. This distinction matters more than most of us are willing to admit.

The elusive nature of diversion transparency

Despite the attention drug diversion receives, it remains somewhat “taboo” to discuss broadly because of the serious consequences associated with confirmed cases. Even with the best intentions behind leadership and regulations, the reality of these consequences creates built-in pressure to resolve cases quietly and minimize external visibility. This results in a national data landscape dominated by underreporting.

However, the absence of evidence does not mean the evidence of absence.

The illusion of diversion data

What is the average percentage of confirmed diversion cases? This is a near-impossible data point to find, and the data available is only representative of the narrow end of a wide funnel. Diversion teams analyze dispensing cabinet discrepancies, waste documentation inconsistencies, peer-group outliers, and behavioral red flags, while often relying on reports from co-workers and patients. In reality, these are signals, not truths. They tell us where to look, not necessarily what we will find.

Too often, healthcare organizations equate “low confirmed cases” with low risk. But confirmation is dependent on the prowess of the investigator. In practice, “low confirmed cases” usually reflects something else entirely: low detection maturity.

What does “better diversion data” look like?

For many, diversion metrics live and die in spreadsheets – tracked diligently, reviewed occasionally, and rarely converted into meaningful operational change. This is the central flaw in how healthcare often approaches diversion – data is treated as an output, not an engine. Data is only as valuable as the decisions, behaviors, and control it informs.

Even if national diversion benchmarks existed, most organizations would still be unprepared to use them effectively. Without a robust internal data foundation – consistent definitions, structured case documentation, and longitudinal signal tracking – external benchmarks would offer little more than comparison without context. Before the industry can worry about national standards, every organization must first understand how to create, interpret, and act on their own data.

So what does better diversion data actually look like? Data can transform investigations from isolated events into continuous intelligence engines that improve detection, accelerate learning, and strengthen over time. This includes:

• Behavioral baselines: accountability to policy and procedures improves overall practice, generating stronger peer-based comparisons to more effectively distinguish risk of diversion.
• Signal lineage: the ability to trace every investigation back to its original signal, as well as the ability to extrapolate those signals to identify patterns across cases.
• Case documentation: capturing what was reviewed, what evidence was considered, and what systemic gaps were identified.
• Continuous feedback loop: ensuring every investigation, whether confirmed or unsubstantiated, contributes to stronger monitoring and more precise analysis.

How teams can become stronger investigators

Even the most highly skilled teams encounter challenges – long detection timelines, investigator fatigue, data volume, inconsistent workflows, unconscious bias, or lack of an established learning loop. These challenges are not signs of weak teams, they are symptoms of an immature detection infrastructure.

Organizations must expand their learning lens. Confirmed cases are valuable, but they represent only a fraction of what investigators review. Triaged, unsubstantiated, and inconclusive cases are equally important. They reveal where controls may have failed silently, where analysis was too broad, where workflows created noise, and where risk may be emerging, even if not yet visible.

To improve, teams should:

1. Normalize uncertainty: establish a transparent, reproducible methodology for all reviews and investigations; document all findings (even when inconclusive) to establish patterns over time
2. Start with a hypothesis: establish a testable hypothesis about initial observed patterns and signals; focus on specific signals and draw connections between signals to prove or disprove the hypothesis
3. Create a learning loop: implement a structured process of oversight reviews and root cause analysis for both confirmed cases as well as triaged reviews.
4. Establish measures for impactful and actionable data that reflect process evolution (time to detection, escalation ratio, confirmed case percentage)

The strategic reframe healthcare leaders must embrace

The success of a diversion prevention program is not defined by how many individuals are “caught.” It is defined by the strength of the investigation systems behind every decision – systems capable of producing reliable, defensible truth even when diversion is ultimately ruled out.

This requires a fundamental shift in how healthcare organizations think about diversion:

• Transparency becomes protection – not exposure 
• Detection becomes proactive – not reactive 
• Diversion programs become strategic – not punitive
• Regulatory readiness becomes embedded – not episodic

Ultimately, the greatest limitation in diversion transparency is not the absence of data, but rather, investigation maturity. Organizations that invest in strengthening their investigation framework will not only reduce regulatory risk, they will create their own benchmarks, improve institutional trust, and quietly set the standards others will inevitably follow.

Successful diversion programs will not be defined by having the most or the fewest confirmed cases. They will be defined by something far more defensible: their ability to demonstrate, with confidence, their approach to preventing patient and provider harm.

Picture: Getty Images, thomas-bethge

Lauren Forni, PharmD, MBA, is Senior Director of Clinical Strategy at Bluesight, where she works with health system leaders to modernize medication management, strengthen diversion oversight, and translate complex regulatory and operational challenges into scalable, data-driven solutions. As a former Assistant Director of Pharmacy at Brigham and Women’s Faulkner Hospital, she brings frontline operational insight to industry-wide strategy. She is also the co-founder of the Diversion Collective, a multidisciplinary collaborative designed to elevate how healthcare organizations measure, manage, and mitigate diversion risk through shared expertise and practical application.

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