According to Wolters Kluwer’s 2025 State of Drug Diversion Report, 81% of healthcare leaders believe diversion incidents at their institutions continue to occur and go largely unreported. Those two figures describe the same failure: programs built to track incidents cannot see behavior.
Most incident-based diversion programs share three structural weaknesses:
- They review transactions in isolation, which means a pattern of small, repeated behaviors never coheres into a signal
- They generate alert volume that outpaces the capacity of diversion teams to investigate meaningfully
- They surface problems reactively, after a behavior has repeated enough times to become obvious on its own
Behavioral baselines and analysis are the two mechanisms that change this. Together, they surface risk earlier, before a pattern has repeated enough times to become obvious, and reduce investigation noise by giving compliance teams the behavioral context to distinguish a one-time error from an escalating problem.
Controlled substance tracking systems that only react to one-off events cannot do either. The question for pharmacy and compliance leaders is whether their current program can see the difference between a mistake and a pattern.
Why Incident-Based Detection Falls Short
Traditional diversion programs are built around transaction-level flags: a missing count, a documentation variance, an override without a matching order. Each event is reviewed in isolation, which means the behavior driving those events often goes unseen until it has repeated itself enough times to become undeniable.
The False Negative Problem
For a 500-bed hospital, national estimates suggest 25–75 individuals may be diverting at any given time, yet most hospitals investigate five or fewer cases annually. Monthly discrepancy reports and one-off audit flags cannot close that gap. They suffer from low sensitivity by design, because they assess each transaction independently rather than looking for behavioral drift across time.
That structural weakness shows up in confidence levels. Only 33% of healthcare leaders surveyed in the Wolters Kluwer’s 2025 report were “very confident” in their program’s ability to detect and prevent diversion, despite most organizations having updated their policies to meet current regulatory requirements.
The Noise Problem
When every documentation variance generates a flag, investigators spend time on low-risk events and miss the ones that matter.
Bluesight’s 2025 Diversion Trends Report, drawing on 266 million controlled substance (CS) transactions across more than 1,100 hospitals, found that 6% of all transactions contained documentation variances requiring further review. At that scale, manual review is not a realistic strategy. Compliance teams cannot work through that volume and still maintain meaningful oversight of the behaviors that actually indicate risk.
Without behavioral context, a one-time documentation error and escalating diversion risk look identical in a discrepancy report. That ambiguity produces investigation fatigue and a program that reacts more slowly than the behavior it is trying to catch.
What Pattern Recognition Actually Means
Pattern recognition shifts the unit of analysis from the transaction to the person. Instead of asking whether a specific event crossed a threshold, it asks whether a person’s behavior over time has deviated from their own baseline in ways consistent with diversion risk.
Behavioral Baselines Across Users, Time, and Workflow
Effective drug diversion monitoring establishes individual behavioral baselines: what a given user typically accesses, when, at what volumes, and against what patient acuity. Deviations are scored against that individual baseline, not a static institutional threshold that cannot account for role, unit, or shift differences.
As the governing standard for diversion program design, the ASHP Guidelines on Preventing Diversion of Controlled Substances explicitly recommend monitoring software with advanced analytics capable of identifying anomalous behavior specific to the facility.
A 2024 national survey of CS diversion practices across U.S. hospital pharmacies found that over 70% of respondents with dedicated surveillance teams used diversion monitoring software, and that larger institutions showed meaningfully stronger compliance with behavioral detection standards than facilities relying on manual methods.
Relevant behavioral indicators for pattern-based programs include:
- Frequency of early medication returns relative to peer cohort
- Waste variance clustering by shift or unit
- Override patterns that correlate with specific patient types
- Time-of-access anomalies that fall outside the normal workflow for a given role
Baselines are only part of the picture. The time dimension is what turns a behavioral flag into a defensible risk signal.
Effective Analysis Across Time
A single waste discrepancy may be noise. The same discrepancy repeated across 90 days, concentrated on one shift, by one user, in one unit, is a pattern.
Controlled substance tracking software that stores and correlates data longitudinally allows programs to identify escalating behavior before it becomes a confirmed diversion case or a regulatory exposure. It also changes the nature of the investigation when a case does proceed. A documented behavioral trend is far more defensible than a collection of isolated transaction flags. It shows a progression, not a moment.
Longitudinal analysis only works when the underlying data is complete. Gaps in source coverage create gaps in the behavioral picture, which is where diversion hides.
Multi-System Data Integration
Pattern recognition requires a complete view of how CS move through the organization. That means pulling from automated dispensing cabinets (ADCs), electronic health record (EHR) administration records, wholesaler data, time-and-attendance systems, and controlled substance vault logs simultaneously.
Bluesight’s ControlCheck automatically reconciles 95% of transactions by synthesizing data across all of those sources, removing the manual reconciliation burden and ensuring behavioral baselines reflect actual clinical workflow rather than just cabinet access. Programs that monitor only ADC access miss entire categories of risk because diversion does not limit itself to one system.
The Operational Impact: Less Noise, Earlier Detection
Behavioral baselines and longitudinal analysis produce two concrete outcomes that incident-based programs cannot replicate: earlier detection and less investigation noise. Risk surfaces before it compounds because the system is tracking behavioral drift over time, not waiting for a single event to cross a static threshold. And because cases are ranked by pattern severity rather than alert volume, investigators spend time on signals most likely to represent real risk rather than chasing documentation errors.
The difference between incident-based and pattern-based programs shows up across three operational dimensions:
| Incident-Based | Pattern-Based | |
| Unit of analysis | Individual transaction | User behavior over time |
| Alert trigger | Static threshold | Deviation from individual baseline |
| Investigation input | Isolated event | Documented behavioral trend |
ControlCheck detects drug diversion 6.6x more effectively than other next-generation solutions. That figure reflects signal quality, not alert volume. More alerts is not a better program. Fewer, higher-confidence alerts directed at real patterns of concern are.
The staffing reality makes this matter even more.
Bluesight’s 2025 Diversion Trends Report also found that 75% of hospitals have full-time diversion monitoring staff, most commonly one or two individuals. Pattern-based tools multiply the effectiveness of those limited resources by directing attention where risk is highest rather than distributing it across a high volume of low-value flags.
Reduced false positives also protect staff. Employees who are investigated but cleared face real professional and personal harm. A pattern-based approach requires that risk accumulate to a credible threshold before a formal review begins, which means the program is not burning trust across the organization every time a documentation error triggers a flag.
Regulatory and Compliance Dimensions
The Drug Enforcement Administration’s (DEA) Diversion Control Program requires registrants, including health systems, to maintain adequate controls to prevent diversion. Failure carries substantial civil penalties.
Beyond financial penalties, the CDC has documented outbreaks of hepatitis C and bacterial infections traced directly to healthcare workers who tampered with injectable medications while diverting. The patient safety stakes are documented, recurring, and preventable with the right program infrastructure.
Pattern-based programs generate the documentation trail regulators expect:
- Longitudinal transaction records tied to individual users
- Behavioral risk scores that contextualize flagged events
- Investigation timelines showing when risk was identified and how it escalated
- Case resolution data demonstrating program effectiveness over time
The ASHP guidelines call for key performance indicators (KPIs) across the full medication use cycle, covering procurement, prescribing, dispensing, administration, and waste. Point-in-time audit tools cannot generate those metrics because they were not designed to track behavior across the full cycle. Pattern-recognition platforms are.
Proactive programs are also better positioned for Joint Commission reviews and state board of pharmacy audits. Regulators increasingly evaluate the sophistication of a facility’s surveillance methodology, not simply whether a diversion program exists.
Building or Upgrading to a Pattern-Recognition Program
Pharmacy and compliance leaders evaluating their current program should start with a few direct questions:
- Does your narcotics tracking software establish individual behavioral baselines, or does it flag against static thresholds?
- Are you integrating data from ADCs, EHR, wholesaler, and time-and-attendance systems, or monitoring one source at a time?
- Can your system surface risk trends longitudinally across weeks or months without requiring manual data pulls?
- When a case is confirmed, do you have a behavioral trend record that supports the investigation, or only the triggering incident?
The ASHP CSDPP Assessment Tool offers a structured framework for identifying gaps across procurement, dispensing, and administration. It is a useful baseline before evaluating software because it surfaces where a program’s current methodology breaks down, not just where documentation is incomplete.
Health systems that benchmark their diversion metrics against peers gain external context for evaluating whether their detection rates reflect program effectiveness or systemic blind spots.
Bluesight’s Diversion Collective gives member health systems access to that benchmarking alongside a community built around shared diversion program development.
Incident-Based Programs Have a Ceiling
Incident-based detection can only see what it is looking for. A missing waste signature. A count discrepancy. An override outside policy. Each of those is a data point, not a story.
Pattern recognition changes the question from “did something go wrong here?” to “is something going wrong with this person’s behavior over time?” Longitudinal analysis answers that question by tracking behavioral drift across weeks and months.
Behavioral baselines give that drift meaning by showing when a user has deviated from their own normal, not just from an institutional average. The result is risk surfaced earlier and investigation noise reduced, which is exactly where the gap between reactive and high-performing diversion programs lives.Health system pharmacy and compliance leaders who want to see where their program stands can download the 2025 Diversion Trends Report for a nationwide look at detection rates, documentation variance trends, and staffing benchmarks across more than 1,100 hospitals. Those ready to evaluate what pattern-based drug diversion monitoring looks like in practice can request a ControlCheck demo.