The American opioid epidemic has brought tremendous scrutiny to pharmaceutical wholesalers and manufacturers that play a role in distributing and developing products classified as controlled substances. A number of wholesalers have already paid out large settlements connected to the oversupply of opioid pain medications. Among those reaching a legal settlement was wholesaler McKesson, which  settled with the United States Department of Justice (DOJ) for $150 million as a civil penalty for alleged violations of the Controlled Substances Act (CSA).  

The focus on accountability for controlled substances is now shifting to manufacturers as evidenced by a host of cities and states pursuing legal action against drugmakers as well as the US Drug Enforcement Agency (DEA) making use of channel data to proactively identify and investigate atypical pharmacy level purchases. These circumstances reinforce the pharmaceutical industry’s need for better analytical and reporting tools to monitor suspicious orders of controlled substances.

Gaps in Traditional Suspicious Order Monitoring

Purchase Order Monitoring
Traditionally, the pharmaceutical industry has monitored purchase orders for unusual sizes and patterns. Manufacturers should continue this practice as part of compliance and risk monitoring. However, there are notable gaps when exclusively relying on the traditional approach.

  • Only wholesalers and distributors order directly. These orders are placed in aggregate on behalf of their end customers.
  • Several instances of documented criminal behavior occurred when pharmacies placed orders with multiple wholesalers

Pharmacy Monitoring
A comprehensive solution must also monitor and potentially report whether individual pharmacies are exceeding reasonable purchase thresholds for Schedule 2 drugs. Historically, manufacturers have explored chargeback data as a possible solution. Chargebacks data only provides visibility to contract sales. Most brand manufacturers don’t contract directly with chains or pharmacies. Therefore, only a small percentage of orders is visible.   

A New Approach: Enriched 867 Data

Enriched 867 data provides complete visibility to all ordering at the individual ship-to location level across wholesalers and distributors. Table 1 compares Enriched 867 data to Chargebacks.

Chargebacks Enriched 867 Data Comparison

Table 1: Comparing the Enriched 867 data model to the Chargebacks dataset

In this post we’ll introduce more robust analytical reporting solutions generated from enriched 867 data. These innovative reports can enhance the accuracy of monitoring and reporting suspicious orders at the pharmacy level. And notably, we’ll look at how these analytical reports would’ve helped close glaring gaps exposed in recent crisis years.

Zip Code Level Controlled Substance Order Analytics

There are several additional factors a pharmaceutical manufacturer must consider when developing a complete solution for monitoring and reporting on suspiciously large orders:

  • Per capita population
  • Regional distribution of patients likely to abuse opioids (comorbidities)
  • Pharmacies with unusual ordering patterns (frequent purchases across distributors)

The need to account for these factors is obvious when we look at what’s already transpired.  

One of the most extreme examples of prescription painkiller oversupply took place in a small town in West Virginia, a state with the highest death rate due to drug overdoses in 2016 (52.0 per 100,000 according to the Centers for Disease Control). In a 10-year period from 2006 to 2016, two pharmacies in the town of Williamson, WV, received shipments totaling 20.8 million pills. The full article by the Charleston Gazette-Mail details similar instances of oversupply in the state.

In a statement following its investigation of the oversupply in West Virginia, the U.S. House Energy and Commerce Committee released a statement that in part read, “The volume appears to be far in excess of the number of opioids that a pharmacy in that local area would be expected to receive.”

Enriched 867 Data Enables Market-level Comparisons

As extreme as the West Virginia example is, it illustrates a manufacturer’s limited ability to generate analytical reports that show expected demand for a geography against the amount of controlled substances ordered/supplied. IntegriChain has developed analytics that crosswalk U.S. Census population counts to pharmacy sell-in data at the zip-code level.

On top of per capita dispensing data, IntegriChain’s use of enriched 867 data makes it possible to create suspicious order monitoring analytics that integrate market benchmark data. The reports compare both longitudinal brand level purchasing trends (brand decile) and competitive market products purchasing trends (market decile) at a pharmacy. This additional layer of insight lets a manufacturer determine whether a trend is inappropriate for their brand or consistent with similar products.

Zip Code Level Reporting Brand vs. Market

Figure 1: Zip code level reporting brand vs. market

Zip code level reporting Brand vs. National

Figure 2: Zip code level reporting brand vs. national

The first report pictured here compares zip code-level per capita dispensing rates for a brand vs. the national average (Figure 1). A second report utilizing zip code-level data compares a brand’s dispensing at a local level vs. market peers (Figure 2).

In the case of the small West Virginia town of Williamson, a basic per capita dispensing analysis would’ve easily marked the pair of pharmacies driving the radical oversupply of opioid painkillers Red Flag Pharmacies whose order quantities dramatically exceeded reasonable order thresholds.

Aggregate Wholesale Distributor Reports

Another blind spot for pharmaceutical manufacturers that became more apparent in recent years is a lack of visibility to wholesale distributor orders in aggregate. A pharmacy order of substantial volume that would typically trigger alarm bells does not look as egregious when spread over three or four different wholesalers.

Aggregate Wholesaler Suspicious Order Monitoring

Figure 4: Aggregate wholesaler reporting at the pharmacy level


Historical Thresholds Suspicious Order Monitoring

Figure 5: Historical thresholds provide expectations for a reasonable order volume

IntegriChain developed analytical reports to provide aggregate order visibility for a POC ID that combine activity at multiple wholesalers (Figure 4). A second report leverages historical order data to identify greater than expected volume in real time (Figure 5) .


Heightened scrutiny around suspicious orders of controlled substances will continue and likely increase in the coming years. Investment in innovative analytics that combine disparate commercial data sources is the surest way for manufacturers to achieve true accountability in this area and prevent abuse. Suspicious order monitoring analytics and reports identify pharmacies that have high levels of product purchasing, trace high orders to the originating distributor, and allow manufacturers to establish reasonable order thresholds.

About the Author

Shivani Patel is Solutions Principal on IntegriChain’s Industry Solutions team. She is responsible for many of IntegriChain’s Distribution and Inventory custom data analyses and reports. Prior to joining IntegriChain, Shivani was Operations Research Analyst at ZS Associates. Shivani holds a Masters Degree in Applied Statistics from Columbia University. 

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About the Author

Shivani Patel

Shivani Patel

Solutions Principal, Industry Solutions