With the US Sunshine reporting data fast approaching March 31, compliance teams are heads down in getting the spend records collected, audited and ready for final reporting. The frenzy of systems issues (both internal and CMS), personnel challenges and vendor management makes the race to the finish line a close call for most compliance team. How does one take a break to analyze the data before it goes in for submission? What are the risks for submitting data in the public domain without a pre-audit and analytical review?

After conversations with a variety of compliance teams, we narrowed down the risks to three broad categories:

1. Incomplete Spend Report
   1. Missing spend transactions
2. Incorrect Spend report
   1. Spend amounts are incorrect
   2. Improper classification of spend
3. Suspect Spend
   1. False Claim / Fraud
   2. Questionable spend (excessive for a category)
   3. Unethical spend

As evidenced by the case of Insys Therapeutics and Olympus, the risks of getting trapped for any of the above issues go beyond just the penalties imposed by CMS. The fines are many fold and the reputational risk many multiples of the dollar amount at stake.

Why aren’t more compliance teams reducing the risks and ensure monitoring programs are actually handling the exceptions identified above? With an average compliance department handling several thousand spend records, this becomes a challenge in data analysis more than it is in operational compliance. Playing Sherlock with several hundred thousand spend records many of which are under $10, requires sophisticated data analytics techniques hitherto unavailable to compliance teams. Looking at the type of spend and number of spend transactions to go through before submission, we are summarizing the data related challenges into four categories. For each category defined below, we are also recommending some analytical techniques to make the workload of compliance teams lighter.

1. Volume of data:

   1. Too many spend transactions to individually monitor
   2. Aggregated transactions require context and right tools

The average spend volume for companies in 2014 was:

Figure 1.1: Average transaction volume for companies as published by CMS for reporting year 2014.

Recommendations:

• If you are finding yourself in the top quadrant of the manufacturers with several tens of thousands of transactions, you may want to look beyond Excel for more sophisticated BI and data analytics toolsets to analyze and visualize this data for easy identification of issues and outliers.
• Several large manufacturers have already embedded data analysts as full time team members part of the compliance function. New titles such as “Aggregate Spend Analyst” and “Transparency Data Analytics” are starting to appear which are healthy signs for compliance functions.

1. Method

   1. Tedious manual effort required per individual spend record/exception
   2. Systematic process for data analysis, aggregation and reporting are non-existent
   3. Industry leading aggregate spend tools offer limited capabilities for rich analytics

Individual transaction verification can be outsourced to third party audit firms and excel can be used for quickly spotting threshold violations. However, compliance teams need methods to look at the spend holistically for systematic identification of outliers and unusual spending patterns.

1. (b)

Figure 1.2 (a): Visually spotting outliers in “food and beverage” from 2014 CMS data for a device manufacturer. (b)comparing company A’s food and beverage” bell curve with the rest of the industry for 2014 CMS published data.

Recommendations:

• Utilizing Business Intelligence tools to visualize and inspect data holistically. Outliers can be spotted using a Box Plot for example that is available in most industry leading BI tools.
• Create a systematic workflow with inspection scenarios that are repeated on a periodic basis throughout the year to ensure continuous monitoring.

1. Accuracy:

   1. Need to engage multiple stake holders for spend verification

An example of this scenario would be compliance teams may want to delegate validation of individual spend transactions to commercial teams. A sales rep/regional manager/district manager could be granted access to a specific dashboard/spend report to view and approve their own records. This will bring higher accuracy across the spend and limit the level of effort required from the compliance departments.

Figure 1.3: Sample current year spending for a given rep delegated to approve. Sales rep/regional heads maybe in a better position to approve their own spend.

Recommendations:

• Spend can be segmented and assigned to specific departments to provide final validation before submission. A workflow can be created where spend is in error or report looks abnormal. Example above illustrate a sales rep view of approving or disputing individual as well as aggregated spend records.
• Utilize compliance teams to ensure system records are collected, centralized and match the receipts for actual spend. Delegate and engage users across the enterprise for final validation. This does require initial setup effort but once it is done right, takes a huge portion of the workload off from compliance teams and provides better accuracy.

1. Benchmarking:

   1. Comparisons to industry and peer organization(s) for determining acceptable spend levels.

In the absence of absolute standards for different types of spend, compliance teams have to rely on patterns generally followed by the industry or their specific peer groups. For example, compliance teams should be able to compare advisory board or speaker fees paid by them to a specific physician vs paid by others in the industry to the same physician.

Figure 1.4: Shows speaker fees and advisory boards for a single physician and how different companies are negotiating different amounts (from CMS published 2014 data).

With over 15 million spend records generated in just two years of reporting the volume of data is only going to grow. Add to this the EFPIA related spend getting published by EU this year, there will be more and more data to analyze, compare and utilize for reducing risks. It may be worth adding a full time ‘data analyst’ type role into the team to get full value for the effort. In the interim, and for the current March 31^st submission, you may consider utilizing Open Payments Analytics – an analytics platform built just for compliance teams to reduce risk pre-submission risks.

By: Ned Mumtaz

About the Author Ned is the Associate Partner and Practice Leader for Pharmaceutical Services at Streebo where he is leading the transparency directive program for both US as well as EU.

With over 20 years of experience in the pharmaceutical industry, Ned has worked with various pharmaceutical and biotech companies in the past. He has served as Life Sciences Practice Leader at SAP/Business Objects and EMC consulting. Ned has also lead several Sunshine implementation programs in the US as IT Director for Otsuka, Eisai, Wyeth, and others.