Who Did Your Doctor Have Lunch With? Open Payments, Sunshine Act, Compliance, Part Deux
On June 30, 2015 the Open Payments database, previously known as the Sunshine Act, containing information for the calendar year 2014 was made available for the public to query via this link on the Centers for Medicare and Medicaid Services (CMS) website.
Physicians and teaching hospitals had 45 days in April and May to review and dispute compliance reporting information by looking at their data here. This was then followed by a period of correction for manufacturers and GPOs to respond, as part of finalizing their aggregate spend submissions, by reviewing and fixing the data.
Since September of 2014, the data for 2013 has been readily available for search, download and general analysis by anyone interested in viewing what payments were made by which manufacturers to physicians, and how they were related to various products (drugs). Over 1M have taken a look at the database of over 4.4M records.
Last year, the program got off to a rocky start with data incorrectly associating payments (lunches, speaker fees, educational materials) to a physician who had the same name, but lived in a different state. The incident received publicity through a blog post by the physician who flagged the errors. See "Dark Clouds for the Sunshine Act".
This came as a bit of a surprise to me, since I, together with many of my colleagues now at Reltio, developed one of the original pioneering master data management (MDM) solutions while at Siperian (subsequently acquired by Informatica), which was designed to address this very issue. MDM for the customer domain, commonly called customer master, was one of the most widely deployed and accepted capabilities utilized by life sciences companies.
Less surprising was the article "Why Pharma Payments to Doctors Were So Hard to Parse" by ProPublica in January that highlighted significant data errors in the form of misspellings of products (drugs). This was not a shock since many life sciences companies were either unsuccessful or had not deployed robust MDM for the product domain or product master, which could have been used to correct basic misspellings and other problematic product classifications.
The fact that there were both customer and product data errors in the submissions mean't that the data was unreliable. This isn't ideal for life sciences' compliance teams or the doctors involved. The public may get misinformed, physician and manufacturer reputations are at stake, and there actually may be useful information contained within the data that is being missed.
Last year we brought the publicly downloadable data into Reltio, which immediately corrected the offending errors through built-in multi-domain master data management. From there we were able to configure an easy to use, Reltio data-driven application to visualize and pivot views from any perspective. Unlike analytics only tools, we continuously cleanse, standardize information prior to reconciliation and analysis. Information cannot provide relevant insights without a reliable data foundation.
The Reltio data-driven application also gave us immediate free text search to find information under any attribute or context, a compliance officers dream come true. Narrowing searches Linkedin-style through search facets to filter and focus on relevant insights, we were not only able to uncover unique viewpoints, but also provide an operational application to go act on the information.
We will be interested to see what the data quality level is in the 2014 database with Open Payments/Sunshine Act Part Deux (My use of the French #2 is a nod to the upcoming EU version EFPIA code reporting coming in 2016).
From our perspective, open payments and EFPIA code data can be rapidly reconciled and made reliable to deliver relevant insights to all that need them. Capabilities such as MDM are fundamental, and life sciences companies, and in this case physicians and the public should expect them to be part of all future generations of data-driven applications. When we were at Siperian 10 years ago this was a hard problem to solve, today it's a mere stepping stone to addressing tougher challenges across any use case that life sciences, or other industries have.