DEEP DIVE: Opioids ~ Unsupervised Methods for Kick-back Detection

Alternative Title: How to kill 6 days during Spring Break. The unmasking of the opioids epidemic over the last several years has brought new scrutiny to marketing practices associated with high-risk pharmaceuticals products; Exposé journalistic efforts have been especially effective in shedding light on how misinformation and miscalculations of associated risk factors catalysed a torrent … Continue reading DEEP DIVE: Opioids ~ Unsupervised Methods for Kick-back Detection

Open Payments Data #4: Things to come.

Sorry – no visualizations; Just a quick preview of things I’m hoping to roll out in the next month. I am going to try to do something a little unorthodox: use Rstudio’s Shiny framework to launch data exploration app (ahem!). …Sure, people use Shiny to build dashboards all the time, but the I don’t see … Continue reading Open Payments Data #4: Things to come.

OpenPayments Data #3: Subsidiary Filings Prt. 2

First, some context: The requirement for direct-to-doctor (D2D) marketing transparency was enacted by Physician Payment Sunshine Act as part of ACA reforms in 2010. The call for transparency was initiated in response to concerns that D2D marketing practices by pharmaceutical & medical device companies may exert on undue influence on physician prescription practices. These concerns … Continue reading OpenPayments Data #3: Subsidiary Filings Prt. 2

OpenPayments Data #1: Activity Distributions

I’m finally getting to a point where I can start exploring CMS Open Payments data after a couple months of on-and-off tying in complementary datasets. Spoiler: There’s probably going to be a decent amount of posts following this one on pharmaceutical marketing practices. I’m starting things off with a series of visualizations of how payments … Continue reading OpenPayments Data #1: Activity Distributions

rbindlist() for all of you list-of-dataframe monsters

ALAS! Why is gov’t data so terribly structured?! I’ve been working with the CMS Open Payments data recently because the annual datasets offer enough granular info to allow for good modeling practices and pharmaceutical marketing practices are pretty interesting from a policy stand-point. My only harp with the data is it’s poor data structure and … Continue reading rbindlist() for all of you list-of-dataframe monsters

Address Data Matching Methods for Sanctions Compliance

Too many the implemented models out there: On a serious note, I don’t ever want to hear the saying “garbage in, garbage out” ever again — but alas, I have no intentions of hunkering down in cave until the end of time. This proverbial phrase is thrown around so frequently in compliance circles that we … Continue reading Address Data Matching Methods for Sanctions Compliance