Fuqua Insights Podcast: What Happens When Drug Company Payments to Doctors Go Public?
Fuqua Insights Podcast: What Happens When Drug Company Payments to Doctors Go Public?
Professor Tong Guo explains how mandated transparency didn't reduce pharmaceutical payments to physicians—instead, it taught companies to optimize them
When the federal government mandated that pharmaceutical companies publicly disclose every payment to physicians—from conference sponsorships to consulting fees—policymakers expected transparency to reduce potential conflicts of interest. Instead, the payments kept flowing, and companies learned to optimize them.
In this episode, Professor Tong Guo, an associate professor of Marketing at Duke University’s Fuqua School of Business, discusses her study of the Sunshine Act—a federal law requiring pharmaceutical and medical device companies to publicly disclose payments to healthcare providers. Published in the Journal of Marketing Research (2021), Guo's research using advanced machine learning methods called causal forests analyzed $100 million in payments between 16 antidiabetic brands and 50,000 physicians. Her findings reveal a nuanced reality: while total payments did not decline significantly, they shifted toward physicians who prescribe more expensive drugs and generate higher ROI for firms.
As Guo explains, most disclosed payments are legal, including sponsorships for events, conference travel, and educational presentations. "Much of these expenditures are considered legal," she notes, "so it's natural that it doesn't come with much pressure to cut it down."
"For firms, the number one rule for them to run their business is always to think about their ROI model," she explains. The transparency regulation gave firms information about which competitors were reaching out to which physicians and when, allowing them to optimize their existing relationships for maximum return. When transparency gives all competitors access to the same information, firms don't retreat—they optimize.
For MBA students and professionals, Guo's findings offer critical lessons extending beyond healthcare. Transparency doesn't always lead to restraint. Understanding who benefits from newly available information—and how—is essential across industries, from healthcare to digital marketing. As Guo points out, similar disclosure regulations now apply across industries —from TikTok influencers required to disclose brand sponsorships to financial services and beyond. "Transparency regulations would not necessarily lead to drastic changes of how people practice their business," she says. Different parties have different capabilities to leverage disclosed information, potentially creating new competitive advantages rather than leveling the playing field.
(music)
00:03
Scott Dyreng
Hello there. My name is Scott Dyreng, and I'm the Senior Associate Dean of Innovation at the Fuqua School of Business. Today. I'm joined by Professor Tong Guo, associate professor of marketing at Fuqua and Duke Department of Economics, as well as a faculty affiliate at the Duke Margolis Center of health policy. Her research focuses on the causal role of information in marketing and its policy implications, especially for healthcare new technology and consumer protection. In this episode, we'll explore her research on the unintended consequences of the Sunshine Act, revealing how well-intentioned policies can have complex effects in the real world. Tong, it's great to have you here.
Tong Guo
Thank you for having me today.
Scott Dyreng
So let's start with the big picture. What prompted you to study the Sunshine Act disclosure rules?
Tong Guo
Yeah, I've always been very intrigued by how complex the whole industry of healthcare is, especially given so many different parties are involved in making the decisions for everybody, for patients, for doctors and for hospitals.
01:08
At the same time, you probably noticed there's so much money invested in the whole discipline. Surprisingly, as the first impression some area like healthcare should be an area with heavy investment into R and D, but quite contrary to many layman beliefs, the top expenditure goes to marketing. So during my graduate study, I was caught by this very shocking phenomenon in this discipline. And I wonder, how does the money move the needles around in this discipline, and therefore I decided to dedicate much of my graduate studies towards understanding healthcare marketing. In particular, regulators have noticed that there's so much expenditure in this domain involved with paying towards physicians, and they come up with this so-called Sunshine Act, which aims at disclosing every details between medical device and pharmaceutical companies and healthcare providers.
02:07
That's basically the initial motivations of why I started looking into the regulations in this domain. And as I spent more time and effort studying this area, the more I realized so much unintended things are going on with this well intentioned policy
Scott Dyreng
Yeah, very interesting. One of your key findings is that, on average, payments to doctors did not decrease, but that there's a deeper story. Can you walk us through why the average masks some of the big differences underneath?
Tong Guo
Sure. First of all, I do want to give this disclaimer that much of these expenditures reported under the Sunshine Act are considered legal. So they are fine and they're not in cash payment for the most part. They're mostly sponsorship for events, conference travels, informational presentations and things like that. So much of these payments are legal, and therefore it's natural that it doesn't come with much of a pressure to cut it down.
03:10
In addition, many of these companies build their marketing budget at the beginning of the marketing cycle, usually annually, so the budget would work as a whole, and it doesn't involve too much of a personalization towards individual relationships. That's why, on average, there is very little reason to expect a huge change on the payment because of a simple transparency law. However, as we dig deeper, it seems that firms do have more capability to make use of this piece of information after transparency law, they can further optimize across all the relationships they maintain with health care practitioners. That's why, if you allow a little fine-grained analysis of the individual changes, we do detect some differences, some changes before and after that Sunshine Act, but still going back to the initial point. Given the payments are all legal and they're mostly events driven, it's very hard to imagine how, on average, the payment would change significantly because of the simple regulation.
Scott Dyreng
Yeah. Okay, great. So on average, they don't change, but you do find that some payments actually increase for expensive drugs and top prescribing physicians. What does that tell us about how pharmaceutical firms responded to the new transparency measures?
04:41
Tong Guo
Yeah, this is, as I mentioned earlier on, this is a very nice continuation of what we've been discussing. For firms, the number one rule for them to run their business is always to think about their ROI model. So they will take all measures, exploit all resources to make good use of market information so that they could improve their ROI part of this transparency regulation gives them this necessary information they can optimize on so they have information about who their competitors are reaching out to for what reasons, during which time period that allows them to further maximize their returns on their existing relationships that they manage. That's why they know maybe by tweaking a little bit of my marketing dollars towards more influential physicians, it will pay me even more. Therefore we see these increased payments towards more expensive drugs and top prescribing physicians all because they give them even more they give firms even more ROI. So in a nutshell, all the firms just work with their ROI model to bet their money on the most rewarding partners.
Scott Dyreng
Your analysis relies very heavily on machine learning, specifically a method called causal forests. For those of us who are not data scientists, can you explain what causal forests do and why they were the right tool for this study?
Tong Guo
That's a very important question when we think about leveraging all these new technologies and tools coming from machine learning and AI. So a simple summary of what causal forest can do for us is that we can capture fine grained treatment effect over and above the average treatment effect that usually people look at in these experimental studies. For causal forest, it allows us to speak a little more carefully about individual relationships, in my case, between firms and physicians,
06:51
I think they're the perfect tool for this study, because, as I said, on average, you don't -- theoretically, empirically -- you don't have a strong reason to expect a huge change on average. So what matters more is how firms reallocate their marketing resources towards different partners and capturing that uneven change across different relationships is really important, and causal forest offers that capability to capture the heterogeneity in the changes. With the academic word, it’s called heterogeneous treatment effect.
Scott Dyreng
Yeah, very cool. In an era where AI and data transparency are reshaping many industries, what lessons does this research offer about the promises and limitations of using machine learning in policy evaluation?
Tong Guo
First and foremost, as I mentioned, to the previous question, causal forest as an example of such AI tools are super powerful in helping us understanding the fine-grained changes due to any policy intervention. That's the most immediate benefit if we think about using AI and machine learning tools to understand policy impact. On the other hand, what I think would be crucial whenever we apply AI or machine learning tools to our analysis is to always remember you need a human mind behind all those analysis. Yes, with state of the art statistical and machine learning tools, you can capture more fine-grained data patterns. But how to interpret these patterns? What do they really mean? What is the most prioritized questions and issues that you want to sort out, and how to incorporate those data patterns and takeaways into your decision making? It's always a decision left to a human instead of AI. So I think it's, it's a huge limitation to all these AI tools -- also a risk to all these AI tools
08:57
Scott Dyreng
Yeah, very, very interesting. We have many MBA students here, obviously at the Fuqua School of Business, how should an MBA student think about the unintended consequences of transparency policies like public disclosure payments in industries where they might end up working or leading teams?
Tong Guo
That's a great question. I would say this public disclosure or any transparency regulation really applies to industries beyond healthcare. You nowadays see similar disclosure regulations almost everywhere. If we think about an MBA student who wants to be a TikTok influencer, and he happens to receive some sponsorships from brands, he has to disclose all those brand sponsorships as well for every sponsored videos. So public disclosure regulations really apply to areas way beyond healthcare. How should we think about the unintended consequences? First of all, transparency regulations would not necessarily lead to drastic changes of how people practice their business, but it's always important for whoever is involved in this practice to be clear about who else are involved in the whole process and how would they use this piece of information that’s getting disclosed. Different parties would have different capabilities of leveraging the power of fuller information, and therefore that might create a different comparative advantage or disadvantage. So for our MBA students, it might be useful to think about who else are getting these pieces of information and the ways they're using the information. It's also a lesson for them to reflect on themselves.
Scott Dyreng
Tong, thank you so much for joining me today and chatting about your very fascinating study.
Tong Guo
Thank you so much.
(music)
Bio
Tong Guo is an associate professor of Marketing at Duke University’s Fuqua School of Business and holds a courtesy appointment in the Department of Economics. She also serves as an affiliate faculty member at the DukeMargolis Center for Health Policy.
Guo’s research sits at the intersection of quantitative marketing, causal inference, and health information policy. She applies advanced methodologies — including econometrics, machine learning, and online experiments — to examine how information disclosures and misinformation influence decision-making in healthcare, consumer products, and regulated markets.
She earned her Ph.D. in Marketing from the University of Michigan (2018), and holds an M.A. in Economics from Duke University, along with dual B.A./B.S. degrees in Economics and Biological Science from Peking University.
Guo’s work has been recognized with prestigious honors such as the MSI Young Scholar award, selection as a faculty fellow at both the AMA Sheth Foundation Doctoral Consortium and the ISMS Early Career Scholars Camp, and finalist for the UM ProQuest Distinguished Dissertation Award. She serves on the editorial board of Marketing Science.
Her research has appeared in top journals like Marketing Science and Journal of Marketing Research, with recent studies on misinformation (2022) and green‐food adoption (2023).
She received a recognition as runner-Up for Excellence in Teaching in the 2021–22 Duke Kunshan MMS program.
This story may not be republished without permission from Duke University’s Fuqua School of Business. Please contact media-relations@fuqua.duke.edu for additional information.