The Effect of Information Disclosure on Industry Payments to Physicians

Abstract

In 2019, U.S. pharmaceutical companies paid $3.6 billion to physicians in the form of gifts to promote their drugs. The practice of offering financial incentives has raised concerns about potential conflict of interest. To curb such inappropriate financial relationships between healthcare providers and firms, several states have instituted disclosure laws wherein firms were required to publicly declare the payments that they made to physicians. In 2013, this law was rolled out to all 50 states as part of the Affordable Care Act. The authors investigate the causal impact of this increased transparency on subsequent payments between firms and physicians. While firms and physicians were informed of the disclosure regulation at data collection, complete transparency did not occur until the data were published online. The authors estimate the causal impact of the online data disclosure by exploiting the phased rollout of the disclosure laws across states. They use a quasi-experimental difference-in-difference research design to find control “clones” for every physician-product pair in the states with and without prior disclosure laws, facilitated by recent advances in machine learning methods. Using a 29-month national panel covering $100 million in payments between 16 anti-diabetics brands and 50,000 physicians, the authors find that the monthly payments changed insignificantly on average due to disclosure. However, the average null effect masks some unintended consequences of disclosure, where payments may have gone up for more expensive drugs and among physicians who prescribed more heavily. Interestingly, more popular physicians and heavier prescribers continue to receive high consulting and speaker fees after the disclosure. The authors further explore potential mechanisms that can parsimoniously describe the data pattern.

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

“…Narang et al [ 67 ] apply causal forests, the causal ML framework developed by Wager and Athey [ 68 ] as well as Athey et al [ 9 ] also used in this study (see Section 6), to assess the heterogeneity across shoppers in how mobile app failures affect the frequency, volume, and monetary value of their purchases. Guo et al [ 69 ] assess the effect of a law requiring pharmaceutical firms to disclose their marketing payments to physicians on the firms’ payments to physicians using a Difference-in-Differences approach and assess expected individual-level effect heterogeneity by means of causal forests. Zhang and Luo [ 70 ] incorporate causal forests in their study on modelling restaurant survival as a function of photos posted on social networks.…”

Estimating and evaluating treatment effect heterogeneity: A causal forests approach

“…In economics, Davis and Heller (2017a, 2017b) apply CF to estimate the effects of summer youth employment programs. In marketing, Guo et al (2021) use CF to explore the effect of information disclosure on physician payments. We will show that CF can be applied to causal inference in political science as well.…”

The value of context-specific studies for marketing

“…& Governments or policymakers: Governments and public stakeholders monitor different aspects of the life sciences market. For example, Guo et al (2021) use information reported to the Centers for Medicare and Medicaid Services (CMS)-part of the Department of Health of the government of the U.S.-to study the effect of information disclosure on industry payments to physicians.…”