The February 27, 2013 Lab seminar was presented by Daniel Carpenter, Freed Professor of Government at Harvard University, and Edmond J. Safra Center for Ethics Lab affiliate. Professor Carpenter discussed his research project, a joint study conducted with Lisa Lehman, Eric Campbell, Steve Joffe, and Lab Fellow Alison Hwong, centered on the future of disclosure and management of conflict-of-interest dynamics in medicine. Specifically, Professor Carpenter explained the need to study and optimize existing measures of disclosure in consideration of recent state laws that have passed in Vermont, Minnesota, and West Virginia, which require doctors to disclose to the state, or publicly (and increasingly both) the payments that they have received from pharmaceutical companies and medical device companies. Further, as part of the Affordable Health Care Act of 2010, the enactment of the Physician Payments Sunshine Act provision (PPSA) will also demand disclosure of payments and a national disclosure architecture. In light of this, Professor Carpenter described his team's effort to develop a database that will enable patients and consumers to directly access information to aid them in determining if agents are acting in their best interest or not.
Citing Machiavelli's problem of how to get accusations without calumnies, Professor Carpenter explained that since the conflict-of-interest debate is inevitable, a system where people can make claims or inferences about physicians and professionals with room for less falsehoods is greatly needed. In considering the efficacy of disclosure institutions, Professor Carpenter introduced the "affective effect" principle, which states that if a consumer cannot judge the usefulness of a disclosure to his or her utility in 10-15 seconds, then the value of this information to his or her utility is greatly reduced or can become null altogether. One of the main ideas that have come to fruition from this project, Professor Carpenter explained, is the need for a unique researcher or practitioner identifier to be included in the disclosure database. This is largely due to the fact that most of the existing institutions of disclosure typically only cover MDs, leaving out chemists, biochemists, and pharmacologists with PhDs, all of who receive research funds from medical device and pharmaceutical companies. At this point in the seminar, one Lab participant questioned if auditing mechanisms have been developed for the database, reasoning that incentives would be high for underreporting. Alison Hwong, Edmond J. Safra Lab Fellow and project collaborator, answered that there is not yet an auditing mechanism, and conceded that this was problematic.
For the remainder of the presentation, Professor Carpenter discussed initial designs for looking at cross-border variation in New York and Vermont prescribing trends after the Vermont laws were established and then parsing out changes in these prescribing trends. He explained that a problem that they have run into is a limit on prescribing data, specifically; geographic indicators for physicians are not easily available in RX data. He added that this is a long-term problem that the research team is working to resolve. In light of this information, one Lab participant asked Professor Carpenter if private datasets, such as those maintained by large pharmacies like CVS, would have individual level physician identifiers. If so, he commented that these datasets would be very powerful tools for looking at prescribing behavior. Professor Carpenter deferred to another Lab participant who was more knowledgeable about the subject and she claimed that this is certainly possible; however, that the cost might outweigh its value. Finally, another participant of the Lab was eager to know if any of the disclosure information mandated by new state laws has led to malpractice lawsuits. Alison Hwong commented that to her knowledge there have not been any third-party lawsuits filed as a result of conflict-of-interest information disclosure. She reasoned that this is most likely due to the fact that the information does not supply actual empirical evidence, and that causal relationships are difficult to draw from such information.