Trading-Off Reproductive Technology and Adoption: Does Subsidizing in Vitro Fertilization Decrease Adoption Rates and Should It Matter?

For those facing infertility, using assisted reproductive technology to have genetically related children is a very expensive proposition. In particular, to produce a live birth through in vitro fertilization (IVF) will cost an individual (on average) between $66,667 and$114,286 in the U.S. If forced to pay these prices out of pocket, many would be unable to afford this technology. Given this reality, a number of states have attempted to improve access to reproductive technology through state-level insurance mandates that cover IVF. Several scholars, however, have worried that increasing access in this way will cause a diminution in adoptions and have argued against enactment of state mandates for that reason. In this paper, which was selected for presentation at the 2010 Stanford-Yale Junior Faculty Forum, we push against that conclusion on two fronts. First, we interrogate the normative premises of the argument and expose its contestable implicit assumptions about how the state should balance the interests of existing children waiting for adoption and those seeking access to reproductive technology in order to have genetically related children. Second, we investigate the unexamined empirical question behind the conclusion: does state subsidization of reproductive technologies through insurance mandates actually reduce adoption; that is, is there a trade-off between helping individuals conceive and helping children waiting to be adopted? We call the claim that there is such an effect the “substitution theory.” Using the differential timing of introduction of state-level insurance mandates relating to IVF in some states and differences in the forms these mandates take, we employ several different econometric techniques (differences-in-differences, ordinary least squares, two-stage least squares) to examine the effect of these mandates on IVF utilization and adoption. Contrary to the assumption of the substitution theory, we find no strong evidence that state support of IVF through these mandates crowds out either domestic or international adoption. Appendix A re-analyses our results using the insurance mandate categorization of other studies in the literature

Nudging the FDA

[Excerpt] The FDA’s regulation of drugs is frequently the subject of policy debate, with arguments falling into two camps. On the one hand, a libertarian view of patients and the health care system holds high the value of consumer choice. Patients should get all the information and the drugs they want; the FDA should do what it can to enforce some basic standards but should otherwise get out of the way. On the other hand, a paternalist view values the FDA’s role as an expert agency standing between patients and a set of potentially dangerous drugs and potentially unscrupulous or at least insufficiently careful drug companies. We lay out here some of the ways the FDA regulates drugs, including some normally left out of the debate, and suggest a middle ground between libertarian and paternalistic approaches focused on correcting information asymmetry and aligning incentives.

What (if anything) is Wrong With Human Enhancement? What (if anything) is Right with It?

The video of the presentation of this paper is also available

The Science, Fiction, and Science Fiction of Unsex Mothering

Response to Darren Rosenblum’s UnsexMothering: Toward a Culture of New Parentin

The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition