Pharmaceutical approval today suffers from a serious ethical flaw: newly FDA-approved drugs are de facto “tested” on an unknowing general public in the months and years immediately following drug approval, without either the informed consent of the consuming public or an understanding by the public of the risks that remain. This post-approval human “testing” occurs due to the inherent inability of even the largest clinical trials to detect rare adverse events, as became famously evident following the high profile withdrawal of Vioxx® (rofecoxib) almost a decade ago. Drawing inspiration from successful user-driven rating systems such as those of eBay and Amazon, this Article explores the feasibility of using a consumer-friendly online crowdsourcing platform to allow patients to evaluate the drugs they consume. Using crowdsourcing as a supplement to existing clinical trial practice holds the potential to generate large amounts of data quickly by harnessing the self-interested desire of patients to better understand the risks and benefits of the drugs they are consuming, thereby helping to remove dangerous or useless drugs from the market more quickly. In addition, public participation in drug evaluation will itself serve as an unmistakable signal to patients that drug information remains incomplete despite FDA-approval, helping to address the deficit of informed consent that is at the root of the ethical problem just mentioned. Finally, crowdsourcing has the potential to dramatically reduce the costs expended for post-approval market surveillance, which currently takes place via cumbersome programs such as Sentinel, MedWatch, and REMS.
Volume 98 - No. 3
- Note: Stranger than Science Fiction: The Rise of A.I. Interrogation in the Dawn of Autonomous Robots and the Need for an Additional Protocol to the U.N. Convention Against Torture
- SIRI-OUSLY 2.0: What Artificial Intelligence Reveals About the First Amendment
- The Consequences of Disparate Policing: Evaluating Stop and Frisk as a Modality of Urban Policing
- Regulating Cumulative Risk
- Toward a Critical Race Theory of Evidence
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