This comment assesses the evidence from four papers on the development process for four breakthrough drugs or treatments. The comment notes that the papers show all the key breakthroughs in the development process were financed either through the National Institutes of Health or various foundations or charities, not drug companies looking to be compensated through patent monopolies. It argues that this evidence suggests patent monopolies are an unnecessary distortion that results in high drug prices and creates perverse incentives. Expanded public funding could replace patent monopolies, allowing new drugs to be sold at free market prices, ending the problem of high drug prices blocking access.
Hospital-based violence intervention programs (HVIPs) help survivors of violent injury to return to physical, psychological, financial, and social health by addressing a broad range of social determinants of health and reducing the risk of future injury. This work requires connections to a range of providers and services, which often requires the sharing of patient health information. While HIPAA gives providers more discretion to share information to address health-related social needs and care coordination than many providers realize, patients can and should be the ones deciding what health information is shared, with whom it is shared, and under what circumstances. Providers, patients, and programs like HVIPs would benefit from greater clarity on appropriate information-sharing practices. In this article, we consider the real and perceived barriers to information sharing to address social needs and we make recommendations for practice, research, and policy to improve how patient information is shared to address social needs, with relevance for a wide range of interventions.
Words can have inherently multiple meanings; therefore, semantics can directly impact policies and behaviours. In regulatory practice, semantics refers to the specific, actionable meaning behind terminology, and attention to semantics can help ensure that legal frameworks are interpreted accurately. Refining semantics is not a purely theoretical exercise, but a subtle yet powerful element that has the potential to make the regulatory system more efficient in setting priorities and more reliable in meeting societal expectations and needs. When their intended purpose does not align with their true meaning, labels widely used in pharmaceutical regulation such as "breakthrough," "orphans," or "unmet medical need" risks undermining the clarity and precision essential to a sound regulatory framework. In addition, differences in definitions, scopes, eligibility criteria, and incentives can obstruct the efficient convergence of efforts in drug development and access. As pharmaceutical investments and clinical research increasingly transcend borders, the adoption of consistent terminology and regulatory definitions becomes crucial. This alignment can enhance cooperation among investors, governments, regulators, researchers, health care practitioners, and patients.
Despite mounting enthusiasm regarding the introduction of artificial intelligence (AI) software as a medical device (SaMD) to clinical care and, consequently, the development of a new regulatory proposal for the federal oversight of AI/machine learning medical devices, little attention has been paid to the oversight of AI tools used by large insurers. The US Food and Drug Administration (FDA) has advanced an "Action Plan" for clinical AI governance. However, the unregulated application of insurance AI still threatens US healthcare delivery. I use insurance AI tools in the Medicare Advantage (MA) prior authorization pathway as an illustrative case to argue that these technologies require further regulatory attention through interagency collaboration. I identify three agencies at the federal level (i.e., Centers for Medicare & Medicaid Services, Office for Civil Rights, and Office of the National Coordinator for Health Information Technology) which, alongside state-level governance (i.e., through state insurance commissioners), are equipped to expand their regulatory jurisdiction to encompass insurance AI. I advance a framework that harmonizes existing authorities, assigns clear responsibilities, establishes accountability, and creates mechanisms for continuous improvement, respecting agency expertise while ensuring coordination. My aim is to ultimately shift the regulatory paradigm from reactive enforcement to proactive oversight that prevents the deployment of flawed tools in insurance pathways.
This commentary examines the challenges that generative artificial intelligence poses to the authenticity and integrity of scientific data. It proposes data-attestation requirements to strengthen accountability, preserve access to original research records, and help journals and institutions distinguish authentic evidence from synthetic or fabricated data.
