The Research‐Treatment Distinction: A Problematic Approach for Determining Which Activities Should Have Ethical Oversight
Abstract
The rise of quality improvement research and comparative effectiveness research in health care settings constitutes progress toward the goal of what the Institute of Medicine has called a “learning healthcare system,” in which we are “drawing research closer to clinical practice by building knowledge development and application into each stage of the healthcare delivery process.” As clinical research and clinical practice move closer to a deliberately integrated system, the distinction between the two is increasingly blurred, although the sharp distinction in U.S. regulations and research ethics literature remains in place. In the 1970s and for two decades thereafter, this distinction was helpful: for some forms of research, it sheds light on which activities require ethical oversight. Research that is closely integrated with health care—notably, health delivery research—was then uncommon, however. That is no longer the case, and regulations and research ethics need to change to accommodate the new landscape . In this paper, we argue that conceptual, moral, and empirical problems surround the received view that we can and should draw sharp distinctions between clinical research and clinical practice. We start with the history of the research‐practice distinction in the reports of a U.S. national commission and in U.S. federal regulations, and then offer a critical assessment of five characterizations of research that have been used in policy documents and the scholarly litera…
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Realizing generative AI's potential in healthcare requires translational approaches rooted in implementation science.
“Realizing generative AI’s vast potential in healthcare requires translational approaches rooted in implementation science.”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [17]
Technological progress alone will not revolutionize healthcare overnight.
“Such approaches recognize technological progress alone will not revolutionize healthcare overnight [16, 17].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [17]
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- Authors
- Kass, N. E., Faden, R. R., Goodman, S. N., Pronovost, P., Tunis, S., & Beauchamp, T. L.
- Venue
- Hastings Center Report
- Publisher
- Wiley
- Published
- 2013-01
- Volume
- 43
- Issue
- s1
- Pages
- S4-S15
- ISSN
- 0093-0334 (ISSN-L)
- 1552-146X
- OpenAlex
- W1976450230
- Cited by
- 235
- References
- 66
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 10 · 2023: 3 · 2022: 13 · 2021: 14 · 2020: 18 · 2019: 21 · 2018: 26 · 2017: 14 · 2016: 27 · 2015: 32 · 2014: 28 · 2013: 17
- FWCI
- 35.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Hastings Center Report (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health-National Center for Research Resources (RC1RR028876)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Health Sciences › Medicine › Physiology › Biomedical Ethics and Regulation
- Keywords
- research ethics, ethical oversight, quality improvement research, comparative effectiveness research, learning healthcare systems, patient protection, research regulation, integration of research and clinical practice
- MeSH terms
- Clinical Trials as Topic, Concept Formation, Patient Care, Humans, Human Experimentation, United States, Risk Assessment, Moral Obligations, Ethics, Research, Research Subjects
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