Health Professionals and Modern Human Research Ethics
Howard Winet in Ethics for Bioengineering Scientists, 2021
Clinical research is any formal investigation that seeks data from human subjects. The investigation may be scientific (Can angiogenesis during wound healing take place in the absence of vascular endothelial growth factor?), engineering (What artificial joint materials show the least wear in vivo?), medical (How much do effective insulin doses vary in elderly diabetes II patients?), sociological (Do the health habits of subjects in a longitudinal study like the Framingham Heart Study improve as compared with the population at large?) or psychological (How can medical personnel determine the emotional stability of a patient considering euthanasia alternatives?). A bioengineering scientist would tend to be involved in the first three. However, all would have to be approved by the Institutional Review Board (IRB). In our context, clinical research is all the testing on humans associated with medical product development. This testing is for determination of the safety and efficacy of the product.
Selection of Endpoints
Susan Halabi, Stefan Michiels in Textbook of Clinical Trials in Oncology, 2019
The overall objective of the conduct of a research study is to make inferences about hypothesized relations within a population. Clinical research involves the evaluation of medications, devices, diagnostic products, and treatment regimens intended for human use [1]. Research studies are conducted in human subjects to answer questions pertaining to interventions, including new treatments such as novel drug combinations, immuno-therapies, or molecular targeted therapies; disease prevention such as smoking cessation studies or evaluation of cancer screening programs; symptom relief and pain management; palliative treatments to improve quality of life; or standard of care treatments that warrant further study or comparisons [1]. The goals of a study may include the evaluation of proposed medical treatments for safety and efficacy, assessment of the relative benefits of competing therapies, or determination of the optimal treatment schedule or drug combinations, in specific patients.
Breaking out of the Silos in the Heartland
Thomas S. Inui, Richard M. Frankel in Enhancing the Professional Culture of Academic Health Science Centers, 2022
The Biomedical Informatics Program continues the development of information systems to manage samples, phenotypic electronic medical records, and research data, and to identify possible research subject participants. The Community Health Engagement Program and the Bioethics and Subject Advocacy Program (BSAP) are leading the development of policies and strategies to inform the public of opportunities to participate in and contribute to clinical research studies and to assure that clinical research is being conducted at the highest levels of ethical standards. The Regulatory Knowledge and Support program (RKS) is providing leadership in the development of policies and procedures for operation. The Translational Technologies and Resources program has coordinated the policies of a large number of technology cores across the three universities.
A systematic perspective on the applications of big data analytics in healthcare management
Published in International Journal of Healthcare Management, 2019
Sachin S. Kamble, Angappa Gunasekaran, Milind Goswami, Jaswant Manda
‘Clinical research is a branch of healthcare science that determines the safety and effectiveness (efficacy) of medications, devices, diagnostic products and treatment regimens intended for human use’ [65]. The main utilities of clinical research is for disease prevention, treatment, diagnosis and disease cure. The importance of big data for analyzing the vast clinical databases for carrying clinical research on different diseases like sleep disorders [66], Parkinson’s disease [67] and personalized healthcare [68,69] holds a lot of promise for the future. However, specific challenges for the genomic researchers and clinicians exists regarding getting consent, collecting results and the incidental findings [70]. There are modern sensor technologies available that capture diverse data and presents precise results for improved clinical decision making [68].
Innovative Practice, Clinical Research, and the Ethical Advancement of Medicine
Published in The American Journal of Bioethics, 2019
Jake Earl
The worry that requiring clinicians to gather and share information would transform innovative practice into clinical research is exaggerated. The proposal is to require that clinicians engage in learning activities, clinical interventions or manipulations of health information aimed at gathering information to help improve clinical practice (Faden et al. 2013, S19). Clinical research is one kind of learning activity that might warrant a distinctive kind of oversight, but other kinds of learning are a necessary and commonplace strategy for improving the quality of medical care. Learning activities such as gathering and sharing anonymized clinical outcomes from innovative practice will often not impose additional risks on patients or burden their autonomy, so prospective IRB review and research-specific consent are neither necessary nor appropriate. There are certainly questions about the ethical limits of learning activities (e.g., should patients be required to provide information as a condition of receiving innovative interventions?), but policies requiring learning in innovative practice do not intrinsically risk transforming it into clinical research and should be included as part of a permissive oversight approach.2
A Systemic Approach to the Oversight of Machine Learning Clinical Translation
Published in The American Journal of Bioethics, 2022
Effy Vayena, Alessandro Blasimme
Computer scientists are most concerned with demonstrating the superiority of ML-driven systems at performing classificatory or predictive tasks vis-à-vis human practitioners. While this form of testing offers a good indication of an algorithm’s accuracy, little can be inferred about the clinical efficacy of a system or its effectiveness in real world conditions. Clinical trials with meaningful clinical endpoints offer the best available evidence to justify the use of ML systems in medical practice (Topol 2020). Many forms of clinical research exist, each suited to answer different clinical questions. The design of clinical research for medical ML will need to take into account the tasks ML systems are intended to perform and be proportionate to the level of risk that such tasks entail for the patient. Robust evidentiary standards and validation practices are essential to the safe and ethically sound clinical translation of medical ML. Regulatory agencies have a key role.
Related Knowledge Centers
- Clinical Research Associate
- Clinical Trial
- Toxicity
- Efficacy
- Medication
- Medical Device
- Test Article
- Investigational New Drug
- Consent
- Preclinical Development