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Designing and Delivering a DTx Clinical Research Program: No Need to Re-invent the Wheel
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Colin A. Espie, Alasdair L. Henry
Clinical research studies aimed at evaluating a medical, surgical, or behavioral intervention are the primary means by which a new treatment, like a new drug, medical device, CBT program, or SaMD, may be regarded as safe and effective. There are different types of clinical research studies. These can be differentiated by the study design and the associated level of evidence that a particular study design offers about the intervention. There are also different phases or stages in a clinical research program. This is sometimes known as the clinical trials pipeline, which progressively evaluates the intervention's safety, efficacy, and clinical effectiveness when offered at scale and in the real world.
Breaking out of the Silos in the Heartland
Published in Thomas S. Inui, Richard M. Frankel, 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.
Health Professionals and Modern Human Research Ethics
Published in Howard Winet, 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.
Health concerns and attitudes towards research participation in a community of rural Black Americans
Published in Clinical Gerontologist, 2023
Priscilla A. Amofa, Andrea M. Kurasz, Glenn E. Smith, Shellie-Anne Levy
Why might you or others not participate in clinical research? Although participants explained that conducting research and participating in research are vital, they also identified several reasons why they might not participate. Among those were as follows: potential side effects in drug trials, concerns about being experimented on (“not wanting to be a lab rat”), shame and stigma surrounding others knowing you have a particular disease (like dementia), lack of clear communication about research opportunities and its benefit to the individual or the community, lack of continued communication about the status of the research from the research team, fear of unknown outcomes from participation, health conditions that might exclude them from the study, accepting the inevitable, getting a placebo as opposed to the active drug, access to transportation or increased home responsibilities, and lack of appreciation for participating. While all of these were mentioned, three themes were consistently mentioned in all focus groups:
Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal processing
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Sergey Filist, Riad Taha Al-kasasbeh, Olga Shatalova, Altyn Aikeyeva, Nikolay Korenevskiy, Ashraf Shaqadan, Andrey Trifonov, Maksim Ilyash
There have been several researches (Chang et al., 2004) on the relationship between music and human physiological or psychological responses. However, there are cardiovascular index factors that have not been explored quantitatively due to the qualitative nature of acoustic stimuli. This study proposes and demonstrates an experimental design for the quantification of cardiovascular responses to music stimuli in humans. The system comprises two components: a unit for generating and monitoring quantitative acoustic stimuli and a portable autonomic nervous system (ANS) analysis unit for quantitative recording and analysis of the cardiovascular responses. The experimental results indicate that the proposed system can exactly achieve the goal of full control and measurement for the music stimuli, and also effectively support many quantitative indices of cardiovascular response in humans. In addition, the analysis results are discussed and predicted in future clinical research.
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.