Evidence-Based Medicine and Clinical Trials

Nature Magazine recently published a perspective on the future of evidence-based therapeutics and clinical research. Advancements in data mining, machine learning, and technological wearables initiated evidence-based medical practice transformation. The COVID-19 pandemic exposed clinical trial limitations and improved trial design with a focus on research and the patient. Here is what to know about clinical trials, evidence-based medicine, and how both fields are changing.

Current Research Drawbacks

Advances in clinical research lag in translating into most research domain clinics. Most chronic disease data is in data silos that require collaborative efforts to generate the best evidence quality and increase clinical trial efficiency. Challenges in developing evidence in a cost-effective and timely manner make randomized controlled trials impractical.

The narrow size of the population limits generalization, and many questions go unanswered. It is necessary to clearly define pathological, physiological, and clinical biomarkers and endpoints to accelerate drug development. Staffing limitations are also a challenge.

Clinical Trials of the Future

The COVID-19 pandemic forced clinical trials to be more patient-centered. Deep neural networks, machine learning, and artificial intelligence are tools adapted to advances in precision medicine and immunology for improving drug discovery, interpreting images, streamlining medical record data electronically, and improving trial workflow.

The future includes studies used to enhance the design of clinical trials. Other aspects of future clinical trials are more virtualized, decentralized, and digitalized endpoints for globally harmonized, more realistic, standardized remote monitoring and tracking of patient experiences. Improving the research landscape requires collaborative efforts by contract research organizations, regulatory organizations, pharmaceutical, government and cooperative group sponsors, and academic organizations.

It is believed that site-agnostic matching and navigation services improve patient participation and trial accessibility in clinical trials. Breakthrough destinations, fast-track designations, orphan destinations, and priority reviews reduce the duration of the premarketing process and increase the drug development speed.

Transportation analyses increase the validity of the external trial findings validity. N-of-1 individualized genome trials improve assessments of rare diseases and the trials. AI-based combinations of medical history data and polynomial mechanic information use the ‘digital-twins’ concept of medical analog to enhance trials.

Synthetic and external control arms incorporated in trial comparator arms mirror the comparator arms of contract research organizations. More AI-based methods of pediatric RCTs improve understanding of rare diseases. They are adjuncts, not substitutions, for human intelligence.

Contact Altus Research

Based on research, clinical trials of the future require trial design, conduct, documentation, and assessment transformation to optimize the use of technological advances and AI-based digital data to bridge the gap between the trial and the real world.

The most significant challenges are tapping into the multidimensional potential and generating real-world evidence by retrieval, accumulation, and analysis of large datasets and obtaining advanced scientific techniques. There is a need for increased global funding partnerships.

Online platforms and social media help increase community engagement and awareness. It requires precision, preventative, pragmatic, and personalized medicine, decentralization, digitalization, and breaking silos to address worldwide accessibility, equity, and diversity to improve public health. To learn more about clinical trials, contact Altus Research in Lake Worth, FL today.

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