5 Ways RWD Is Transforming Clinical Research

Technological advancements and data analysis have reshaped the entire medicine and healthcare industry. Not only are procedures getting safer to conduct, but we are also becoming better as a society in terms of providing accurate healthcare. One of the primary reasons for this transformation has been real-world evidence solutions.

A tremendous amount of information, medical records, and other related documents get stored in data warehouses. Digital eco-systems have been built to enhance medical healthcare services. Solutions such as these can improve every stage of the journey. The goal is to open new development avenues such as AI-driven solutions and decentralized clinical trial.

Pragmatic and significant simple trials include many of the same characteristics of clinical trial data services, including randomization, and are generally huge databases that allow a larger patient sample to participate. These studies are increasingly utilized to establish efficacy in everyday clinical practice settings and investigate more clinically significant outcomes, resulting in easily transferrable benefits for patients.

Here are a few ways that can help transform clinical research:

1. Trial Design & Single-Arm Trials

The statistical design of a clinical trial can be guided and optimized by real-world data. Many considerations arise such as identifying the target population to choose from, optimizing the use of historical data, etc.

You could reduce the length of a clinical trial by using single-arm trials.

2. Life Science Research

RWE is critical to clinical trial data services' research throughout the product lifecycle. It can assist researchers to discover possible patients and develop correct inclusion criteria for clinical trials, which can help shape pre-trial study design.

By delivering information on a larger cross-section of society, real-world data can aid in overcoming the limitations of clinical trials. This can assist academics and doctors in better understanding their products and how they function.

3. Post-Market Surveillance

RWD can help a pharmaceutical company understand its products with relative ease. It solves purposes such as detecting adverse events or dangers as they occur during actual device or medicine use, evaluating new goods or therapies in comparison to existing choices and the standard of care, and meeting regulatory criteria.

4. AI-Driven Solutions

The synergies between AI and RWD are growing as more life sciences businesses begin to embrace artificial intelligence. It includes, according to Dr. Dai, perfecting patient recruiting tactics to the point that 100 individuals were recruited for specific research in 105 days, with 98 subjects completing their therapy.

AI also assisted in determining the acceptability of inclusion/exclusion criteria, allowing the sponsor to revise its recruiting approach after the trial had begun.

5. Decentralized Trials, Feasibility & Outcomes Reach

RWD can help with feasibility research, digital screening of patients, digital audits of reporting quality (i.e., checking between electronic data collection and source data to indicate quality reporting), remote monitoring, and uploading for decentralized clinical trials. It may be used to estimate site-level enrolment to ensure that milestones get met.

Real World Data can be used to quantify the economic advantages of medicines and provide new paths for continuous monitoring.

Wrapping Up

Preparing a solid analytical strategy at the start of the study is a vital aspect of preparing novel studies to provide real-world evidence solutions that offer appropriate scientific evidence for regulatory decision-making. It is currently a standard component of most conventional pivotal and post-safety trials and should be appropriately expanded to RWD effectiveness studies.

Most traditional RWD data processing and analysis tools can be used to identify and mitigate the impact of some of the limitations inherent in real-world studies, such as potential confounding factors. Real-world data require the development of new procedures or applications to realize their full potential. These examples show the possibilities that hybrid research approaches provide by combining the greatest characteristics of RCTs and studies with RWD while mitigating some of the downsides.