Sujay Jadhav, CEO of Verana Health highlights how real-time real-world data dashboards are transforming the way life sciences organisations understand patient care.
In 1747, James Lind conducted one of the earliest comparative trials, testing oranges and lemons to treat scurvy in sailors. In 1948, Austin Bradford Hill designed the first modern randomised controlled trial (RCT), testing streptomycin and bedrest compared to bedrest alone for treating pulmonary tuberculosis.
Go deeper with GlobalData
Access deeper industry intelligence
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
Eighty years later, the RCT – more evolved, but with its fundamental characteristics (randomisation, control groups, and blinding) intact – remains the highest level of evidence to establish causal relationships in science.
For evaluating efficacy and safety in pharmaceutical drugs and medical devices, the RCT is considered the gold standard. And yet, it’s not perfect.
In addition to the associated high costs, potential feasibility issues, and ethical restrictions for certain interventions, RCTs often have a problem with generalisability.
Despite efforts to improve patient diversity, gaps still exist between the participant pool for clinical trials and the population at large. Patients outside of trials often have characteristics, experiences, and treatment protocols that differ from the controlled environment of RCTs, meaning the results may not apply to broader, more diverse patient populations.
To evaluate real-world product effectiveness, more information is often needed on how treatments perform within age groups, genders, races and ethnicities, as well as when patients have differences in disease severity and/or co-morbid conditions that require other medications.
Fortunately, life science researchers have access to a growing resource for addressing these shortcomings of the RCT. Real-world data (RWD) is increasingly providing insights and generating evidence that is benefiting all phases of clinical research.
Answers in an avalanche
Modern healthcare generates a barrage of electronic data. Electronic health records (EHRs), medical claims data, wearables and apps, disease and product registries – all these sources of RWD have the potential to shed more light on the health and experience of vast pools of diverse patients in varied, real-life settings.
Increasingly, the advanced power of artificial intelligence (AI) has made it possible to assemble this avalanche of information into usable datasets that researchers can study to complement clinical trials with insight to actual clinical practice.
In this way, RWD holds power to address the knowledge gaps between what is observed of the drug in a controlled environment versus in a broader group of patients and advance our understanding of the long-term effectiveness and safety of treatments.
Properly curated and analysed RWD is showing potential to inform clinical studies, generate real-world evidence (RWE), reduce the cost of research, and speed the delivery of life-saving therapies.
How RWD is applied
RWD can be used to improve clinical trial design and efficiency, facilitate enrollment and enhance diversity. Post-marketing, it supports long-term safety monitoring, and validates efficacy in broader, real-world populations beyond controlled environments.
RWD enables researchers to better understand disease progression, responses to treatments, and other factors in real-life settings. Trial sponsors can use it to identify optimal patient populations, select appropriate study sites, and determine realistic enrollment targets. One benefit of this is reducing costly and time-consuming study amendments.
Another application of RWD to trials is to speed recruitment and gain more inclusive recruitment. By using data from EHRs and registries, researchers can quickly identify eligible patients, reducing the time and cost associated with manual screening.
In research on diseases where a placebo is unethical (e.g., oncology or rare diseases), RWD can be used to create external control arms (ECAs), with historical or external control cohorts to compare against the treatment arm.
Another benefit of the application of RWD to trials is enhanced generalisability. RWD allows expanded trial participation, for more diverse patient populations and ultimately a better understanding of how drugs perform in real-world settings.
In the long-term, RWD supports post-marketing surveillance and follow-up studies, allowing for the detection of rare side effects or long-term benefits that might not be visible in shorter, controlled trials with smaller patient populations.
Quality is key
RWD doesn’t come without its own challenges. The unstructured nature of much RWD (for example, clinician notes in EHRs) requires advanced AI-driven tools to assemble it into a usable format that can be queried or manipulated for study.
Yet this data curation cannot simply be outsourced to technology.
Ensuring the validity of the data requires a deliberate, sophisticated process, often involving a team of clinicians, nurses, clinical informaticians, data scientists, epidemiologists, biostatisticians, and engineers. Working together, they ensure that data is curated and standardised while retaining its original clinical context; data harmonised and models continuously refined to prevent bias and maintain accuracy.
It is the output of this process – the robust, rich, specialty-specific real-world datasets of de-identified medical information – that’s vital to supplement clinical trial data, drive business insights, and inform better research outcomes.
A promising future
Randomised clinical trials continue to be the cornerstone of modern medical research. But RWD and RWE are playing an increasingly valuable role supporting them.
From study design and patient recruitment, to evaluating the value of treatments, to supporting decision-making about product effectiveness and improved post-marketing evaluations, RWD is proving to be a real asset to pharmaceutical development.
Fittingly, regulatory agencies, health authorities, and payers are increasingly recognising the value of RWD and RWE. Following the requirement of the 21st Century Cures Act of 2016 for the US Food and Drug Administration (FDA) to implement a framework for RWEs, subsequent guidelines have fuelled widespread interest and adoption.
Centuries after the early development of the RCT, we are starting to realise the tremendous potential of carefully curated RWD and derived RWE for supporting clinical studies and advancing pharmaceutical research and public health to a new era.
