Brain-enhancing: The future of real-world data, advanced analytics and clinical trials
In today's hyper-competitive global market, life science companies are constantly seeking ways to improve their research efficiency and efficiency. Real-world data (RWD) is increasingly becoming the source and focus of these efforts.
With advanced cloud technology, the vast territory of RWD can now be opened up to collect, store and analyze objects of information. RWD is properly processed and is emitting new light and portrayed for portraits of the patient experience, from the differences in prescription mode and patient response to long-term efficacy and side effects.
The evidence collected from these data can help establish clinical trials and inform ongoing research. However, realizing the benefit of RWD is not simply arranging a series of computing power.
Although the application of artificial intelligence (AI) technology is crucial to curating meaningful information from a large number of different data, it is only part of a carefully planned effort that relies on human intelligence, as well as collaborations from physicians, disease-specific experts, nurses, data scientists and technicians.
Doing it right, these efforts can bring far-reaching benefits and offer a promising future for clinical research and patient care.
A strategic approach
Among the digital records of doctor visits, there is a lot of information about laboratory results and treatment history. When RWDs are linked together, RWD – health information collected outside the scope of traditional clinical trials – can enrich perceptions about how patients experience disease in their daily lives, respond to treatments, and interact with the healthcare system.
Many of this information, such as clinician notes and images in electronic health records (EHRs), are unstructured, meaning that the data are not prepared for analysis in a consistent format.
AI-Techniques, especially machine learning (ML) and natural language processing (NLP), can be game-changing, used to curate large amounts of unstructured data and search for previously hidden relationships and patterns.
However, meaningful insights are based on the effectiveness of potential data. The key to successful AI-driven data planning is the process of using quality data.
This requires a carefully executed approach and is subject to ongoing review and supervision by qualified teams and clinicians. Developing a reliable ML model is crucial through clinician-led AI output, unique training data and validation datasets, and continuous model improvements to prevent bias.
This complex multifaceted effort uses AI technology to support the expertise of human professionals. In this way, advanced analytics has the ability to provide the product of transformative real-world evidence (RWE) (RWE) to drive clinical trial design and execution.
The value of RWD
RWD is crucial to the struggle to reduce research costs and complexity, and is essential to modernizing clinical trials through data-driven decision-making methods.
High-quality, disease-specific, curated datasets from a range of healthcare environments provide patient pools that better reflect the real world. This allows researchers to understand a diverse patient population in a way that eliminates previous knowledge gaps.
Life science companies use RWD and the evidence obtained from it for a variety of purposes, including retrospective and prospective studies, comparative effectiveness studies (CER), health economics and outcome studies (HEOR), and market research and objectives (i.e. commercialization).
Meanwhile, FDA guidance and use case scope increasingly support the growing insights into unstructured RWD in clinical studies.
Improve clinical trials
Traditional clinical trials often rely on relatively simple inclusion/exclusion criteria. RWD implements a more nuanced approach.
RWD can be used to evaluate criteria for trial eligibility, recruit potential study participants, and simplify recruitment. Researchers can identify patients based on changes in the disease, previous treatment failures, comorbidities (there are multiple diseases), and even specific laboratory values and test results.
This accuracy improves efficiency, leads to shorter schedules and improves patient research.
Data-driven trials informed by RWD start with a stronger foundation, possibly avoiding recruitment mismatch, unexpected side effects and expensive delays that plague traditional trials.
Ongoing research and nursing
RWD provides a longitudinal perspective on diseases that have developed over several years or decades. Analyzing how patients cope with long-term patterns of treatment, or how their health needs can be changed over time, can shape better trials with the actual trajectory of chronic diseases.
RWD also sheds light on the gaps in current treatment options. For example, if patients in the real world frequently switch treatments or experience common side effects, it indicates a need for better treatment options. If clinical trials have limited ability to detect rare side effects, large-scale RWD can reveal patterns that may appear slowly or affect only a small percentage of patients. Active monitoring of RWDs can identify potential issues early and modify ongoing trials to investigate safety issues.
For health insurance companies, RWE can provide a way to assess support for patient use and reimbursement.
Overall, AI-driven curation of RWD is enabling possible new insights to have a significant impact on the modernization of clinical trials and patient care.
Sponsors are equipped with RWE with engaging and complementary data to enhance randomized clinical trials, allowing them to accelerate the development of innovative therapies, including the discovery of new instructions for approved therapies.
Photos: metorworks, Getty Images
Sujay Jadhav is CEO of Verana Health, who is helping accelerate company growth and sustainability by advancing clinical trial capabilities, data services, medical association partnerships and data abundance.
Sujay has over 20 years of experience as experienced executives, entrepreneurs and global business leaders along with Verana Health. Most recently, Sujay was the global vice president of Oracle's health science business unit, operating throughout the organization's entire product and engineering team. Prior to Oracle, Sujay was CEO of Gobalto, a cloud-based clinical research platform, who was responsible for Oracle's acquisition of the company. Sujay is also the former executive of Life Sciences Technology Company N Model N Model, who helped oversee its transition to a public company.
Sujay holds an MBA from Harvard University and receives a bachelor's degree in electronic engineering from the University of South Australia.
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