In today’s ultra-competitive global marketplace, life sciences companies are continually seeking ways to improve their research effectiveness and efficiency. Increasingly, real-world data (RWD) is becoming a source and focus of these efforts.
With advanced cloud technologies enabling the collection, storage, and analysis of petabytes of information, the vast realm of RWD is now open for mining. Properly handled, RWD is shedding new light and painting a much fuller portrait of the patient experience, from the nuances of how treatments are prescribed and patients respond, to long-term efficacy and side effects.
Evidence gathered from this data can aid in the set up of clinical trials as well as inform ongoing research. Realizing the benefits of RWD, however, isn’t so simple as lining up an array of computing power.
While the application of artificial intelligence (AI) technology is critical to curating meaningful information from vast amounts of disparate data, it’s just part of a carefully orchestrated effort dependent on human intelligence, and the collaboration of physicians, disease-specific specialists, nurses, data scientists, and technologists.
Done right, these efforts can lead to profound benefits, and offer a promising future for clinical research and patient care.
A strategic approach
Within the digital records of doctor visits, lab results, and treatment histories lies a wealth of information. When linked together, RWD — health information gathered outside the confines of a traditional clinical trial — can provide a rich view of how patients experience diseases, respond to treatments and interact with the health care system in everyday life.
Much of this information, for example the clinician notes and imagery in electronic health records (EHRs), is unstructured, meaning the data isn’t in a consistent format that lends to ready analysis.
AI-techniques, particularly Machine Learning (ML) and Natural Language Processing (NLP), can be game-changing for curating vast troves of unstructured data and searching for previously hidden relationships and patterns.
But deriving meaningful insights is predicated on the validity of underlying data. Key to successful AI-driven data curation is using a process that ensures quality data.
This requires a thoughtfully executed approach with ongoing review and oversight by qualified teams and clinicians. It’s essential to develop robust ML models, with clinician-led validation of AI outputs, distinct training data and validation datasets, and continuous model refinement to prevent bias.
This sort of sophisticated, multi-faceted effort employs AI technology to support the studied expertise of human professionals. In this fashion, advanced analytics has the capacity to deliver transformative real-world evidence (RWE) — a product of analyzed RWD — to advance clinical trial design and execution.
The value of RWD
RWD has become essential in the fight to reduce costs and complexities of studies, and is pivotal to modernizing clinical trials with a data-driven approach to decision making.
High-quality, disease-specific, curated datasets sourced from a range of health care settings provides a patient pool that better reflects the real world. This enables researchers to understand diverse patient populations in a way that removes previous knowledge gaps.
Life sciences companies use RWD and the evidence derived from it for a wide variety of purposes including retrospective and prospective studies, comparative effectiveness research (CER), health economics and outcomes research (HEOR), and market research and targeting (i.e., commercialization).
Meanwhile, the increasing adoption of insights from unstructured RWD in clinical research is supported by FDA guidance and a growing range of use cases.
Improving clinical trials
Traditional clinical trials often rely on relatively simple inclusion/exclusion criteria. RWD enables a much more nuanced approach.
RWD can be used to evaluate trial-eligibility criteria, recruit potential research participants, and streamline recruitment. Researchers can pinpoint patients based on disease variations, previous treatment failures, comorbid conditions (the presence of multiple illnesses), or even specific lab values and test results.
Such precision increases efficiency, leads to shorter timelines and improves patient access to research.
Data-driven trials informed by RWD start with a stronger foundation, potentially avoiding mismatched enrollment, unexpected side effects and costly delays that plague traditional trials.
Ongoing research and care
RWD provides a longitudinal perspective on diseases that evolve over years or decades. Analyzing long-term patterns in how patients respond to treatments or how their health needs change over time can shape trials that better align with the actual trajectory of chronic illnesses.
RWD also illuminates gaps in current treatment options. For instance, if real-world patients switch therapies frequently or experience common side effects, it suggests that better treatment options are needed. Where clinical trials have limited ability to detect rare side effects, large-scale RWD can reveal patterns that might emerge slowly or only affect a small percentage of patients. Proactively monitoring RWD allows for identifying potential issues early and modifying ongoing trials to investigate safety concerns.
For health insurers, RWE can supply a means of assessing support of patient use and reimbursement charges.
Across the board, AI-driven curation of RWD is making possible new insights that are having a significant impact on the modernization of clinical trials and patient care.
Armed with RWE, sponsors have compelling and complementary data to augment randomized clinical trials, enabling them to accelerate the development of innovative treatment approaches, including discovering new indications for approved therapies.
Photo: metamorworks, Getty Images
Sujay Jadhav is the Chief Executive Officer at Verana Health where he is helping to accelerate the company’s growth and sustainability by advancing clinical trial capabilities, data-as-a-service offerings, medical society partnerships, and data enrichment.
Sujay joins Verana Health with more than 20 years of experience as a seasoned executive, entrepreneur, and global business leader. Most recently, Sujay was the Global Vice President, Health Sciences Business Unit at Oracle, where he ran the organization’s entire product and engineering teams. Before Oracle, Sujay was the CEO of cloud-based clinical research platform goBalto, where he oversaw the acquisition of the company by Oracle. Sujay is also a former executive for the life sciences technology company Model N, where he helped to oversee its transition to a public company.
Sujay holds an MBA from Harvard University and a bachelor’s degree in electronic engineering from the University of South Australia.
This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.
