In the ultracompetitive drug development industry, life science companies continually seek ways to improve the brutal path of bringing a new drug to market.
With the average cost of bringing a new drug to market tallied in billions, and the percentage of experimental drugs successfully gaining regulatory approval hovering in single digits, it’s no wonder why there is a relentless search for an edge.
Companies desperately need better tools and methods to reduce the innovation gap in drug development. One area showing increasing promise to curtail development timelines and increase the odds of bringing new treatments to market involves digital data mining.
The advent of artificial intelligence (AI), advanced machine learning (ML) and natural language processing (NLP), supported by the dramatic expansion of computing power and cloud storage capabilities, gives life sciences organizations the ability to extract meaningful information from massive amounts of real-world healthcare data.
This new frontier makes the vast realm of real-life patient experiences available to examination, in ways that haven’t previously been feasible. As a result, life science companies now have the ability to approach clinical trials with the benefit of data-driven planning and decision making.
The Value of RWD
Real-World Data (RWD) refers to data collected outside of controlled experimental settings (e.g., clinical trials)—data derived from actual patient experiences, everyday medical practice, and real-life situations. RWD encompasses an array of information from disparate sources, including medical claims, electronic health records (EHRs), imagery, and wearable devices.
Real-life health information captured outside the confines of traditional clinical trials offers a raw, unfiltered view of how patients experience diseases and respond to treatments.
Historically, however, it’s been impractical to glean insights from RWD because much of it is unstructured—the data lacks the consistent formatting or structure to be easily queryable. For example, clinician notes in EHRs contain invaluable details of patient care over time, yet the variance in terminology and how notes are recorded has made analysis infeasible.
AI-powered techniques change that equation. Now, with the help of sophisticated ML and NLP tools models, skilled teams of multidisciplinary medical professionals and data scientists can manipulate vast troves of unstructured data into resources that can be analyzed.
From Many, One
RWD provides a longitudinal perspective on diseases that evolve over years or decades. From the jumbled array of countless individual experiences, researchers can glean insight: From many patient journeys, one step forward for research.
While not a panacea, RWD acts as a catalyst, providing insights needed to empower business intelligence, process optimization and efficient resource allocation. Only by removing the blinders, inherently created by the environment of disparate EHR systems and data silos for capturing and storing patient information, can we see the possibilities.
Analyzing long-term patterns in how patients respond to treatments or how their health needs change over time can shape clinical trials that better align with the actual trajectory of chronic illnesses.
Data Driven Trials
RWD illuminates gaps in current treatment options. If patients switch therapies frequently or experience common side effects, it suggests that better treatment options are needed. 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.
This paradigm shift serves to improve collaboration among stakeholders and is pivotal to modernizing clinical trials and enabling a data-driven approach to decision-making.
Many clinical trials struggle to enroll patients, hindering meaningful results and delaying the delivery of therapies to patients. RWD can be used to evaluate trial-eligibility criteria, recruit potential participants, reduce dropout rates, and streamline recruitment. It increases efficiency leading 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.
Quality Data Matters
Key to the success of applying RWD to clinical research is access to rich, high-quality, curated datasets. Afterall, the outcome of any analysis is dependent on the validity of the underlying data.
For researchers, it’s important to have disease-indication specific datasets for given therapeutic areas. Targeted information—sourced from a variety of healthcare settings and data sources, and de-identified to protect privacy—enables researchers to gain an understanding of the nuances of real-life disease progression, the treatment of diverse patient populations, and results over extended periods.
Analyzing this de-identified patient pool can create real-world evidence (RWE) regarding the usage and potential benefits or risks of a medical therapy, and offer insights about efficacy, safety, and cost effectiveness different from those obtained during a randomized clinical trial (RCT).
The Power of Insight
RWD analysis removes knowledge gaps and underscores the collective power of individual experiences.
For clinical research, it can lead to a patient pool that better reflects the real world with inclusivity and precision that fuels the development of tailored treatments, ultimately leading to better health outcomes for all.
Aimed with RWE, sponsors have compelling and complementary data to augment RCTs, enabling them to accelerate the development of innovative treatment approaches, including discovering new indications for approved therapies.
In the quest to improve the efficiency of clinical research and drug discovery, the smart application of RWD is fast becoming a critical resource.
