In recent years, real-world data (RWD) and real-world evidence (RWE) have been gaining traction across life sciences research and development (R&D) and healthcare. This traction makes sense and is a good thing for all of us. RWE is where we identify significant breakthroughs in care, backed up by real-world [patient] data (RWD) versus reliance only upon findings from controlled environments such as clinical trials. The increased focus on RWD and RWE signifies a shift in how the life sciences and medical communities use data to make informed decisions to improve patient outcomes and experiences. The challenge lies in unlocking the value of RWD/RWE while preserving data privacy and complying with security regulations.
In this post, we will look at RWD/RWE through the lens of healthcare and life sciences use cases and their value potential. Stay tuned for a follow-up article in which we will discuss how Globant and Duality Technologies team up to disrupt the status quo and support clients with RWE solutions that streamline the use and maximize the value of RWD.
RWD/RWE in R&D – Medical Breakthroughs
Real-world data (RWD) refers to data – routinely collected from various sources outside of traditional clinical trials – that describes patient health status and the delivery of healthcare. Example sources of RWD include electronic health records (EHRs) used and owned by care providers, claims and billing activities, prescription data, data from wearables, and data collected via patient surveys or other patient-generated methods. Unlike data collected during randomized controlled trials (RCT), RWD reflects real-life scenarios, encompassing a broader demographic of patients and environments, making it inherently more diverse and robust than RCT data. Real-world evidence (RWE) refers to the evidence derived from the analysis of RWD. RWE can offer different – and more representative – insights into a product’s (drug or medical device) effectiveness, side effects, usage comparisons, and more.
Example areas in life sciences R&D where RWD/RWE have had a transformative impact include drug discovery, optimization of study design, patient identification and recruitment in clinical trials, and evidence generation for regulatory approval.
“Together, RWE and RWD can provide mechanisms to accelerate the development of therapies that improve the lives of patients with rare diseases.” — Dr. Robert Califf, FDA Commissioner, 2022
RWD/RWE can accelerate the discovery of new drugs or treatments in many ways. Insights into disease prevalence, progression, and the real-world impact of diseases can help identify gaps in care and areas of unmet medical needs, informing strategic decisions about where to focus drug discovery efforts. RWD can guide the development of new therapeutic targets by revealing patterns in disease progression, genetic associations, and treatment responses. RWD can provide valuable insights into the correlation between biomarkers and clinical outcomes in real-world settings, facilitating the discovery and validation of new biomarkers. Additionally, once a drug is on the market, RWD helps identify off-label uses or new indications for existing drugs, allowing companies to explore new therapeutic areas without starting from scratch.
Optimization of Study Design
Data-informed study design can significantly increase the probability of clinical trial success and reduce clinical trials timelines. RWD can assist in estimating the required sample size for a trial more accurately, ensuring that the trial has sufficient power to detect clinically meaningful differences. When traditional randomized control trials (RCT) are not feasible (e.g., due to a small patient population in rare diseases) or not ethical (e.g., in oncology trials), RWD can be used as a comparator group (external control) instead of a separate group of patients who receive a placebo or standard of care treatment. By analyzing RWD, researchers can also determine which patient subgroups are most relevant to a study and ensure that the criteria do not inadvertently exclude significant portions of the target population. This can result in a more representative patient sample, making trial findings more generalizable. Finally, with RWD, it’s possible to identify which outcomes matter most to patients in real-world settings. While a clinical trial might traditionally focus on primary clinical outcomes, RWD can help pinpoint relevant secondary outcomes, such as quality-of-life measures or patient-reported outcomes, which can be pivotal in the drug approval and adoption process.
Patient Identification and Recruitment in Clinical Trials
RWD/RWE have played a significant role in recruiting participants in clinical trials. Electronic health records (EHRs) and health databases allow for more precise identification of potential trial participants. By analyzing this data, researchers can identify populations of patients who fit the exact criteria for a given clinical trial, reducing the time and resources typically expended on recruitment. This can inform decisions about where trials should be located or which clinical sites might have a higher likelihood of finding the required patient population. It’s important to note that the analyses of RWD must be done with utmost care to protect patient privacy and that it typically leads to the identification of clinical sites or healthcare professionals (HCPs) who have qualifying patients under their care. HCP help must still be enlisted to find the individuals who match the eligibility criteria and recruit them for a study.
Evidence Generation for Regulatory Approval
While RCTs are the gold standard for approving new drugs, they often involve narrowly defined patient populations under controlled conditions. RWD, derived from broader populations in routine clinical practice, can provide supplementary evidence on how the drug performs in these diverse, real-world circumstances. Regulatory bodies like the FDA and EMA have recognized the value of RWD in providing a more comprehensive understanding of a drug’s safety and effectiveness profile in real-world settings. This is particularly important in post-marketing surveillance and pharmacovigilance, where RWD can identify rare adverse events or long-term safety issues not evident in initial trials. Furthermore, RWD can demonstrate comparative effectiveness, inform health technology assessments, and satisfy specific post-approval study requirements.
RWD/RWE in Healthcare – Improving Patient Outcomes
The significance of RWD for the life sciences industry bridges traditionally distinct and sometimes siloed phases of the value chain: R&D and healthcare delivery. Patient stratification, detection of long-term outcomes and adverse events, comparative effectiveness of treatments, as well as health economics and outcomes research (HEOR) all stand to impact healthcare delivery and patient outcomes while holding the potential to inform drug discovery and development further, and to help determine the long-term value of treatment.
Patient Stratification and Precision Medicine
RWD helps stratify patients into more specific subgroups based on various factors such as genetic variants, comorbidities, concomitant medications, lifestyle, and disease progression. This stratification enables more personalized treatment approaches, improving the precision of care and potentially leading to better patient outcomes. It also supports the development and use of targeted therapies in precision medicine, where treatments are tailored to individual patients or subgroups of patients with similar characteristics. Of course, data privacy requirements complicate efforts to effectively create such segmentations since common methods of meeting compliance often involve the removal of key data points; we’ll discuss better solutions in the next post.
Long-Term Outcomes and Adverse Events
RWD allows for detecting long-term treatment outcomes and rare or delayed adverse events because the data is collected from a more diverse population over an extended period of time and in real-life (world) conditions. This surveillance helps HCPs make more informed decision-making in their clinical practice and helps ensure patient safety. Insight into long-term outcomes and adverse events can guide future research and development efforts, help sponsors understand patient experience, safeguard public health, maintain regulatory compliance, and substantiate the value of a drug in discussions with healthcare payers and providers. Demonstrating a positive long-term safety and efficacy profile can offer a competitive advantage in a crowded market, especially for chronic diseases requiring long-term treatment.
Comparative effectiveness research (CER) evaluates and compares the benefits and risks of different treatment options in real-world settings. Access to large, diverse real-world datasets from various sources can provide a comprehensive and accurate picture of how treatments perform in real-world settings across diverse patient populations. Insights from CER can guide clinicians, patients, and policy-makers in making evidence-based decisions and helping choose the most appropriate and effective treatments for individual patients.
Health Economics and Outcomes Research (HEOR)
HEOR looks beyond clinical outcomes to consider factors like treatment cost, cost-effectiveness, quality of life, and the societal value of interventions. RWD provides the necessary context for understanding these variables in real-world settings. This information is invaluable for payers, policy-makers, and healthcare providers as they evaluate the trade-offs between cost and outcomes, leading to more sustainable healthcare systems and potentially better patient outcomes.
The shift towards RWD and RWE represents a revolutionary change in the life sciences and healthcare sectors. As highlighted by the various applications in drug discovery, clinical trials, healthcare delivery, and beyond, RWD/RWE offers a comprehensive, real-life snapshot of patient health status and treatment outcomes. Combined with classic RCT, this can inform research and development of new treatments and allow for more personalized, evidence-based clinical decision-making. The value of RWD and RWE extends beyond their scientific merits. They pave the way for the healthcare and life sciences industry to become more adaptive, responsive, and patient-centric. However, despite the overwhelming value to the healthcare ecosystem and patients, RWE/RWD progress is slowed by data privacy, security, and governance concerns. The traditional means to meeting these requirements typically begin with restricting who can participate, then by removing key data points, which reduces overall data quality and limits what can be done with it, e.g., linking disparate datasets for analyses.
Next, we’ll discuss the partnership between Globant and Duality Technologies, a company that offers a data collaboration platform that addresses security, privacy, and governance by design through advanced privacy-enhancing technologies (PETs). The partnership supports our clients in unlocking the value of RWE/RWD in a secure and compliant way, removing the hurdles organizations typically face. We leverage Duality’s privacy-enhancing technologies to develop RWE platforms that enable healthcare data sharing and collaboration across geographies while respecting local data privacy regulations.
Get in touch to start your journey to secure and compliant data sharing and RWD-driven research.