Real-world evidence helps accelerate research: Wolters Kluwer Health

By Jenni Spinner contact

- Last updated on GMT

(peshkov/iStock via Getty Images Plus)
(peshkov/iStock via Getty Images Plus)

Related tags: RWD, Real world data, RWE, Real world evidence, Data management, Drug development

Experts from the health information specialist explain how the use of RWD and RWE has helped speed up and improve the development of therapies and vaccines.

For many years, peer review has been considered the ‘gold standard’ in research. However, according to two leaders from Wolters Kluwer Health, increasing acceptance of real-world data (RWD) and real-world evidence (RWE) has helped the drug development community put treatments, vaccines, and other solutions on the fast track:

  • Karen Kobelski, vice president and general manager of clinical surveillance compliance and data solutions for Wolters Kluwer Health
  • Vikram Savkar, senior vice president and general manager of the medicine segment of health learning, research, and practice for Wolters Kluwer Health]

OSP: Could you please explain RWE, its use in drug development, and how its use/understanding has evolved in recent years?

KK: Historically, drug development has relied on carefully executed, well-controlled clinical trials to validate the efficacy of medications. However, these trials are time-consuming, and sometimes cause delays in getting new drugs to market.

Today, real-world evidence (RWE) is being used to augment clinical trials with observational data collected through routine clinical practice, hybrid trials, pragmatic trials, and late-phase trials to accelerate clinical validation and get new drugs to patients more quickly. RWE can be derived from a myriad of sources including electronic health records (EHRs), product and disease registries, wearable devices, genomic data sets, medical claims registries, and social determinants of health (SDOH). As new technologies have emerged for patient data collection, so has the utilization of RWE in drug development.

Additionally, RWE allows researchers to expand the patient population assessed beyond the formal clinical trial to ensure they are not overlooking particular demographics or geographies in assessing the efficacy of a medication. It can also provide a better view of the impact of treatment over time. RWE can complement data from randomized, controlled clinical trials and help improve understanding of treatment effects, disease burden, patient safety, and off-label drug usage within a broader patient population.

OSP: Please share a bit about how combining RWE with more traditional research methods helped get COVID-19 vaccines and treatments to patients more rapidly than would otherwise be possible.

OSP_WoltersRWE_KK
Karen Kobelski, Wolters Kluwer Health

KK: Researchers have cited 17 years as the amount of time it can take for new medical discoveries to get through the peer review process to the patient. At the height of COVID-19, we simply didn’t have 17 years to wait for life-saving treatments and vaccines.

The rapid rollout of vaccines and other countermeasures for COVID-19, which saved tens of millions of lives, would not have been possible without the support of RWE, which enabled researchers to quickly leverage available patient data from sources outside of formal studies. RWE has also provided valuable insights on vaccine efficacy as new variants emerge.

This experience has demonstrated the time is now for researchers and other industry stakeholders to acknowledge the power of using different data sources in a complementary manner to tackle some of health care’s most difficult problems.

OSP: Please tell us what lessons the community learned along the path to COVID-19 solutions, and how you think these lessons may be applied.

VS: The COVID-19 pandemic was a trial by fire that demonstrated the necessity to glean valuable insights from available patient data quickly to save lives. Consequently, we have to find the right ways to leverage nontraditional data sources in future efforts to drive life-changing and potentially life-saving clinical advancements for patients.

Although peer review has long (and rightly) been considered the gold standard for evaluating research, using RWE in combination with peer-reviewed research is likely the most efficient and comprehensive way to accelerate new treatments moving forward. Both sources of data have their own advantages and disadvantages, but in the right balance can complement each other extremely well.

OSP: You mention that peer review is the gold standard—why has this been the case, and do you think that philosophy should continue?

VS: Peer review is considered the gold standard because it involves expert clinicians using the full breadth of their knowledge in evaluating formal research studies to determine their validity, reproducibility, risk factors, and so on. Technology is powerful, but nothing is so powerful as an expert human mind, and peer review takes advantage of that and helps keep patients safe as a result.

The drawback of peer-reviewed research is that achieving that level of scientific rigor takes significantly more time and, as we have seen during the pandemic, there is sometimes a need to factor in speed when patient lives are at stake. And however powerful the human mind is, it does have limitations, for instance in processing massive volumes of patient data. This is where AI-based technologies pointed at RWE data can yield insights that peer-reviewed clinical studies sometimes can’t.

Ultimately, peer review will remain a cornerstone of the scientific process, but RWE can and, many argue, should complement it to accelerate clinical discovery.

OSP: How can industry professionals elevate RWE and RWD, and increase trust in such findings?

KK: It’s important to make clear that there are high standards for RWD sources before they can be used as evidence to drive conclusions. Specifically, the data must be high quality, meaning it’s complete, transparent, nonbiased, and able to be codified to industry standards for broad use.

One option to instill greater trust in RWE’s utility could be the establishment of something similar to the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) system used to assess the quality of new medical research. A system like this would support more focused research and provide clear guidelines for how conclusions can be drawn from RWD.

OSP: What technologies do you see as being most useful going forward?

KK: There are a couple of technologies that are essential to RWE. The first is Clinical Natural Language Processing (CNLP), which allows researchers to review medical charts at scale. Approximately 80% of the information found in medical charts (a common RWD source) is unstructured and contained in written notes. CNLP can find the most relevant clinical indicators in those notes and map them to diagnoses (ICD-10), procedures (CPT), and SNOMED terms so that this information can be used in the analysis of RWD. Without this technology, it would be too time-consuming and expensive to manually review charts and extract this valuable information.

A second important technology is a clinical terminology management platform. To analyze all the disparate data that is coming from multiple sources like EHRs or insurance claims, that data first has to be mapped to standards. For example, we’ve seen an A1C test represented at least 100 different ways, which can easily hamper analysis.

A clinical terminology management platform provides data engineers with the tools they need to map local terms to standards and maintain those maps, and keep standard code sets updated as new diagnoses, medications, procedures, and labs are introduced, and build clinical code groups that map the medications, procedures, and tests associated with a clinical diagnosis of a disease such as diabetes. A platform like this also includes tools to look up codes to expedite the process of preparing data for analysis.

OSP: What other (related or otherwise) trends do you see as having the potential to advance and accelerate drug development?

KK: Artificial intelligence is a technology that can help build predictive models based on the analysis of large clinical data sets which can be used to proactively identify patients who are a good fit for clinical trials. Specifically, researchers can use machine learning to sift through large historical clinical data sets to build clinical models of different patient profiles, such as COVID-19 patients under 40 who developed long COVID. That model can then be integrated into EHRs and used to identify patients who recently tested positive for COVID and who can be enrolled in a trial of a medication designed to prevent that outcome.

OSP_WoltersRWE_VS
Vikram Savkar, Wolters Kluwer Health

VS: We saw a rapid expansion of interest in preprints at the height of the COVID-19 pandemic and just recently, the CDC announced an increased focus on preprint research as part of its plan for restructuring. Preprints are a completed draft of a research paper that is made publicly available before it has undergone a formal peer review process. Through preprints, which are housed on preprint servers, authors can immediately disseminate their research in an open-access setting. This can increase the accessibility of their findings and like RWE, accelerate translation of research to practice. The growing acceptance of preprints is leading to an evolution in open medicine and encouraging more researchers to work collaboratively.

OSP. Do you have anything to add?

VS: The use of RWE in accelerating clinical innovation and drug development is promising, yet similar efforts to leverage large volumes of data in the healthcare space have sometimes not yielded a clear return. One key to success this time will be ensuring that clinical need drives use of the data, rather than the data driving a search for clinical application. When innovation efforts that combine large-scale data volumes with AI are shaped by specific intended outcomes, the problem becomes narrow and therefore more solvable. Close alignment between the technology teams that are dedicated to using RWE and healthcare leaders operating at the clinical edge will be critical.

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