How Real World Data and Real World Evidence Can Improve Health Outcomes

Key Takeaways
  • Randomized clinical trials are the traditional method for observing patients as well as the safety and efficacy of interventions – but there are other tools at our disposal for making studies more efficient and effective.

  • There is a growing interest around real world data and real world evidence given their ability to address a broader population with greater insights, more quickly.

  • These tools can ultimately lead to better health outcomes and deliver impact at scale.

Achieving quality health outcomes is the fundamental purpose and priority of patient care. As the healthcare industry becomes increasingly focused on these outcomes, stakeholders are looking for more ways to bring added value to all parties within the system – patients, payers, providers, and suppliers.[1] Bringing new therapies, which offer the promise of better health, to market, requires clinical trials, which often require the comparison of patient groups – those on the therapy and those on placebo – to determine whether the therapy is effective. Encompassing both clinical trials and clinical care, there is a growing spotlight on real-world evidence (RWE) to provide rich data that informs better decision making, greater market access, and more effective long-term monitoring, which can ultimately make more quality health outcomes possible, more quickly.[2]

Looking Beyond Clinical Trials

Historically, randomized control trials (RCTs) have been used to identify cause-effect relations between intervention and outcomes. In these trials, patients are randomly assigned to one of two groups; the experimental group receives the intervention being tested, and the comparison (or control) group receives an alternative or no treatment. Researchers then observe both groups to assess the effectiveness of the intervention.[3]

While RCTs have long been the hallmark for testing new drugs, devices, or other interventions, they have limitations:

  • RCTs can be very expensive to run. Drug development trials that support FDA approvals have a median cost of $19 million. This number can vary greatly depending on the size of the trial, how long it lasts, and whether the trial is focused on a drug’s ability to prevent a clinically meaningful outcome, such as a heart attack, versus a surrogate outcome like cholesterol levels.[4]
  • RCTs can take many years to complete. Generally, the length of the trial increases with each phase of research, with phase 3 trials, which lead to FDA approvals, often taking several years. This helps ensure that by the time drugs reach the public, they have been thoroughly evaluated for safety and efficacy. Oftentimes, trials leading to FDA approval are not long enough to assess the long-term effect(s) of an intervention, and “long-term follow up studies” must be performed for years after approval of an intervention.
  • The patient experience and results from a clinical trial are somewhat “unnatural”. Findings that stem from RCTs may not be valid beyond the study population. If a trial focused on a high-risk population, for example, in order to maximize the possibility of detecting an effect, the intervention may not be practical or relevant for a broader population – especially if public health decisions must be made quickly.[5]

What is real world data (RWD) and how does it relate to health outcomes?

When it comes to clinical research, data is king, but controlled conditions don’t always represent “real world” settings. It’s important to ensure that the data source is relevant in order to answer questions that may not be evaluated in clinical trials. Real world data, or “RWD”, is an umbrella term for healthcare data that are collected outside of conventional RCTs. This information could come from a large number of sources including data from patients (i.e. questionnaire or wearable device data), healthcare data (i.e. medical records), data from payers, and social data.[6]

Examples of RWD sources include:

  • Electronic health records (EHRs)
  • Patient charts
  • Medical and insurance claims databases
  • Billing activities
  • Product and disease registries
  • Patient-generated data including in home-use settings
  • Data gathered from other sources that can inform on health status, such as mobile devices[7]

Real World Evidence (RWE) and Advantages of Real World Studies

Analysis of RWD gives researchers greater insight into the utilization, benefits, and risks associated with medical interventions.[8] This insight, also called real world evidence (RWE), has several advantages over conventional RCTs when it comes to improving health outcomes.

RWE studies are generally less expensive and don’t take as much time to complete. Often RWE can supplant or enhance a control group, leading to a smaller study size. And unlike RCTs that have many inclusion and exclusion criteria, RWE highlights a more representative patient population. This adds perspective about patient behaviours and a treatment’s safety and efficacy in real world settings – including dosing, adherence, off-label use, and more.[9] These broader observations help address patient groups that are not included in RCTs, and they make it possible to identify trends in specific subpopulations.

Since the data are mostly digital, they are also much easier to access, aggregate, and analyze. The sheer volume of RWD emerging from digital health data (often from smart wearables that capture biometrics) is also improving our understanding of disease processes and therapeutic intervention points. Because of this, RWD and RWE have the potential to enrich clinical studies, reduce time to market, and lower the cost of clinical trial programs.[10]

How Vault is Using RWD and RWE to Improve Health Outcomes

Since the COVID pandemic began in early 2020, Vault has partnered with state governments, educational institutions, and businesses across the United States to help keep communities safe. To date, we have administered over 12 million at-home COVID tests to patients, and with that there is a huge opportunity to do something really meaningful with all of that patient data. In partnership with Datavant, the nation’s largest ecosystem of health data, Vault is engaging with researchers in healthcare and life sciences to conduct robust analyses of patient outcomes for individuals who have had or tested positive for COVID, as well as those that did not– and RWD is making this possible.

Why does this matter? Data fragmentation is one of the biggest bottlenecks to improving patient care. Datavant’s switchboard aggregates patient data from internal sources (e.g., clinical trial data, registries, HUB data) and external sources (e.g., medical claims or lab data) in a secure and compliant way. This approach offers a holistic view of a patient’s journey in a single place and integrates critical data across a variety of use cases. This is advantageous for virtually everyone in the healthcare system because it speeds up the process of clinical research, reduces the cost of care, and leads to better patient outcomes.

By analyzing RWD from positive and negative COVID cases, this will help researchers gain more insight into different variants, breakthrough infections, impacts on vulnerable subpopulations, and re-infection. This data will ideally help bring new treatments to market faster, thereby improving health outcomes.


  1. Porter, M. E. (2010). What Is Value in Health Care? New England Journal of Medicine, 363(26), 2477–2481.
  2. Champagne, D., Devereson, A., Pérez, L., & Saunders, D. (2021, December 1). Creating value from next-generation real-world evidence. McKinsey & Company.
  3. Kendall JMDesigning a research project: randomised controlled trials and their principles. Emergency Medicine Journal. 2003;20:164-168.
  4. Cost of Clinical Trials For New Drug FDA Approval Are Fraction of Total Tab. (2018, September 24). Johns Hopkins Bloomberg School of Public Health.
  5. Skerrett, P. (2017, August 3). Why the ‘gold standard’ of medical research is no longer enough. STAT.
  6. What Is Real-World Data? A Review of Definitions Based on Literature and Stakeholder Interviews. (2017, July 1). ScienceDirect.
  7. (2018). Framework for FDA’s Real-World Evidence Program. U.S. Food and Drug Administration.
  8. Toh, S. (2017). Pharmacoepidemiology in the Era of Real-World Evidence. Current Epidemiology Reports, 4(4), 262–265.
  9. Nazha, B., Yang, J. C. H., & Owonikoko, T. K. (2021). Benefits and limitations of real-world evidence: lessons from EGFR mutation-positive non-small-cell lung cancer. Future Oncology, 17(8), 965–977.
  10. Naidoo, P., Bouharati, C., Rambiritch, V., Jose, N., Karamchand, S., Chilton, R., & Leisegang, R. (2021). Real-world evidence and product development: Opportunities, challenges and risk mitigation. Wiener Klinische Wochenschrift, 133(15–16), 840–846.

DISCLAIMER: This article is for general information purposes only, does not constitute medical advice and is not intended to be relied upon for medical diagnosis or treatment. If you are experiencing a medical emergency, dial 911 immediately.