How the Pandemic Emphasized the Importance of Real-World Data

The COVID-19 pandemic demonstrated how important real-world data (RWD) is for informing public health policy decisions and improving clinical trials. In many ways, the virus set global society back by decades. The medical community and the life sciences industry found themselves truly in the dark with the arrival of SARS-CoV-2, with little data or evidence to drive strategy and decision making in combatting the virus. On a positive note, this necessity led to the acceleration of various innovations that had already been introduced but had yet to catch on fully, from decentralized clinical trials and digitalization, to new ways to leverage RWD to inform the investigation of diseases and treatments as well as policy making.

Prior to the pandemic, we had seen increasing use of RWD enabled by greater access to electronic medical records (EMRs) that longitudinally connected patient’s pharmacy, medical records and laboratories. In this blog post, Avanish Mishra, Ph.D., global vice president of growth initiatives, provides insight into how this convergence brought about a tipping point for RWD, with significant growth in its use occurring over the last two decades.

Making decisions without data and mainstreaming real-world data

The pandemic challenged governments and businesses around the world that were seeking answers in real time but lacked the data or evidence to drive decision making and inform policies, which led to diverse, fragmented policies and responses. For example, ventilators were widely used until it became evident through the propagation of RWD that they could sometimes harm patients. Alternatively, RWD demonstrated that steroids or anticoagulants administered at the right point in the disease progression improved outcomes significantly.

During the COVID-19 pandemic, the concepts of RWD and real-world evidence (RWE) became mainstream. RWD and RWE (which is the clinical evidence derived from analysis of RWD) were no longer restricted to experts and enthusiasts. Suddenly, society as a whole became much more aware of the biostatistics relevant to infectious diseases. They were not discussed only in technical conferences, but in living rooms and newspapers, on television and the internet, and during social conversations, likely over Zoom. Everyone — government officials included — was captivated by issues that epidemiologists and pharmaceutical scientists deal with professionally.

Elevating real-world data as a critical tool for evaluating diseases and their treatments

Fortunately, recognition of the potential utility of RWD is increasing, and the COVID-19 pandemic underscored the value and importance of RWD. At the beginning of the pandemic, the medical community knew little about SARS-CoV-2 and the patient journey. Quickly, though, it became apparent that questionable treatments, such as plasma therapy and hydroxychloroquine, adopted without any evidence of efficacy, were not effective, and we saw the dangers of the politicization of science. By leveraging the evidence emerging from the real world, we were able to recognize the lifesaving impact of older and cheaper therapies, such as dexamethasone and anticoagulants. The incorporation of these therapies in guidelines improved outcomes and saved lives.

Real-world outcomes have also been critically important for the rapidly developed COVID-19 vaccines, which reached the market through emergency use authorizations based on limited clinical data. As more people received COVID-19 vaccines, there was an increasing body of evidence suggesting that these vaccines were indeed safe and effective. It is a perfect example of a use case of RWE in real time in mainstream society.

For RWD to have a real role in clinical trials going forward, it will be critical to optimize the quality of data, study design and methods. While the industry has come a long way in this area, there is still room to standardize and improve.

Real-world data value goes beyond pharma

RWD, it must also be stressed, is not just valuable for improving drug development and enabling the confirmation of the efficacy and safety of post-marketed products. It is also needed for effective policy and guideline development. Masking policies are a good example of this concept in action. People want to know why they need to wear a mask, and when there is no clear, supporting RWD that masks prevent airborne disease transmission, the result can be nonacceptance, as we saw early in the pandemic and still lingering in some areas and within certain groups. 

In fact, many of the prophylactic measures implemented during the pandemic (e.g., social distancing, mask wearing, hand washing) were predicated on RWD. Epidemiologists and public health authorities are less bound by the approaches to data required for drug approval, and need to make critical decisions based on the evidence at hand. This experience has increased the awareness among policy makers of all kinds that medical data and evidence are essential components of effective decision making related to public health, and critical to ensuring consistent policies within and across countries. This pandemic accelerated the adoption of “sewer surveillance” as a tremendously insightful source of RWD at a population level.

Anecdotal versus true real-world data

This lack of data, coupled with increased mainstreaming of RWD, highlighted the crucial need to differentiate between anecdotal evidence (as reported by journalists and shared on social media) versus true RWE based on science and actual RWD. This issue is important to the current investigation of post-COVID syndrome, the broad constellation of symptoms — including exercise intolerance, dyspnea, chest pain, palpitations, pulmonary or cardiac complications, etc. — observed in a subset of patients who have recovered from acute infection.

Despite recent efforts by the medical community and academic researchers, post-COVID syndrome lacks a formal case definition. Currently, the syndrome is being defined and characterized by both journalists and scientists, and in many cases, the narrative is driven primarily by anecdotal information. Scientists, meanwhile, can’t respond appropriately, because they may not know much about a patient’s lifestyle, preexisting conditions and other important factors that have a direct impact on a post-COVID syndrome diagnosis.

At this point, scientists are trying to catch up with mainstream journalists to provide scientific evidence — RWE — that is obtained with appropriate rigor. We are still collectively trying to understand exactly what post-COVID syndrome entails.

Immediate lessons to apply

There have been several other lessons learned about RWD from the COVID-19 pandemic that can be applied in the near term. Clinical trials could be supplemented with RWD about the patients participating in the trials. Capturing data on all electronic medical records would provide more complete pictures of the patients beyond the data collected in the trial.

RWD can also be used to enable precision medicine development. Understanding which phenotypes respond to a drug and why (e.g., genetic, physiological, lifestyle) can help drug developers fine-tune their assessments and make them more precise. The placebo arm in clinical trials remains an ethical dilemma, which was further highlighted in this pandemic. Replacement of placebo arms with external control arms will address this issue. Semi-external control arms, meanwhile, could provide valuable insights on how real-world factors affect drug efficacy and safety.

The medical community may also want to stress the importance of RWD in the rollout of vaccines. In the United States, because COVID-19 vaccines were largely administered in community settings and were not linked to EMRs, the ability to collect RWD was significantly hindered. Israel, on the other hand, was able to quickly and robustly monitor and report large quantities of data on vaccine safety and efficacy, because the vaccines were administered via a centralized, national effort with considerable organizational, information technology and logistical infrastructure.

The need for greater diversity in clinical trials was once again highlighted during the pandemic. Most of the Phase III studies were initially conducted on Caucasian populations. Fortunately, course corrections were made approximately halfway through the trials, but the issue of diversity and representativeness in clinical studies must be systematically addressed. Generally, incentives are given to recruit participants as quickly as possible and to reduce costs — not to ensure a diverse patient pool. The pharma industry needs to develop better ways to collect more high quality RWD, and the U.S. Food and Drug Administration and other health authorities must become more comfortable with the risks associated with using RWD.

The need to capture patient 360

Currently, health care providers only see a patient’s health through the lens of health care intervention. One of the challenges today for drug development is the high level of noise that exists because predictions can only be made using data from health care interventions, while there are significant valuable data sitting outside the health care system.

Drug development and health care delivery will be greatly enhanced when we are able to fully capture patient lifestyle data, known as “patient 360.” This includes collecting and aggregating information on how long they sleep, how much alcohol they drink, how much exercise they get, what groceries they buy, what environment they live in, and their attitudes toward factors affecting lifestyle and health. With that RWD in hand, drug developers will become savvier, reduce the level of noise and be better able to explain disease outcomes. This will not only increase the efficiency of drug development and utilization, but will also expand our understanding of diseases themselves, which will feed back into new approaches to drug discovery and development.

While there are strict quality controls for how clinical trial data are collected, cleaned and processed, RWD can be messy, as researchers are dependent on data that are captured or reported by individuals. Wearables and biosensors offer attractive means for collecting additional data and enable real-time data capture in a manner that was not previously possible. If these technologies can be incorporated into clinical trials more often — both for internal and external cohorts — the additional data could potentially help explain why certain phenotypes have certain outcomes for the same disease or certain responses to a given drug. The value of these kinds of patient-generated data are clear, but these data are often not captured in a systematic manner outside of the context of clinical trials.

Another current limitation in leveraging wearable data collected outside of clinical trials is that the people who track their own health tend to represent a particular definable subset of individuals — highly aware, highly motivated and disciplined. We need utilization of wearables to significantly increase in the general population before they can become more statistically useful.

The value of RWD to society is at a critical stage, and its utility will continue to increase, providing efficiencies across the spectrum. It holds tremendous promise in supporting key advances in areas like diversity, precision medicine and artificial intelligence, to name a few.