The pandemic is prompting widespread use—and misuse—of real-world data

Abstract

COVID-19 has swept across the world, overwhelming healthcare systems and raising countless questions about how best to diagnose patients, treat infections, save lives, and contain the pandemic. In short order, researchers have launched randomized trials to uncover pharmacologic interventions that hold the promise of preventing or lessening the severity of the disease. But getting results takes time. And time was a luxury that doctors on the frontlines of the coronavirus fight could ill afford in the early months of the pandemic. Desperate for medical insights without delay-and hoping to address other questions not answerable in a specialized research environment-researchers, pharmaceutical companies, and government agencies immediately turned to health information captured through insurance claims, electronic medical records, patient registries, and other so-called "real-world" data sources. By analyzing trends in COVID-19 datasets, the research community rapidly helped fill in knowledge gaps around disease symptoms, risk factors, racial disparities, and more. Such observational methods also hinted at which treatments seemed to be making an impact-and which were not-all in near real time. But harnessing this type of real-world data is a tricky business. It requires high-quality data collection and proper methodological considerations. There are established guidelines on how best to plan, execute, and report observational studies in a way that ensures The dangers of COVID-19 present an unprecedented opportunity to leverage diverse, real-world data sources to inform medical and regulatory responses. But researchers and clinicians must be careful not to sacrifice methodological rigor. Image credit: Shutterstock/keyframelab.

Worldwide Trends in Registering Real-World Studies at ClinicalTrials.gov: A Cross-Sectional Analysis

“…[29][30][31] The pandemic has also led to a degree of abuse in promoting the widespread use of RWD. 32 The three main components of RWSs (the triad of research question, design, and data) are important when evaluating research validity. 33 Therefore, a visual framework is recommended for demonstrating study designs and data observability.…”

Linking digital surveillance and in-depth virology to study clinical patterns of viral respiratory infections in vulnerable patient populations

“…Use of real-world evidence has become common practice for those aiming to improve pandemic preparedness and response ( Drury and O'Connor, 2021 ; Pillai, 2021 ). Regulatory agencies and stakeholders from both, academia and industry, endorse compliance with framework conditions for real-world data to become actionable ( Pillai, 2021 ; Berger et al., 2017 ; Dolgin, 2020 ; Drury and O'Connor, 2021 ; Jarow et al., 2017 ). In a contemporary opinion article, Pillai outlined key premises and avenues toward meaningful real-world data and systems in the context of pandemic preparedness ( Pillai, 2021 ).…”

Data-Driven Decision-Making in COVID-19 Response: A Survey

“…The objective of all these fields is to analyse data and to provide insights that actually assist to perform effective decision making. Data-driven decision making is changing the field in all professional endeavours, including business, medicine, and education [29]. Here, experts gather and analyse data to effectively prompt their decision making [38,50].…”