Real-world data and real-world evidence are used to monitor the post-market safety and adverse events of drugs. The monitoring of this data assists in making regulatory decisions. The healthcare community is using RWE and RWD to support coverage decisions and to develop guidelines and decision support tools for use in clinical practice.
The rising geriatric population (and the subsequent increase in the prevalence of chronic diseases) is a key factor driving the growth of this market. The shift from volume- to value-based care, delays in drug development (and the subsequent increase in development costs), growth in R&D spending.
The emergence of this pandemic has posed severe financial constraints on pharma-biopharma companies in several countries. In this regard, RWE solutions have proven to be very helpful, as they allow industrial and academic researchers to monitor patients using digitally connected platforms.
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The uncertainty brought on by the COVID-19 pandemic has dramatically shifted how and when patients decide to seek medical care. In addition, shifts in healthcare coverage and provision during the pandemic have changed the discovery and reporting of certain outcomes in data and the treated population.
This means that disease trends may lead to incorrect interpretations when RWD and RWE are not framed in the context of the pandemic and long-term COVID-19 disease, therapy, and lifestyle changes. The following were identified as key concerns affecting RWD and research due to the pandemic.
Even though emerging technologies, such as telemedicine, have existed for decades, most of healthcare systems rely heavily on in-person interactions between patients and clinicians. Nevertheless, the current requirement for social distancing measures is swiftly pushing the primary care provision toward remote care.
Telemedicine and virtual care may also prompt a greater adoption of technologies such as wearables and digital therapeutics, thus accelerating digitalization in the healthcare space and boosting the importance of RWE and AI.
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RWE can provide valuable insights to better understand, monitor, and prepare for the challenges caused by outbreaks such as the COVID-19 pandemic. The utilization of RWE in infectious disease control is not a new concept. During the Ebola outbreak in 2014, forecasters successfully used Global Epidemic and Mobility (GLEaM) simulations that combined real-world data on populations and their mobility with rigorous stochastic models of disease transmission to predict the global spread of the disease.
By understanding where and how quickly the outbreak is likely to spread, the same tracking models could be adapted to fight any upcoming COVID-19 outbreaks. Even though most countries have announced decreasing death rates from COVID-19, real-world evidence may become an essential tool in trying to suppress the disease.
Mobile contact-tracing apps have played an important role in easing the spread of COVID-19 in China. Government-backed apps analyzed personal data to group individuals into color-coded categories corresponding to their health status and level of risk to contract COVID-19.
Organizations such as the Bipartisan Policy Center have also published recommendations to advance the generation and use of RWE for regulatory evaluation. Such developments are expected to drive the growth of the RWE solutions market.
Life ScienceAventior has been working with some of the leading pharma, biotech, and diagnostics companies providing the technical know-how and support to build their digital ecosystem.
With a significant focus on harnessing the power of data for drug research, clinical trials, and drug manufacturing, leading life sciences companies are building solutions that will help them achieve faster go-to-market solutions.Additionally, Aventior is also helping develop solutions for telehealth and eCOA.
In the current pandemic the popularity of telehealth systems, mobile apps for patient and physician interaction, remote monitoring, real-time analytics are becoming increasingly important.
These technologies also help retain patients longer for clinical trials and overall improve the end-user experience by reducing the number of time patients have to spend at clinics.The use of AI for drug discovery, pre-clinical and clinical data analysis, RWE and patient recruitment, pharmacovigilance, clinical imaging analysis, digital pathology, and commercialization is becoming increasingly important.
Companies are using AI to reduce their cost of drug development, identifying new molecules faster, recruit the right patient and retain them during clinical trials.Life Science Solutions by AventiorData Restructuring and Analytics Platform (DRIP)DRIP integrates data from disparate sources and creates a universal database for easy data visualization.
Read moreContinued Process Verification (CPV) - AutoCPV - Auto is an AI-based automation platform for processing pharma/biotech compliance documents.
