How ‘real-world evidence’ helps in transforming healthcare
Real-world evidence is helping the healthcare sector manage data and cull actionable insights from it.
3 MINS READ
Industry
- Healthcare
Patient-centered Care: Health and Wellness Redefined
Highlights
- Analyzing real-world data (RWD) to study the value of a drug’s effectiveness in treating medical conditions is integral to improving patient outcomes.
- Real-world evidence (RWE), or insights from the analysis of real-world data, is key to understanding the efficacy of a drug.
- Cloud helps healthcare organizations improve patient outcomes by transforming genomics, transcriptomics, and other omics data into actionable insights.
Why real-world evidence
Improving patient outcomes with insights from real-world data
Data is central to the drug development process, understanding the efficacy of a drug, and improving patient outcomes. Real-world evidence (RWE), or insights from the analysis of real-world data, is particularly important. Based on data used by life sciences and healthcare companies to securely obtain, store, and analyze a drug or medical invention, RWE helps demonstrate the effectiveness of a drug or tool to treat medical conditions in a real-world setting. One of the main benefits of using RWE to evaluate drug effectiveness is that it provides a more comprehensive view of how a drug performs in the real world. It reflects the diversity of patients and healthcare settings in the real world and not just a clinical trial cohort.
RWE can also help medical professionals identify subpopulations of patients who may benefit more from a particular drug. For example, it may reveal that a drug works better on older patients or patients with certain comorbidity, thus helping medical professionals make informed treatment decisions for improved patient outcomes.
Safety is another aspect. Adverse events that occur in the real world may not be captured in clinical trials, and RWE can help identify potential safety concerns that may have been missed in clinical trial settings.
Scale clinical trials
Augmenting clinical trial data with RWE and data from mobiles and wearables
In recent times, the use of mobile devices, wearables, and different mobile equipment to collect and store massive amounts of health data has surged. This data has the potential to allow life sciences companies to conduct clinical trials more effectively. They can help answer questions pertaining to medical unmet needs, natural history, and the burden of disease; identify potential biomarkers; and help shorten the duration of planned clinical studies. By combining this information with RWE, clinicians can efficiently determine the appropriate study sample size, and modify study selection criteria and clinical endpoints. All of this can help expedite the drug development process, which is time-consuming and expensive.
Creating value
Giving personalized medicine a booster shot
Another area that RWE can give a boost is personalized medicine. Companies today are focusing on developing integrated solutions and moving “beyond the pill” to provide solutions based on the genotype of a person, rather than take a one-size-fits-all approach. With valuable insights into how a patient responds to a particular drug, RWE can play a key role in making personalized medicine more effective. At a time when companies are under pressure to create more value for all stakeholders, it can help drive greater value for patients by enabling them to offer new value-based pricing. This emphasizes payment for a therapy or treatment only when it works for the patient. For instance, when approved in 2013, Sovaldi, the drug that cures hepatitis C, was priced at $84,000 for a course of treatment. However, after a cost-benefit analysis in patient populations, it was brought down to $55,000 in Canada and $33,000 in France.
The potential of cloud
With cloud, it is now easier for pharma companies to derive huge datasets from a vast pool of sources.
The best way to maximize the value of RWE is to successfully integrate disparate data types. Consider data on medical devices, wearables, genomics reports, data claims, clinic trials, and electronic medical records—all of which come in different types and sizes from different sources and which life sciences companies must store, search, analyze, and normalize. Managing these massive volumes of data and making them accessible for use is a big challenge. Healthcare payers and governments continue to face enormous data management and storage capacity challenges.
This is where cloud comes in, making it easier to integrate and access data from multiple sources. Cloud providers like AWS offers more. With AWS data lake, data can be stored as-is without the need to transform it into a predefined schema. What’s more, the artificial intelligence and machine learning capabilities of leading cloud providers like AWS help process data for real-world evidence, with quick access to all types of data, such as genomics, clinic trials, and claims. They also enable the integration of new RWE data into existing data in the data lake.
Advanced RWE analytics use predictive and probabilistic causal models, unsupervised algorithms, and machine learning to extract deeper insights from these data sets. By combining the power of cloud and RWE, pharma companies can drive technological innovations that can transform the industry and benefit patients.
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