This is part two of a two-part series, you can read part one here. In this post, we will cover non-traditional data points, their use in randomized control trials (RCTs), and how they contribute to a successful data strategy.
The rise of real-world data and real-world evidence
Non-traditional data points, in particular real-world data (RWD) and real-world evidence (RWE), are becoming more and more important in the current research climate. RWD is unstructured data relating to patient health status and/or health care delivery that is routinely collected from a variety of sources, including wearable consumer data, disease registries, and electronic medical records. RWE is the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.
Harnessing all of those unstructured pieces of data allows an organization to make regulatory or reimbursement decisions, develop clinical guidelines for how a disease should be treated, and look at how a certain population is trending on a medication or a medical device. This is where the impact and the power of unstructured RWD translate into RWE.
The FDA and other regulators have started to embrace the use of real-world, unstructured data alongside traditional RCT data. This allows sponsors to present a data set that is more conclusive and representative of the patients being studied. Combining unstructured data with structured data offers several advantages, including:
- More accurate patient recruitment
- Faster trial design validation
- Getting data trends to support endpoints
- Real-time access to data
- Support of fast-track regulatory submissions
- Better decision-making across the healthcare landscape
Use of economic data points
Commercialization is the intended endpoint of all therapies under development, and economic data points are another type of non-traditional data point that can be leveraged for better decision-making. In fact, the FDA is encouraging sponsors to start looking at reimbursement insurance data to support the totality of data when dealing with regulators. Now, more than ever, it’s essential that any type of validation and adjudication that can be done on the financial viability of a product or therapy is presented early in the development cycle.
Building a comprehensive data strategy
How can we use non-traditional data points to develop a comprehensive data strategy? The FDA introduced the Technology Modernization Action Plan (TMAP) in September 2019 to serve as a guide for sponsors. A key part of this is a data strategy focused on data quality, stewardship, exchange, and analytics. There are several ways this can be achieved, including:
- Use of artificial intelligence (AI) and algorithmic decision-making. AI technologies such as machine learning are increasingly being used to optimize data
- Increased reliance on patient-reported outcomes (PROs). The patient is now a major stakeholder in terms of contributing to the overall data strategy. As patients become more remote, their contributions – whether from traditional PROs or electronic patient-reported outcomes (ePROs) such as wearables – are now relevant pieces of data
- Integrated data ecosystem. Disparate data sources must be integrated and leveraged together for effective decision-making
Technology-enabled processes can be used to not only acquire the data but also analyze and report outcomes to aid researchers and regulators in bringing creative solutions to patients faster. New processes are now enabling us to better understand the data and how to translate it into what is usable and what can be improved to better the lives of patients going forward.
Altogether, these objectives can help an organization build a successful and modern data strategy that is ready for today’s evolving clinical research environment.
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