Posted: 17 Feb. 2022 8 min. read

RWD strategy may increase value of analytics-generated insights

By Karla Feghali, senior manager, head of ConvergeHEALTH Expert Services, and Seshamalini Srinivasan, manager and product management lead, ConvergeHEALTH Miner platform, Deloitte Consulting, LLP

Life sciences company leaders often ask us to help them strengthen their technology capabilities and operations so that they can make more effective use of real-world data (RWD). RWD is generally defined as patient data that is routinely collected from sources outside of a controlled clinical trial. Examples include electronic health records (EHRs), claims and billing information, product and disease registries, wearable and mobile devices, and other sources that can inform on health status.1

RWD is playing an increasingly important role in life sciences and health care decision-making across disease types. The value of RWD—as well as the value of the technology that can analyze the important trends/patterns it contains—has been most recently apparent in the ecosystem’s response to COVID-19. For example, RWD combined with data from clinical trials has been used to assess the impact the virus has on patients and the effectiveness of vaccines. RWD has also been used by regulators to help determine dosage timing and the need for boosters.2 In addition, RWD is being used to understand the impact of COVID-19 on treatment patterns for other therapeutic areas (TAs). Advanced artificial intelligence and machine learning (AI/ML) algorithms were used to increase the odds of vaccine-trial success by identifying the most appropriate patient populations and the best locations for trials. This helped to ensure that vaccines were made available to the public at an accelerated pace. The combination of various sources’ of RWD data on populations with COVID-19 has produced one of the largest RWD sets ever available.3

Understandably, many life sciences companies are making sizeable investments in licensing RWD to support the understanding of disease, improve clinical-trial designs, augment trials with external control arms, and to generate innovative treatments and devices. 4 What is less clear is why many of these companies appear to be more focused on licensing different types of data from different sources than first thinking about the types of analytics-generated insights they might be able to pull from the data. Setting data strategy as an afterthought rather than a prerequisite can make it difficult to demonstrate value from newly achievable analytics.

Companies that are planning to license RWD can increase its effectiveness and value by proactively developing a data strategy that clarifies the types of data they're licensing and the subsequent analyses they want to conduct. Companies should consider focusing broadly on data that generates evidence across indications, therapies, and use cases, and allows hundreds of analyses to be run. Companies might also link internal clinical-trials data with RWD to answer novel questions at an accelerated pace. Leaders of life sciences companies should consider the following questions:

  • Have we conducted a landscape assessment of current and potential data vendors, academic institutions, disease-specific foundations, and niche players based on the types of analytics we want to conduct?
  • Are the investments we’re making in licensing data also helping to structure the data for use across a variety of fast-changing analyses, from R&D through commercial launch?
  • Where did the data originate? Does the data vendor manage where the data is being collected or does the vendor clean and standardize second-hand data?
  • Are patients informed about how sharing their data can help advance science? Do they understand that their identity will be protected? Have they consented to use of this data?

More data, more availability, more potential

The quantity and types of RWD data—and the availability of that data—have expanded substantially over the last several years, offering ever-more potential for reaping value-added insights from data analysis. The types of analyses company leaders want to conduct should determine their data strategy. This can help ensure that they can cost-effectively license the mix of foundational, TA-specific, and fit-for-purpose data that best meets their needs. Here’s a brief overview of those three types of data and how they might be used:

  1. Foundational data—Large data sets, multiple TAs, less complex data models, good for exploratory analysis
  2. TA-specific data—Extensive data models, deep dive into the TA of interest (e.g., oncology)
  3. Fit-for-purpose data—Often accessed and built through partnerships (both traditional and non-traditional/disruptive vendors). It can take effort to structure fit-for-purpose data to be usable, but when done correctly, it can truly differentiate the organization. Our last RWE benchmarking survey showed that more than 80% of surveyed companies are entering into strategic partnerships to access new sources of RWD, but at the same time, 64% say that lack of infrastructure and/or business model to manage and analyze data to support collaboration is a challenge. 

Over time and across analyses guided by thoughtful data strategy, life sciences companies should be able to assemble a robust, cost-effective repository of RWD from multiple commercial, academic, and non-traditional sources to help enhance current offerings and enable future innovations.


1. Real-word evidence, The US Food and Drug Administration, February 1, 2022

2. Real-world evidence confirms high effectiveness of Pfizer-BioNTech COVID-19 vaccine and profound public health impact of vaccination one year after pandemic declared, Pfizer new release, March 11, 2021

3. Effectiveness of the Single-Dose Ad26.COV2. S COVID Vaccine, medRxiv. October 2020

4. External control arms and debunking real-world data myths, pharmaphorum, August 31, 2021

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