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How digital innovation can inform RWE-led decision-making

Digital innovations are changing how the industry not only develops new products but also supports decisions about how best to position those products.

Digital screen with icons of healthcare sectors

Embedding digital innovation within any design or data management initiative is becoming key to successfully navigating product development, regulatory, market access, and commercialization. One area of growing importance is the ability to leverage real-world data (RWD) to develop real-world evidence (RWE) into how products work in broader populations. RWE can help pharmaceutical companies to better understand a product’s potential, hone their messages, and engage with other stakeholders more effectively.

Leveraging RWE for product decision-making

The interplay between RWE and ultimately getting a new therapy to a patient is increasingly recognized as a value proposition. RWE can improve understanding of a medicinal product’s efficacy and safety and potentially give patients faster access to innovative therapies.

Companies can use this RWE to define the market for their product and carve out a niche by demonstrating differentiated or superior outcomes to a competitor product in some conditions or in specific subpopulations. For example, if a product in the oncology space is used as a second- or third-line therapy, a company might use RWE showing better survival or better quality of life to demonstrate an advantage over a traditional therapy.

While randomized controlled clinical trials are the gold standard for evaluating safety and effectiveness, they are tested on a limited population. Once approved and a much broader population begins using those products, new information comes to light that can be leveraged by the company and by payers in different markets.

RWE offers insights into the effectiveness of medications, especially for populations that are often underrepresented in clinical trials, such as children, the elderly, and individuals with comorbidities. Moreover, in some disease areas, traditional clinical trials are neither feasible nor ethical, such as for patients with life-threatening or rare conditions.¹

Making sense of RWE with digital tools

Innovative digital technologies are integral to realizing the full potential of RWE. The journey to understanding this RWE begins by leveraging information from multiple sources including medical claims and billing data, electronic health records, patient registries, patient reported outcomes, public health databases, and patient-generated data such as from wearable devices.

To determine what this data means, digital innovators and analysts search for a dataset that can answer a specific scientific question, such as the data needed to position a company’s product or support a label expansion. These insights can be honed by leveraging artificial intelligence (AI) and other digital tools, for example, helping companies to understand patient behavior and understand disease progression.²

For their part, payers are expected to use digital tools, such as AI, to assess product data in order to inform coverage decisions, product reimbursement, and patient management. Digital tools such as an interactive dashboard or web page can provide payers with not just the data but an analysis of what that means, perhaps even allowing them to drill down into specific geographies or patient populations versus just having tables of data to sift through. This could potentially enable decisions to be made in a more informed manner.

There is also movement toward ingesting and managing vast amounts of data earlier in the clinical development cycle, even before there is evidence or claims data about the specific intervention, such as to identify rare disease patients. This could help to inform which indications a company might pursue, inform product development, support communication with the regulatory authorities, and give payers insight into how much they potentially might pay for a product depending on how many patients have a rare disease or cancer subtype.^3,4

two people looking at a tablet with chart

Overcoming the barriers to leveraging RWE

One major drawback to RWD is that the data used is large, dispersed, and messy. Much of it is claims data and, as such, is not designed for research and would need to be cleaned up before data analysis could begin. Digital tools or sophisticated methodologies are key to enabling this data to be ingested, transformed, and managed, and, ideally, requires a cloud computing environment that supports agnostic data to be ingested quickly. With the right tools in place, data scientists and health outcomes researchers could review data findings against specific scientific questions and offer strategic approaches to help companies make sense of the data.

Another important consideration is the broader comfort level with using digital tools to analyze RWE patient data. Companies need to feel assured that they aren’t breaching regulatory guidance, and, as of now, there is insufficient guidance to give companies that confidence.

Regulatory authorities are working to provide greater clarity on RWE. The European Medicines Agency (EMA) issued its report on an RWE framework to support decision-making, assessing experience with using RWD to support regulatory decisions.⁵ The Food and Drug Administration (FDA) has issued multiple guidances on the use of RWE and RWD to support regulatory oversight.⁶ Nevertheless, there remains a need for more guidance around how digital tools can and should be used to analyze patient data.

Payers and health technology assessment (HTA) bodies are also starting to put greater focus on RWE and the use of tools such as AI for evidence generation. The United Kingdom’s National Institute for Health and Care Excellence (NICE) published a real-world evidence framework in light of the important role RWD plays in understanding how care is delivered, patients’ experience, and the wider impact of interventions.⁷

NICE has also issued a position statement on the use of AI in evidence generation, highlighting both the important role AI will likely play in helping to inform NICE decisions as well as concerns around appropriateness, transparency, and trustworthiness.⁸ In its review, NICE states: “Real-world data may lend itself increasingly to the use of AI approaches as accessibility and standardisation of large datasets reflecting routine care and real-world populations improve. AI approaches have several potential roles for supporting real-world evidence across numerous stages of evidence generation.”

McKinsey recently explored the important role AI could play in reimbursement by lowering medical and administrative costs for payers, noting, however, that few so far have seized the opportunity to leverage AI.⁹

Advancing the goals of RWE in 2025

Despite the uncertainty, use of RWE and AI to enable payers to make better-informed decisions is gaining traction and is likely to see greater interest in the year ahead.

Both payers and pharmaceutical companies are likely to commit to greater collaboration or openness, taking advantage of digital solutions such as cloud-support platforms to share data. This would allow pharmaceutical companies to gain access to large databases in return for letting health systems see how they are using that data, and possibly even results from the use of that data. Furthermore, providing tools that could help smaller health systems, such as regional hospitals, gain on-the-ground insights and make informed formulary decisions for their patient populations. Such a move would go a long way to supporting access to medications for often under-served patients.

About our experts:

Derek Swiger, PharmD, MS is an Assistant Director with Cencora’s Global Consulting Services Digital Innovation team and serves as the Digital Innovation Business Partner for the Market Access and Healthcare Consulting Value Delivery Center.

Ryan Fiano, Ph.D., MPH is an Assistant Director on the Real-World Evidence team at Cencora. He provides expertise in health economics and outcomes research studies through knowledge of data management, research design, data analysis and interpretation, biostatistics, machine learning, and scientific writing.

Derek Swiger

Assistant Director, Digital Innovation, Cencora

Ryan Fiano

Assistant Director, Real-World Evidence, Cencora

The information in this article does not constitute legal advice. Cencora, Inc., strongly encourages readers to review available information related to the topics discussed in this article and to rely on their own experience and expertise in making decisions related thereto.