Are you going to be caught out by the RWE revolution?
Real World Evidence (RWE) is the clinical evidence about the use and potential benefits/risks of a medical product derived from analysis of Real World Data – as opposed to data generated by traditional clinical studies.¹
RWE is used haphazardly across the product life cycle - predominantly used for developing value propositions to market access and payer customers² but also to understand the disease burden, disease progression, identify patient segments and monitor patient safety.³
However, its applicability is rapidly evolving with some interesting areas for the future…
Outcome data
As the market shifts to greater emphasis on patient outcomes, with a focus on getting the right treatment to the right patient at the right time,⁵ RWE will be used less for understanding sub-populations, and burden of disease but more in understanding the outcomes achieved with new medicines.⁴
Healthcare system data
Healthcare professionals and payers are also demanding more data to understand how efficacy data impacts the healthcare system.⁵ This in turn is leading to biopharma organisations creating Integrated Evidence Plans, or IEPs¹. These help organisations see a fuller representation of the impact of a healthcare intervention, drawing in multiple domains of health information like pieces of a puzzle, and understanding the gaps and opportunities.
The impact of these will be the increased use of RWE earlier in the lifecycle of the product and as part of the R&D mix.²
Traditionally used in R&D for informing the clinical trial design and site selection⁵, it will, in the future be used for innovative applications, like to build regulatory-grade synthetic control arms or to design adaptive trials.³
Both NICE and the FDA have created frameworks to utilise RWE, recognising that it can resolve gaps in knowledge and drive forward access to innovations for patients.⁶ ⁷
AI Data
A huge variety of data now falls under the RWE umbrella. AI-generated data is accelerating fast and non-health data, such as consumer credit-card spending, geospatial data, and web-harvested data presents new possibilities, but also challenges⁵.
To understand the volume of data, companies are increasing their analytical maturity with ‘advanced-analytical models’³. These are sophisticated data-engineering approaches which include predictive models, machine learning, and unsupervised algorithms. Obviously, there is a worry on the interpretability of the data and how this will be used to inform decisions⁵. Other concerns for this data is the quality and the bias which can be hard to detect – a whole other story for another time.
At LUCENT, we partner with biotech and pharmaceutical organisations that don’t want to fall behind. We have designed and implemented Integrated Evidence Plans and Evidence Solutions that maximise on the value of RWE data.
Contact us today to see how we can help you.
References:
² The latest RWE trends from top 50 pharma | Clarify Health
³ Real-world evidence use accelerates | Deloitte Insights
⁴ Real world evidence impactful areas worldwide 2018 | Statista
⁵ How pharma companies are applying advanced analytics to real-world evidence generation | McKinsey
⁶ Overview | NICE real-world evidence framework | Guidance | NICE
⁷ The future is bright for RWE in pharma approvals | Reuters Events | Pharma
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