As public and private healthcare organisations around the world become increasingly aware of the power of ‘real world evidence’ (RWE) in developing and evaluating medical products, many are launching pilots and seeking policy guidance to explore the opportunity. While biopharma companies have been active in RWE for years, however, in vitro diagnostics manufacturers and clinical labs are only just beginning to join these efforts.

But what exactly is RWE and why might it matter for clinical labs? Let’s take a closer look.

What is RWE?

RWE is all about generating evidence from the aggregation and analysis of ‘real world data’ (RWD), a term that empasses the wide range of data sources that provide real-time insight on the health of populations and the performance of treatments, diagnostics and medical services. By leveraging these datasets, healthcare stakeholders aim to streamline the development, evaluation, regulation and monitoring of various interventions.

RWD can come from a variety of sources, such as electronic medical records, patient registries, and diagnostic information from wearables, digital biomarkers, and clinical lab datasets. The diversity, size and real-time nature of RWD presents many advantages over more traditional approaches to data generation and analysis. Examples include:

• Driving R&D and delivering clinical trials with improved efficiency by collecting a greater volume of data in less time
• Shortening the gap between concept of a medical product to market execution by using RWE to support regulatory submissions
• Providing a better understanding of safety, effectiveness and efficacy of medical products along a patient journey

While interest in RWE has been growing for many years, the COVID-19 pandemic helped to showcase its potential. In the United Kingdom, for example, one emergency department deployed a rapid, laboratory-centred COVID-19 triage process using artificial intelligence (AI) to reduce diagnosis time from 24 hours to one hour upon arrival at the hospital [1]. AI-enabled screening highlights the application of RWE and the resulting benefits: better patient care, better use of clinical resources and better outcomes.

Emerging RWE policy frameworks

In order for healthcare stakeholders to build a viable RWE ecosystem, the policy and regulatory environment must support its use. A recent white paper on Asia’s emerging RWE ecosystems from the Asia Pacific Medical Technology Association (APACMed), a trade group, called for stronger policy frameworks and foundational infrastructure to be established in areas such as criteria development, capacity-building and collaboration [2].

Longer term, RWE will thrive when there are more participants and contributors in the space, from biopharma to medical devices to clinical labs, so capacity-building is a key activity to enabling a robust RWE ecosystem. This requires thoughtful collaboration between governments, industry, patients, clinical labs and other stakeholders with the shared vision to support RWE adoption.

As Tracey Duffy, First Assistant Secretary, Medical Devices and Product Quality Division for Therapeutic Goods Administration in Australia said during the APACMed whitepaper launch webinar: “I think the industry commitments and effort for RWE put forth in the whitepaper need to be reciprocated, in terms of other ecosystem stakeholders like government taking on board the recommendations and building responsibility of the pieces of work to be done”.

Implications for clinical labs

While participation of clinical labs in RWE ecosystems has been limited to date, labs have vast and untapped potential to be an integral part of RWD production and RWE adoption. Since a large share of medical decisions are determined using results from clinical labs, the data generated from these sources are invaluable in improving diagnosis and overall patient care.

“RWD in the lab has the potential to lower inefficiencies and fill gaps in information silos among stakeholders throughout the healthcare ecosystem of diagnostic labs, providers, payers, manufacturers, government bodies and patients,” says Varun Veigas, Policy and Strategic Partnerships Leader at Roche Diagnostics Asia Pacific and co-author of the APACMed report on RWE.

“This information sharing, in turn, enables all parties to generate new insights, support value-based care and deliver better health outcomes for patients,” adds Veigas, who also serves as Chair of the China Centre of Excellence and Vice Chair of the Reimbursement Working Group of APACMed’s Digital Health Committee.

Data from patient profiles, for example, has the potential to reveal insights from underrepresented cohorts and to impact how studies are designed, especially in a diverse region like the Asia Pacific. Within study design, RWD can establish reference intervals for specialised cohorts in an efficient and low-cost manner. This requires lab data to be standardised [3].

There are already a number of applications of RWD/RWE emerging, and the next frontier will be to harness the data being collected into insights that transform care and even predict outcomes. Lab managers and the broader clinical lab community can take the lead in designing a model of RWD and RWE usage in Asia Pacific, ensuring that it meets the needs of all stakeholders and positively impacts patients too.

Research and writing for this piece was contributed by Navi Boparai, Independent Consultant and Adjunct Lecturer, University of Toronto.

References:

[1] Soltan, A., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., Beer, S., Soltan, M., Thickett, D., Fairhead, R., Zhu, T., Eyre, D., Clifton, D., Watson, A., Bhargav, A., Tough, A., Rogers, A., Shaikh, A., Valensise, C., Lee, C., Otasowie, C., Metcalfe, D., Agarwal, E., Zareh, E., Thangaraj, E., Pickles, F., Kelly, G., Tadikamalla, G., Shaw, G., Tong, H., Davies, H., Bahra, J., Morgan, J., Wilson, J., Cutteridge, J., O’Byrne, K., Farache Trajano, L., Oliver, M., Pikoula, M., Mendoza, M., Keevil, M., Faisal, M., Dole, N., Deal, O., Conway-Jones, R., Sattar, S., Kundoor, S., Shah, S. and Muthusami, V., 2022. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. The Lancet Digital Health, 4(4), pp.e266-e278.

[2] APACMed. 2022. Advancing Real World Evidence in APAC – Key Considerations For Policymakers – APACMed. [online] Available at: <https://apacmed.org/advancing-real-world-evidence-in-apac-key-considerations-for-policymakers/>

[3] Ma, C., Wang, X., Wu, J., Cheng, X., Xia, L., Xue, F. and Qiu, L., 2020. Real-world big-data studies in laboratory medicine: Current status, application, and future considerations. Clinical Biochemistry, 84, pp.21-30.