While some clinical labs and in vitro diagnostic manufacturers are only just beginning to explore opportunities in real world evidence (RWE), others are taking concrete steps to participate in RWE ecosystems.

As noted in a recent introductory piece on the topic from June 2022, these efforts include emerging public-private collaborations, such as those driven by the Asia Pacific Medical Technology Association (APACMed), to improve the policies, regulation and capability development for RWE in Asia Pacific [1, 2].

But where specifically can clinical labs have the most impact when generating the real world data (RWD) that can then be harnessed for RWE? For the second post in our RWE series, let’s take a closer look at how lab-driven RWE ecosystems may emerge.

The power of RWD studies

While conversations about data often focus on the use of real-time information to improve regulatory submissions and post-market monitoring of medical products, there are also opportunities in RWD studies, in which lab data is combined with other patient medical data sources, in vast as well as in narrow quantities, in order to complement modern clinical trial activities.

As clinical labs pursue greater digitisation and automation, including through the use of artificial intelligence, they have an opportunity to contribute to RWD studies. Applications for RWD studies in laboratory medicine can range from establishing dynamic reference intervals; real-time quality control of patient data; prognostic modelling; sourcing of analyte variation; epidemiological investigation; and overall next-level laboratory management.

For clinical labs to realise these opportunities, they must recognise the shifts happening and develop capabilities to harness the valuable amounts of data that they generate, notes a 2020 study in Clinical Biochemistry from a team of Chinese researchers about RWD studies in lab medicine [3]. Doing so will require overcoming challenges, including consistency of database architectures and rules for validity of the RWE insights extracted.

The lab as RWE integrator

Increasingly, clinical lab data is expected to be requested as a key source of the RWE ecosystem. Since RWD studies need to combine various datasets from within the lab and beyond, clinical labs may also be in a position to work as a data integrator of the ultimate RWE that brings it all together.

The augmented role of clinical labs, and the RWE data integration therein, can help bend the entire cost curve of the wider healthcare ecosystem. For example, Lab Information Systems (LIS) could become the connector between Electronic Health Records (EHR), disease registries, diagnostic details, feedback from medical equipment, and all the other data sources necessary for RWD studies.

Through RWE analytics, moreover, clinical labs could provide insights which enable faster diagnosis, continuous patient management, identification of efficiency gaps, streamlined billing, and ultimately, innovative contracting arrangements such as outcomes-based care.

Increasingly, payers are taking notice of these opportunities, especially as health economic data across the care continuum is in high demand in order to make smarter resourcing decisions for populations of growing need [4].

Mitigating the barriers ahead

To realise the opportunities of clinical labs in RWD and RWE ecosystems, including their potential role as data integrators, lab leaders must optimise their data across many dimensions, including:

• Relevance – sample sizing and representation of data coming out of lab testing and other devices, when recommending actions based on the results.
• Data Quality & Reliability – completeness and accuracy of the data, particularly in consideration of unstructured formats and lack of standardised testing results.
• Data Linkage – interoperability between clinical labs systems, EHRs and other data sources, as well as the growing sources of ‘omics data.
• Privacy – maintaining compliance with patient consent and protocols that may differ dramatically across countries and jurisdictions, and ensuring anonymity where required.
• Methodological Rigour – strategies for addressing missing data, and maintaining an unbiased evaluation of the RWD/RWE outcomes.

Fortunately, many stakeholders are working together to realise the opportunity. For example, as diagnostic technology platforms mature, IVD companies and labs are able to collaborate around cleaner data protocols. In addition, working committees have been formed across the public and private sectors to try to harmonise the approaches being undertaken for RWD/RWE in clinical labs [4].

Yet more remains to be done, so subsequent articles in this series on RWE and Lab Insights will explore upcoming initiatives and areas of opportunity in greater detail.

References: 
[1] Hardesty, C. “Real World Evidence and Clinical Labs: A Brief Introduction”. Lab Insights: Jun 2022.

[2]  “Advancing Real World Evidence in APAC – Key Considerations for Policymakers”. APACMed: Mar 2022.

[3] Chaochao, M., et al. “Real-World Big-Data Studies in Laboratory Medicine: Current Status, Application, and Future Considerations”. Clinical Biochemistry: Oct 2020.

[4] Baumfeld Andre, E., et al. “The Current Landscape and Emerging Applications for Real-World Data in Diagnostics and Clinical Decision Support and Its Impact on Regulatory Decision Making”. Clinical Pharmacology and Therapeutics: Dec 2022.