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Unlocking the Value of Real-World Data to Provide Real-World Care

For this tHEORetically speaking video interview, we spoke with Mary Tran, MS, from Syapse for a deep dive into the strengths, weaknesses, and challenges of real-world data (RWD). Mary also explains some effective multi-source RWD strategies to transform patient data into patient experience. Read a transcript of the interview below!

Who is Mary Tran? What is Syapse?

I’m Mary Tran, Senior Vice President and General Manager of Data Insights here at Syapse. Many of you may know Syapse as a real-world evidence (RWE) company, but we are more than that. We are uniquely positioned as the premier Real-World Care company. We sit at the intersection of unlocking the value of RWD and putting those findings into action with our health system, life science, regulatory, and molecular lab partners to improve patient outcomes and experience.

In collaboration with our health network partners, we are not only working to aggregate the most comprehensive and recent data needed to answer ever-evolving research questions, but also staying closely tied with clinical practices to act upon our mission of extinguishing the fear and burden of cancer and serious disease. My primary focus is to oversee our data team in transforming multi-source data into intelligent data that helps fuel the discovery of insights for life science companies and providers.

What are the strengths and limitations of RWD?

I really like how this question is framed as there really are strengths and limitations to all evidence generation in healthcare, even for gold standard clinical trials. The strength of real-world data is that it already exists! A collective, common goal for those in the healthcare space is to identify and deliver the best therapies to each patient. Rightfully, the evidence burden is high for evaluating the effectiveness and safety of treatments. This evidence burden can hamper getting novel therapies onto the market and even expanding existing therapies to new populations, and increases the cost of research and development.

RWD helps to fulfill the evidence need in a way that may not add tremendous cost to the system by utilizing data that is already being captured during the course of a patient’s routine care. Not only are you able to better understand how actual patients are being cared for and potentially subsegment treatment populations even further, but you can monitor the outcomes of regimens to intervene in a patient’s care more quickly and develop case studies for new treatment planning.

As for limitations, RWD was not built for the purpose of research itself. Using RWD isn’t like setting up a clinical trial where you can control for variables or dictate the capture of data. You are typically taking what is already existing and available, which may be coming from regular patient visits or data that were used for patient billing.

I consider this aspect of RWD more of a challenge than a limitation, though. To really unlock the value of RWD, you need to understand the clinical practice patterns and documentation systems of each provider network. At Syapse, we pull everything we can to build confidence around the context and meaning of the data points we look at to ensure that our insights are as reliable as possible. I will not underestimate this challenge. Syapse has worked for over a decade with experts knowledgeable in different EMR systems, informaticists, clinicians, data scientists, analysts, and epidemiologists to turn this into trustworthy evidence. One other limitation of RWD that we acknowledge is understanding where RWD fits best. There may just be some outcomes that aren’t easily replicated with RWD, and so increasing the level of transparency and documentation for how RWD is used can help build that confidence that is necessary in different stakeholders within the RWD community. An example of data that may not be readily available within the RWD space is RECIST criteria, which isn’t captured in routine care. What we can do, however, is work together to validate different proxies for outcomes and other RWD endpoints; for example, using treatment-based assessments for progression-free survival.

What is an effective multi-source RWD strategy that helps advance Real-World Care?

Our clients tell us that they often struggle with piecing together what we call the “Swiss cheese” that is RWD. What we mean by this is that there may be perceived “holes” or data gaps in individual sources that can be filled when layering or reviewing these multiple data sources together, and our health systems come to us to do this heavy lifting. When you put these multiple data points together, they may either corroborate or refute one another. From there, we can select either the best data source from which to pull that data variable, or even develop composite scores or algorithms to unlock the value of different data sources.

For an RWD strategy to be effective, I think those looking at it need to have a healthy dose of humility and appreciation for its complexity. We like to say here that no one health system or lab is exactly like another. The different data sources can really reflect differences in how patients are cared for. Different formularies within a health system, different clinical guidelines that may be in practice, and the setup of the EMR system itself should all be considered when investigating these data variables.

We very much see it as a puzzle, and we enjoy the investigation and the nuances that are revealed when looking at these multiple data sources together. We also say that RWD isn’t lacking, it’s just different. We want to stress the importance of documenting and increasing the transparency of how you are working with RWD to ensure we’re building confidence in the market itself. This is why we actively work with other data partners that share the joint vision of advancing RWD/E, including the RWE Alliance and the FDA.

We also like to put our own investigations out there for peer review and discussion, including our different validation methods, algorithms, and study results, which helps advance the field as a whole and drum up that dialogue of what is “trustworthy.” We like to encourage others out there, especially RWD recipients, to be looking for peer-reviewed literature to understand if there is quantitative proof in the data they are looking at and ensure that the data sources they’re using really do meet the quality thresholds for transparency, completeness, accuracy, recency, and even “fit for purpose” datasets.

Lastly, on how to turn RWE into care: review the populations that your data represent. At Syapse, we choose to focus on the community health system market because that is where the majority of cancer patients are treated. These populations aren’t set up to look like the controlled populations that you may see in clinical trials. These are real patients with different comorbidities, socioeconomic factors, and barriers to actually getting the care that they need. Our ability to look at these patient populations on a regular basis gives us the opportunity to intervene with our provider network to improve care for people across different populations and potentially even reduce health disparities.

In closing, our ask of you is this: no matter where you are in the healthcare innovation cycle, ecosystem, or the segment in which you reside, please join us in our mission. Together, we can extinguish the fear and burden of serious disease through the power of RWD because we all deserve the best care, and that is personalized, Real-World Care.

Meet Mary and the Syapse team live at ASCO 2022 in booth #18143 in Chicago June 3 – 7, 2022! Check out the event site to learn more about Syapse’s plans.

tHEORetically Speaking

tHEORetically Speaking is the official blog of HealthEconomics.Com.

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