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Video

Revolutionizing Patient Care and Clinical Research

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Title:

Revolutionizing Patient Care and Clinical Research, featuring Verana Health CEO Sujay Jadhav, MBA.

Sujay Jadhav, MBA:
Hi, my name is Sujay Jadhav. I’m CEO of Verana Health. Verana Health is a real-world data company focused on quality insights. We focus in three therapeutic areas, ophthalmology, neurology and urology. In essence, we bring in and ingest real-world data from EHR, augmented with other data types, help curate the data with a focus on not just structured data, but unstructured data to help provide insights to help improve quality of care, as well as improve drug research in the therapeutic areas that we focus on.

Question:
Can you provide us with an overview of the recent collaboration between Verana Health and the Foundation Fighting Blindness, and how it aligns with Verana’s mission in revolutionizing patient care and clinical research?

Sujay Jadhav, MBA:
Yeah, sure. Our ground-breaking partnership that we have with the foundation in essence leverages a de-identified genomic and patient-reported data on thousands of individuals with inherited retinal degenerative diseases to support commercial and real-world evidence clinical research initiatives. FFB provides some low-cost genetic testing to people with inherited retinal diseases, and the comprehensive panel screens for over 350 different genetic mutations known to be associated with retinal disease.
The data is captured in the My Retina Tracker Registry. It can be linked using privacy preserving tokens with Verana, a very key population health data engine, which contains real-world data, electronic health record data from AO’s Iris registry. So this combination makes it possible for life sciences companies to accelerate and advance the accuracy of their clinical research, patient recruitment and clinical engagement efforts.

Question:
The press release highlights the use of de-identified genomics data from the My Retina Tracker Registry. How does incorporating genomics data enhance the potential of real-world evidence in ophthalmology research?

Sujay Jadhav, MBA:
The incorporation of genomics information is a critical piece to enable precision medicine. With this added layer, deeper questions can be answered that identify what are certain patient characteristics or associated with response to treatments or predisposition to particular disease subtypes.

Question:
Could you elaborate on the specific goals of this partnership with the Foundation Fighting Blindness? How does it contribute to the advancement of clinical trials and research in inherited retinal degenerative diseases?

Sujay Jadhav, MBA:
So this partnership with FFB enables linking of two distinct data modalities, which helps provide a detailed view into the treatment and care of patients with IRDs. The genetic variant level data from FFB enables patient selection, and Verona’s EHR data helps understand disease progression and within the on-demand needs and this specific IRD patient population. I think together this data can be utilized to further the development of targeted therapies for multiple IRDs.

Question:
The collaboration involves integrating genomics data with the IRIS Registry’s real-world electronic health record data. How does this combined set provide a more comprehensive understanding of patient journeys and outcomes in ophthalmic care?

Sujay Jadhav, MBA:
There are various patient attributes that can be tracked across the journey that impact the outcomes they experience. EHR data houses important insight into demographics as well as clinical attributes assessed longitudinally.
Genomic data can help increase granular understanding of what type of disease the patient may have. It can also serve as another patient characteristic that can be evaluated to see whether it’s associated with different outcomes at the time.

Question:
The press release mentions the use of privacy-preserving tokens to link data. Can you elaborate on the privacy measures in place to ensure the security and confidentiality of patient information in this collaboration?

Sujay Jadhav, MBA:
Both FFB and Verana operate together in a completely de-identified environment by utilizing the Datavant Tokenization platform. Datavant is a third party that offers a best-in-class services to ensure privacy and security of patient-level data to ensure patient’s identity remains safe and secure.
The tokens for individual patient records are generated using PHI. However, the tokens themselves do not contain any PHI. And the token matching between Verana and FFB happens at a third-party site, which has no access to underlining PHI.

Question:
In what ways do you anticipate that the partnership will streamline patient recruitment and enhance clinician engagement in clinical trials for inherited retinal degenerative diseases?

Sujay Jadhav, MBA:
This is a big focus area for the overall partnership. And by using this combined data set, we can target trial sites with precision that have patients with both the relevant clinical phenotype and genotype as it relates to a trial’s inclusion-exclusion criteria. This allows us to go into a deeper set of criteria to select sites with patients that are highly likely to be eligible for a trial to help improve the pre-screening success rates.
This partnership workstream has not yet been formalized with FFB as yet, but we’re aiming to bring the solution forward in early 2024, which will help I think from an overall clinical trial process, reduce costs, help ensure and make sure that we can accelerate research in a very quality fashion.

Question:
The partnership aims to transform ophthalmic treatment through the identification of biomarkers. How do you envision this collaboration influencing the development of targeted therapies and improved treatment outcomes?

Sujay Jadhav, MBA:
One of the key goals of precision medicine is to ensure that the most appropriate and effective therapy is provided to a patient. When working with a database that has this kind of size and depth that Iris Registry and we have, it provides the opportunity to see actually meaningful trends within key cohorts. This includes understanding of baseline characteristics, co-morbidities, the sequence of treatments received and subsequent outcomes experienced.
This collaboration allows for the additional lens of genomic data that can augment the creation of more targeted sub-cohorts. Analysis can be conducted to understand if there are differences in therapy effectiveness across key subgroups to enable more targeted approaches in treatment selection.

Question:
Given the global prevalence of inherited retinal diseases affecting approximately five million people, how do you see the collaboration contributing to a global understanding and approach to these conditions?

Sujay Jadhav, MBA:
So, it’s really around the scale of the Iris Registry. This allows for the opportunity to generate insights on rare conditions that would otherwise have gone unstudied. This often requires a need for multiple datasets in order to reach a sample size that unlocks analyses to be conducted, and the footprint that both the Iris Registry has within the United States enables a meaningful overlap for the dataset from FFB, such that the linked dataset can already contribute to addressing some critical questions that need to be answered with regards to this key disease sub-area.

Question:
As the exclusive data curation and analytics partner of the Academy’s Iris Registry, how does Verana Health plan to leverage its position to further advance research initiatives and support life sciences teams?

Sujay Jadhav, MBA:
There’s so much data that is generated in this day and age with every interaction that a patient has with the healthcare system. This data may exist in a variety of formats, both in a structured and unstructured fashion.
Interesting enough, almost 80% of healthcare data is unstructured, and that’s been the key focus of Verona, which is looking at this unstructured data, which sometimes is not readily available, but leveraging it via techniques such as natural language processing, machine learning models to help generate insights. And those insights can help throughout the overall clinical development process, from helping work through what type of patients that you want to select for a particular clinical trial to understanding the effects of the therapies and these particular procedures throughout the overall patient life cycle.

Question:
What other potential applications do you see for the combined data set, such as in health economics, and outcomes research and medical affairs support?

Sujay Jadhav, MBA:
With the combined data set cost-effective analyses can be conducted with a deeper look into more granular clinical cohorts. It can also be augmented with additional linked information such as claims and imaging.

 

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