COTA and Panalgo Q&A

COTA Spokesperson: C.K. Wang, Chief Medical Officer

Panalgo Spokesperson: Meg Richards, PhD, MPH, Executive Director of Scientific Strategy

Abbreviated answers

Q: What is the value of real-world data for accurate healthcare analytics – what can be learned from high-quality electronic health record data? 

Meg: Real-world data (RWD), particularly those derived from high-quality electronic health records (EHRs), are a goldmine for advanced healthcare analytics. These data offer a comprehensive, longitudinal view of the patient health journey, enabling insights on a broader and more diverse group of patients than the highly selected patients who participate in clinical trials. High quality EHR data can be used to inform improved patient outcomes, more tailored product development, real-time product safety, and the most cost-effective care. RWD can also fuel personalized treatment plans by tailoring care based on individual patient characteristics, genetics, and response to treatments. 

RWD can also fuel personalized treatment plans by tailoring care based on individual patient characteristics, genetics, and response to treatments. With RWD, sponsors can stand up an external control arm or ECA. Placebo arms may not be ethical or practical, so sponsors may wish to create an ECA based on standard of care for comparison to the treated arm. 

CK: Healthcare data is notoriously messy, with cancer data being especially difficult to work with. Cancer patient journeys are complicated, containing numerous unique disease-specific data elements such as staging and line of therapy.  They can be very lengthy with care that spans multiple specialties, facilities and care settings.  Datasets can be incomplete, especially in critical data elements such as death.  Additionally, many others are non-representative and only include patients treated in academic medical centers thereby excluding outcomes from the 80 percent of people who are treated in community hospitals.  All can lead to skewed results that don’t represent a wider population.

By combining the expertise of oncologists, engineers, and data scientists with real-world data from multiple sources such as EHRs and community-based healthcare institutions, we can create a more thorough understanding of cancer care. This comprehensive view provides insights into clinical practices, patient experience, and patient outcomes that better resemble what is happening in the real-world.  More importantly, high quality EHR data can answer questions that clinical trials can not. 

Q: Which types of RWD are fit for regulatory use cases and what data considerations must be made when generating evidence to submit to regulators, and how does this partnership support this application? 

Meg: The types of RWD most often deployed (and sometimes overlaid) in regulatory submissions include EHRs, administrative claims data, real-world patient registries, patient-generated data (PGD), and wearable device data. The data need to be accurate, complete, and representative of the target population, and protected from unauthorized access and use. The COTA-Panalgo partnership is a game changer for “regulatory-grade” RWD and RWE. 

Researchers need to generate RWE with full disclosure and transparency regarding the RWD used, the methods and platforms employed, and the potential limitations of the study. Ideally, another researcher asking the same question(s) using the same data and the same methods would get the same results. We call this replicability of analysis. 

CK: COTA believes that  regulatory ready datasets need to be high-quality, representative, and secure with clear provenance and high pedigree. Our datasets were developed with the concept of “fit for use” in mind and can be further customized to suit different use cases, including regulatory. Panalgo offers a highly efficient, transparent, robust RWD analytics platform that generates regulatory-grade RWE in one-sixth the time that it takes to program an analysis from scratch. Together, we hope to accelerate the speed and efficiency of oncology care while improving outcomes at the point of care. 

Q: What challenges do life sciences teams face when generating evidence from oncology RWD, and how will this partnership help solve those challenges? 

Meg: For one, oncology RWD can be complex and heterogeneous, with varying levels of data quality and completeness across different sources. This can make it difficult to generate reliable and accurate evidence. Accessing and integrating different sources of oncology RWD can be challenging due to data silos, varying data formats, and privacy regulations. This can hinder the ability to generate a comprehensive view of patient outcomes. Analyzing and interpreting oncology RWD requires specialized expertise and advanced statistical methods, so it’s crucial to have the right tools and resources to extract meaningful insights from the data.

CK: COTA’s data network includes over two million cancer patients who have received treatment across 200 sites of care in both academic medical centers and community practices in both rural and urban settings — providing a powerful representative picture of cancer care across the U.S.. COTA delivers datasets containing the depth and context needed to provide clarity for our clients seeking insights to optimize the continuum of cancer care and to improve patients’ outcome and quality of life. COTA synthesizes those records into data that life sciences companies can use to develop better, more personalized cancer treatments. 

Q: As the first oncology data to be added to the Panalgo data network, what value will COTA’s cancer RWD add for life sciences companies – and ultimately patients? [Panalgo]

Meg: By joining Panalgo’s partner network, COTA has made its hematologic oncology data available via Panalgo’s Instant Health Data Analytics (IHD) platform. As the first oncology data to be added to the Panalgo data network, COTA’s cancer RWD will not only add value for life sciences companies, but ultimately, help improve patient outcomes.

The addition of COTA’s oncology data to the Panalgo data network is noteworthy because of the uniqueness of oncology data. Oncology data are complex and highly dimensional. Visualizing the patient journey through diagnosis, genetic testing, treatment modalities, lines of therapy, and various measures of progression and/or survival can be challenging without a highly curated data set and a platform that is purpose-built to visualize and interrogate such data. 

Q: As AI advances, what opportunities do you see to drive even deeper RWD analyses? How may these support stronger analytics, more informed decisions, and improved  outcomes in healthcare? [COTA]

CK: AI has already transformed data analytics, enabling faster data processing and the ability to generate rapid and potentially novel insights. Until recently, RWD research had high barriers  to entry, often requiring data scientists at every step of a project. This approach can be slow and costly. In June, COTA launched CAILIN, a suite of cancer AI solutions that enables researchers to simply ask their research questions of a dataset as they would in a search engine – then receive an answer in moments. The efficiency gained with CAILIN is unprecedented and will turn our traditional model of insight generation upside down.      

Q: Through this partnership, what impact do you hope to have on the future of healthcare analytics cancer care? 

Meg: Our goal is to smooth the path to life-saving cancer treatments, from pipeline to patient and help prolong cancer patients’ lives with good quality-of-life months or years. But why stop at prolonging life? We’d be thrilled to use RWD to help cure cancers with innovative treatments such as vaccine + immunotherapy options like the combination that Moderna and Merck currently have in trials. We’re excited about the possibilities of what COTA and Panalgo can do together and believe that the sky’s the limit.

CK: COTA and Panalgo firmly believe in the value of high quality RWD and rigorous analytic methodologies. We believe our partnership will bring accelerated insight generation and drug discovery across the life science industry and to patients.  Together, we will bring innovations in data and analytics to every sector of the biopharma industry.