RamaOnHealthcare April 18, 2024
How to Rapidly Expedite Cancer Research
Today, RamaOnHealthcare talks with Vinay Seth Mohta Co-Founder and CEO of Manifold, an AI-powered clinical research platform that streamlines the manual workflows of modern study and data management, enabling researchers to do more high-impact research and collaborations with fewer resources. The company recently raised $15M in Series A funding to scale this platform and support the company’s vision to make clinical research 10x faster and one-tenth of the cost. Previously, Mohta was CTO & Co-Founder of Kyruus, an enterprise data platform serving the 400 largest health systems in the US.
Vinay Seth Mohta, Co-Founder and CEO of Manifold
RamaOnHealthcare (ROH): Your company just launched a new AI-driven clinical research platform, but you’ve been around for a few years. What is Manifold and what has it been up to?
Vinay Seth Mohta (VSM): Manifold was founded in 2016 as an AI lab, at a time of remarkable technology advances. Machine learning was becoming more prevalent, but we saw firsthand how hard it was for healthcare organizations to build a solid data and AI technology foundation. We recognized an immediate need within clinical research management to leverage machine learning to streamline the manual workflows.
…we saw firsthand how hard it was for healthcare organizations to build a solid data and AI technology foundation.
We are grateful to our early adopters like Indiana University Melvin and Bren Simon Comprehensive Cancer Center and Winship Cancer Institute of Emory University whose valuable product feedback over the past two years has resulted in a product that will accelerate time-to-insight and increase efficiency for all our customers.
ROH: There’s a lot of hype around AI. Where do you see AI having the most impact in the clinical research space?
VSM: We have been selective in our application of AI, focusing where there’s lasting value – not just a flashy demo—and where the robustness and reliability of the approach and solution match the need. Two large areas of opportunity for impact are intelligent infrastructure and intelligent interfaces.
Two large areas of opportunity for impact are intelligent infrastructure and intelligent interfaces.
Intelligent infrastructure refers to the unglamorous yet critical data wrangling that happens behind the scenes. It is the aggregation and curation of data using AI-powered pipelines from multiple sources (e.g. EHR, surveys, LIMS, sequencing, histology images, etc.) into a patient-centric, analysis-ready model. This is all about automating as much of the manual workflows and administrative processes as possible. Importantly, this is also about preparing and enabling clinical research for the new AI world, since high-quality data is the essential ingredient for future AI applications.
…high-quality data is the essential ingredient for future AI applications.
Intelligent interfaces should give clinical researchers what feel like superpowers today: for example, the ability to explore clinical information using patient-centric terms (e.g. “How many breast cancer patients with BRCA2 did we treat last year?”) rather than data-centric terms about rows and columns (which then require waiting for someone’s help). We are enabling our partners to have “conversations with their data” – to truly be able to leverage analytic tools fit for the needs of clinical researchers.
We are enabling our partners to have “conversations with their data”….
Overall, the growth and diversity of data is surpassing the legacy data management and analytic tools and manual processes in clinical research. AI can automate many of these processes and significantly expand the scale of problems we can address.
ROH: What makes Manifold different from other technology that exists? Why has it not existed previously?
VSM: The reality today for much of the specialized world of clinical research is still manual study and data operations. Data sets are stored and maintained in legacy systems like Excel spreadsheets, REDCap, and SAS, with some information – such as pathology results or imaging data that are often needed to fully describe a patient’s cancer diagnosis – saved as copies across shared servers or scattered in digital silos. Simple analytic questions or even data refreshes require a months-long project, with a 20-step process and significant time from research analysts.
There are general-purpose modern data infrastructure and applications in place inside leading technology companies today, but they have not been built for clinical researchers in an end-to-end way that works for them.
For the first time, clinical research teams have an advanced AI-powered, purpose-built platform that makes it possible to enroll and engage study participants, harmonize, and curate longitudinal data, find, and analyze multimodal datasets, and collaborate securely across organizations – all in one easy-to-use platform.
For the first time, clinical research teams have an advanced AI-powered, purpose-built platform….
ROH: What are the positive results you’ve seen so far?
VSM: At Indiana University Melvin and Bren Simon Comprehensive Cancer Center, the Biospecimen Collection and Biobank Core will reduce the average turnaround time this year for researcher requests by more than 50% and double the researcher requests supported by its current team by reducing the manual effort and costs required per request.
As another example, at Winship Cancer Institute at Emory University, researchers are using Manifold to securely access and analyze data from a real-world study conducted at another institution.
Another research organization has unified over 11 terabytes of multimodal data which spans 1.5 million patients into Manifold, enabling consolidation and retirement of several legacy systems. In addition, reducing manual processes has both lowered costs and reduced data lags from months to less than 24 hours. This year, this organization will also double the number of multi-institution collaborations supported while keeping headcount the same.
…reducing manual processes has both lowered costs and reduced data lags from months to less than 24 hours.
ROH: How will this change the experience for patients facing a cancer diagnosis?
VSM: On a macro level, President Biden’s Cancer Moonshot initiative has recently put a greater focus on the cancer community, mobilizing resources and creating new initiatives to support the goal of reducing U.S. cancer deaths by 50 percent by the year 2047. According to AACR, cancer death rates must decline faster than they currently are to meet the ambitious target — from decreasing by 2.3% each year to decreasing by 2.7%. Research organizations must adopt new technologies like Manifold to make up these gaps.
If Manifold can help expedite cancer research, it will result in better prevention and early detection, and in getting the right therapies into the hands of the right patients faster.
ROH: What else can you tell us about Manifold?
VSM: Manifold recently announced its Series A of $15 million and believes it can make clinical research ten times faster and at one-tenth of the cost.
About Vinay Seth Mohta
Vinay is the CEO and Co-Founder of Manifold, an AI-powered clinical research platform to streamline the manual workflows of modern study and data management. Prior to Manifold, Vinay was the CTO and Co-Founder of Kyruus Health, a successful enterprise healthtech company of the previous decade that provides the industry’s leading care access platform serving the largest health systems in the US. Previously Vinay was on the early team at Endeca Technologies, a successful enterprise tech company that provided enterprise search and business intelligence applications and was acquired by Oracle. Vinay has earned multiple patents for his contributions. He holds an S.B. and M.Eng. in Electrical Engineering and Computer Science from MIT.