Oncology staff usually search thousands of patient health records to find the right patient trials or care pathways. So, Triomics has raised $15 million to help cancer centers streamline these workflows and process oncology data at scale by applying its framework to build institution-tuned large language models (OncoLLM) and use case-specific software. The funding was raised from several Silicon Valley firms making pioneering investments in generative AI and healthcare, such as Lightspeed, Nexus Venture Partners, General Catalyst, and Y Combinator.
The manual chart review can take hours per patient, and many health systems face significant backlogs when completing key oncology-related workflows for thousands of patients. And this workload leads to clinical delays, such as patients missing out on clinical trials or biomarker-driven treatments, lagging quality reporting, and provider dissatisfaction and turnover. Triomics software automates screening a patient based on a clinical trial’s inclusion and exclusion criteria and citing the medical record as evidence
Triomics co-founders Sarim Khan (CEO) and Hrituraj Singh (CTO) were college friends, who later worked as an MIT biotech researcher and Adobe AI researcher, respectively. And they knew software existed to quickly analyze the 20% of medical data that is stored in a uniform and structured manner like a patient’s demographics or lab values. But they realized recent advances in generative AI created the possibility of similarly analyzing the 80% of medical data in an unstructured format, like a doctor’s free-text note.
After developing an OncoLLM with Medical College of Wisconsin researchers, Triomics found in just minutes that it found 90% of eligible patients for clinical trials, which would have taken days or weeks for qualified nurses. And it also extracted structured data points from unstructured notes at similar or higher accuracy to proprietary models such as GPT4 or Claude while being 40 times cheaper. Plus, Triomics recently also published the results of its information retrieval engine for oncology – which they found to be 1.5-2 times better than other state-of-the-art retrieval models.
OncoLLM powers proprietary Triomics software that integrates with health system EHRs to complete specific clinical and administrative tasks. For example, Triomics Prism aids in patient-trial matching by prescreening oncology patients with upcoming appointments to find relevant clinical trials. Triomics Harmony curates EHR data to support quality reporting, cohort analysis, and precision oncology.
Considering the heightened importance of accuracy for oncology data, Triomics partners with leading academic cancer centers and researchers to develop generative AI performance and safety benchmarks and best practices. And the partners include the Collaboration for Oncology-focused LLM Training (COLT), a consortium of leaders from a dozen NCI-designated cancer centers, and the Cancer Informatics for Cancer Centers (CI4CC) Society.
Triomics plans to publish additional data on OncoLLM efficacy across diverse settings and patient populations and develop software that powers additional use cases.
KEY QUOTES:
“Hrituraj and I decided to partner to build solutions leveraging the advances in the field of generative AI and LLMs to help hospital staff. We want our solutions to reason and sound like experts in oncology.”
“We differentiate ourselves by building tailored models specifically for oncology and pairing them with GenAI native workflows. While other solutions address some of the use cases we’re working on, like patient-trial matching, they are broad based solutions that use or modify legacy technologies that have proven not to have the scalability or ROI the industry is requesting.”
– Sarim Khan, CEO of Triomics
“Most of the solutions on the market today claim they use GenAI, but many lack published evidence. Triomics is setting themselves apart by taking a truly collaborative approach to co-developing these models.”
– Bradley Taylor, Chief Research Informatics Officer at the Medical College of Wisconsin and Director of the CTSI Center for Biomedical Informatics
“The ability to quickly and accurately convert complex cancer data into a format that can be used to improve patient care is crucial. Triomics has been a great partner in integrating our suggestions and rigorously studying their approach to ensure safety.”
– Anai Kothari, a surgical oncologist at the Medical College of Wisconsin Cancer Center
“Our investments in our core areas of focus have been deliberate. We have successfully merged expertise in two complex functional areas: our AI researchers, who are specialized in customizing language models to specific domains, and our clinical staff, who have decades of oncology-specific experience. As a result, our software can complement the strengths of these advanced models while also proactively addressing potential flaws, all with the intricacies of cancer research and care in mind.”
– Hrituraj Singh, CTO at Triomics
“Triomics is leveraging existing healthcare datasets and Generative AI to empower hospital staff to automate clinical trials and streamline cancer center workflows. We are excited to back Triomics in this important mission.”
– Dev Khare, partner at Lightspeed
“With robust early results for their proprietary oncology specific LLMs and partnerships with leading cancer care and research centers, Triomics is well poised to deliver significant value to cancer care providers and patients in the U.S. and globally. We are thrilled to partner with Sarim and Hrituraj to help build a remarkably impactful company!”
– Jishnu Bhattacharjee, managing director at Nexus Venture Partners
