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Dyania Health, Inc. raises $5.3M seed to advance their proprietary and physician-built natural language processing technology to drive better outcomes in clinical research.

Dyania Health’s technology de-identifies and derives clinically accurate meaning from unstructured and structured EMR-based patient data to automate manual chart review, a process commonly used in pre-screening patients for clinical trials, researching biomarkers, and running in-silico synthetic studies. Going forward, the company plans on expanding use cases to several areas throughout clinical trials and research.

Dyania Health was founded in 2019 and is led by a seasoned entrepreneurial team, NLP data-scientists, physicians, and serial drug inventors who have worked at some of the US’s top institutions including UCLA, Harvard Medical School, Northwell Health, and Kite Pharma. The the founders are committed to addressing a significant and unsolved problem for clinical trials: today manual chart review to pre-screen patients for clinical research is antiquated, cumbersome, and time-consuming. The process to match patients on very specific and time-sensitive clinical trial criteria typically takes 18 – 24 months for most trials.

Within the oncology field alone, there are approximately two million new patients per year in the US. These patients’ histories are also dynamically changing over time as their diseases progress, often quickly. Hence, an army of clinical research associates constantly reading ever-changing sets of EMR files is an impossible feat, and as a result, patients are often never found. Finding a “needle in a timestack”, within a patient’s window of opportunity, could mean qualification, access, and potential enrollment in a clinical study for a drug that could be an additional, life-saving care option for a patient who has exhausted other avenues.

Others have also noted the potential for Artificial Intelligence (AI) to aid this process. Today, AI utilized across other application areas is typically a probability-based technology, highly effective in determining the likelihood of a binary outcome like a preference for a movie, a watch, or a pair of shoes. It also has proven to be effective in determining the likelihood of benign or malignant diagnoses from medical imaging, often better than a human physician. On the other hand, clinical research criteria, like much of medicine, are prescriptive, complex, and based on a series of decision trees. A.I. cannot solve this determination in the same way it has been used in other applications. The HIPAA-compliant technology Dyania Health is building takes a prescriptive, rule-based approach, utilizing in-house medical and scientific expertise to drive optimal clinical research, bring drugs to market faster, improve patient outcomes, and save patient lives.

John Chelico, MD, CMIO of Common Spirit and former CIO of Northwell Health described,

DyaniaHealth’s approach to working with large health systems quickly creates a safe IT environment that unlocks and de-identifies the unstructured patient data from electronic health record databases. This process will be instrumental in accelerating the throughput of clinical trials accrual in a safe and compliant way.

To fuel scale of algorithms and engineering, platform product development, and clinical and pharmaceutical client support, Dyania Health has announced the close of a $5.3 million seed round, led by Innospark Ventures. Also participating in the round were Outsiders Fund, Wild Basin, Big Pi Ventures, and Tau Ventures along with recent investment from Genesis Ventures and TLife Investments. The group of investors notably represents a diverse geographic reach to include Boston, New York, San Francisco, Austin, and Athens, Greece.

Matt Fates, partner at Innospark Ventures and new board member at Dyania Health described his excitement for the company’s future,

Matching patients with today’s complex and time-sensitive trial criteria, especially within oncology, where rescanning patient records frequently is often required, is inherently something computers should do better than humans with the proper training. Dyania has built a robust solution that goes well beyond existing offerings, can decipher the proper matches down to the most granular level, and can do this on a daily basis, ensuring no patients are missed. We see tremendous potential to accelerate patient enrollment and increase the number of successful trials, delivering more new therapies to the right patients faster.

Dyania’s founder and CEO, Eirini Schlosser, further added,

We founded the company to bridge two very different worlds, technology and medicine. Physicians’ knowledge is based on centuries of experience taught in medical school and residency programs along with defined, modern standard-of-care protocols. Medicine maintains strict protocol standards that are slow-changing with little room for deviation or creativity in order to minimize risk to patients. On the other hand, technological innovation lends itself to a completely opposite philosophy. We are excited to bridge these worlds and enhance human physician abilities to run clinical research in a compliant manner and to find patients who could benefit most from new therapies under study.

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