OM1 Aims To Improve Healthcare Outcomes Through Big Data And AI

Harvard trained surgeon and big data entrepreneur Dr. Richard Gliklich has been working to improve the management of chronic conditions by organising, analysing and applying health outcomes data since 1999.

Currently he is the founder and CEO of OM1 (pronounced Ohm-one). The company is a health outcomes and registries company focused on the measurement, comparison, and prediction of real-world treatment outcomes, including what the company refers to as the first intelligent data cloud for healthcare, enabling more precise information and better decision making for stakeholders across the healthcare ecosystem. This founder’s journey story is based on my interview with Gliklich.

“After my first company was acquired, I spend time in venture capital and became interested in how big data and artificial intelligence in particular could be applied to electronic health data, to first understand real world outcomes of chronic diseases and treatments at a greater scale, and then be able to apply that information to accelerate medical research,” says Gliklich.

The Boston, Massachusetts-based start-up, founded in 2015, presents an example of the boom in healthcare data and analytics. The global healthcare analytics market size was valued at $23.51 billion in 2020 and is expected to rise to $96.90 billion by 2030, according to Allied Market Research.

“We had originally aggregated a lot of data from a lot of sources, but we realised that in the U.S., healthcare is generally managed by specialists, and that if we could actually create networks of specialists in different specialty areas and aggregate data, bring in data collaboratively from them, we’d get much deeper clinical information. And that would really be better for driving research and personalising care. So we created networks in areas like rheumatology and dermatology and cardiology and otolaryngology. Principally, chronic condition areas,” says Gliklich.

OM1 bring together data, which then that gets processed through electronically to create datasets that have health outcomes. One of the things that are missing a lot of datasets is understanding what the endpoints of care are. “So we enrich it by using AI and other technologies to understand health outcomes. And once we’ve done that we can use the data to better understand what the natural history is of a disease or condition—what’s likely to happen to me with disease x in five years,” says Gliklich.

This way, OM1 can look specifically at comparative treatment outcomes and which treatments work best in a particular population versus other populations. Gliklich points out that what OM1 is doing is different from clinical trials, which are inherently limited, because of the limited numbers of patients that can be enrolled and the types of patients and the data can be biassed by who’s enrolled. “Using this real world data approach, we can better understand how drugs and devices work in real world, clinical use, meaning all patients, not just those treated in the trials,” says Gliklich. As a result, OM1 data is providing a roadmap for treatment and its progression based on past results from, in some cases, thousands of similar patients.

After seven years of aggregating and enriching patient outcomes data, OM1 is building up an impressive list of partners with whom they work with and with those who use the data for commercial purposes. The company is now approaching 200 employees and according to Gliklich, “We’re working with probably seven of the top 10 pharma companies, half of the top 30, and probably half of the largest medical device companies.”

The company now has evidence generating networks with eight different clinical specialties, like dermatology, rheumatology, neurology, cardiology, plus mental health, which Gliklich feels is an underserved area for research. “And we’re working on chronic diseases that cause tremendous suffering in areas that you can imagine, like immunology and mental health, and Cardiometabolic conditions,” says Gliklich.

As a result, the company has attracted nearly $190 million in financing to date. It’s latest $85 million Series D financing in July 2021 was led by D1 Capital Partners, Kaiser Permanente, and Breyer Capital, with participation from existing investors, including General Catalyst (GC), Polaris Partners, Scale Venture Partners, 7wire Ventures, and Glikvest (Gliklich’s venture business founded in 2014.)

Gliklich’s parents were both immigrants and refugees from Eastern Europe. And its every immigrant’s dream to come to America and have their son grow up to become a doctor, which Gliklich fulfilled (in fact he is still a licensed surgeon). But his entrepreneurial journey began when as a surgeon and a faculty member at Harvard affiliated teaching hospital, doing research on patient outcomes. “I was a happy academic faculty member doing research and surgery and I was approached by the institution’s business development director to consider spinning my laboratory, which wasn’t much of a laboratory, out as a company,” says Gliklich.

He entered the business world with some scientific and technical expertise, but almost no business knowledge. He learned by finding good advisors and mentors and learning from experience and making lots of mistakes. He ran his first company Outcomes, which spun off that research laboratory for about 12 years, until its acquisition.

“I learned something in that. But when I joined the acquiring company, which then ultimately went public while I was there, and then I was in the venture capital world and learned about that. So it’s been a long, lifelong learning process. I’m intellectually curious, and I think that was the most important thing. I don’t know that I ever planned to be a CEO,” says Gliklich. He considers himself fortunate to continue being a physician and surgeon and have his academic affiliation continue, along with being a founder and CEO.

As for the future? “What probably gets us up in the morning, is the opportunity to personalise medicine using this phenotyping, which is very similar to genotyping but using data to identify patients with, for example, an abdominal aortic aneurysm before it actually is problematic. Over the next three to five years, use of the data in the real world for outcomes for treatment decisions for FDA and regulatory purposes will grow. But the next five years after that, it’s all going to be using data for personalising care, the opportunity is massive there,” concludes Gliklich.