Three years ago, Cota, a New York-based digital health company had three employees. Now, the company is using the minds of nearly 100 employees to solve one of the most difficult problems in healthcare — finding a way to provide a universal data model for healthcare to help physicians improve the standards of care. Ultimately, the company seeks to make changes not just to fundamentally change how clinicians use data and make decisions in healthcare, but to accelerate the pace of research in academic networks, and provide real-world evidence to pharmaceutical companies developing cancer-fighting treatments. As the company works to solve this challenge, it is producing a significant amount of research in the field and has been published in 15 peer-reviewed journals and delivered presentations at multiple industry conferences.
There’s no way to determine if you are giving the right care to the right patient; at present, there’s no method of looking at patients on an apple to apple basis. That is an information problem we are trying to solve.
Considering the economic impact of cancer treatment, Cota’s introduction to the market could not have come at a more opportune time. According to the American Cancer Society, about 600,920 people are expected to die of cancer in 2017 while the Agency for Healthcare Research and Quality is indicating that the per-person costs of cancer are exceeding those of the #1 killer — heart disease. “We are talking about a healthcare system where costs are rising in an unsustainable way,” says Levin.
Healthcare is moving from fee-for-service care delivery to one based on value, but according to Levin, no way exists to quantify that value. “That’s because there’s no way to determine if you are giving the right care to the right patient; at present, there’s no method of looking at patients on an apple to apple basis,” Levin says. As the medical costs increase, determining the right care will be crucial. Cost expenditures rise when an aging population is combined with the complexity of all the new therapies that are coming to market. “That is an information problem we are trying to solve,” he says.
When evaluating how to turn patient data into useable insight at scale, one quickly realizes the complexity of such a challenge. Levin says it’s one part technology, one part medical/clinical IQ, one part biology, and one part data science and analysis with a certain strong human component added to that. “You just can’t press a button and have a computer calculate this automatically; it takes a system to do this with an incredible amount of engineering and manpower to accomplish the feat. This is not something that two guys in a dorm room can hammer out,” Levin explains.
Healthcare, as well as this multi-faceted challenge for treating diseases with such complexity, remains a giant problem. Only a couple of companies have tried to tackle it. “IBM Watson is a great example. We are very friendly with them, but they are attempting to solve the problem by taking a different approach. They’ve applied the most powerful artificial intelligence (AI) engines in a different way on the data,” explains Levin. “That’s the magnitude of of the problem we are trying to solve.”
In the beginning, the company co-founders used the full name of its database tool — Cancer Outcomes Tracking and Analysis, but later began using the acronym (Cota) when they saw that the Cota methodology could be applied to more than just cancer outcomes. The company has started with oncology, but it definitely has expansion plans. Not all medical areas make sense for the Cota methodology. Orthopedics being one example of where many of these problems have been solved with bundled payments, but Levin says, “many other areas like cardiology, behavioral health and other specialties where a certain level of complexity exists do make sense.”