With the help of the Mayo Clinic, four healthcare startups with artificial intelligence technology are bringing their products to market.
The companies are the first members of Mayo Clinic Platform_Accelerate, the first project they have been working with the startups on since March.On Wednesday, companies such as cliexa, Quadrant Health, ScienceIO and Seer Medical had the opportunity to Mayo Clinic Platform Conference.
According to Accelerate, in order to apply for the program, startups need AI models that are on the market or close to the market website. Each company that accepts the 20-week program will receive $200,000. Mayo experts work with these companies to understand AI model requirements, examine fairness and bias in their AI models, and help them understand the FDA approval process. Accelerate also provides participants with access to Mayo Clinic’s de-identified patient data, which helps them validate their models and plan clinical validation studies.
“These companies are able to take already excellent products and services and accelerate them with access to rich de-identified datasets and expert advice,” said Jamie Sundsbak, partnership manager for the program.
For these companies, the program has proven to have huge benefits, according to Eric Harnisch, Vice President, Partner Programs, Mayo Clinic Platform. Despite a tough job market in early March, one of the companies was able to fill several positions that were long vacant because of Accelerate. Investors contacted all companies, some with suppliers they wanted to work with, Hanish says.
This is just the beginning of the acceleration program. A second batch of seven companies will start this week, Harnisch added. Accelerate will continue to be done twice a year.
The four companies in the first batch of Mayo Clinic Platform_Accelerate:
Seer Medical, based in Melbourne, Australia, is focused on helping people with epilepsy. Currently, access to quality testing is limited, making it difficult for doctors to diagnose and manage the disease, said CEO Dean Freestone. Seer Medical provides physicians with data that makes it easier to diagnose and manage conditions.It is already established in Australia and is using the Accelerate program to help expand its presence in the US
“Currently, patients take about five years on average to get good treatment,” Freestone said. “We want to shorten that to a few weeks.”
Denver-based cliexa offers a suite of support tools and software to help providers deliver care faster. According to its website, the platform covers patient onboarding, remote patient monitoring, provider documentation, automated scheduling, revenue cycle management and telehealth.
“We sell solutions to providers to help fill areas of care that they may be repeating work, and we basically give them the tools to enable them to deliver the same level of care faster,” said Arin Seidlitz, director of product design .
Boston-based ScienceIO creates machine learning models that take unstructured healthcare information — such as documents, doctor notes and internal databases — and make it more structured for easier analysis. Ultimately, the technology reduces administrative processes for payers and providers.
“In practical terms, that means if you’re a hospital, you’ll spit out thousands of pages of notes about your patients,” said founder and CEO Will Manidis. “You really struggle to find out what’s going on in those notes. Our API [application programming interface] Take these notes, among other documents, and turn them into something you can actually use in data science or machine learning or provide better care. ”
New York City-based Quadrant Health aims to use artificial intelligence to analyze electronic health records and patient data to predict disease before it happens, said CEO Anin Sayana. He claims that Quadrant’s model can predict disease 42 hours before clinicians suspect it and provides 40% fewer false positives than typical models.
“When clinicians use our model in a clinical setting, they are more likely to trust that the model is accurate, will provide useful predictions and give them enough time to actually make a difference in patients,” Sayana said.
Photo: metamorworks, Getty Images
