Venture funding in the overall health tech market fell 27% from 2022Read more...
The company's two products help automate tasks, and help with patient follow-up
Physician burnout was a problem long before COVID hit, but the pandemic threw it into overdrive: half of health care workers say their mental health has suffered during COVID-19. That has led to mass resignations, with 18% of health care workers having quit their jobs during the COVID-19 pandemic; even among those who stayed, 31% have considered leaving.
Basically, anything that can make a doctor's life easier right now is going to be crucial.
Rad AI is one company doing just that, seeking to make radiologists more efficient through the use of machine learning and artificial intelligence to automate the most repetitive parts of their job.
"Imaging volumes surged across the country after the summer of 2020, as outpatient imaging centers reopened to handle pending elective imaging," said Jeff Chang, the company's co-founder.
"There's already a shortage of radiologists in the US, with imaging volumes continuing to rise each year, so Rad AI Omni has been key to helping radiology practices reduce fatigue and burnout, while saving time and increasing productivity."
On Wednesday, the company announced a $25 million funding round, bring its total funding to $34 million. The round was led by ARTIS Ventures. with additional funding from Gradient Ventures (Google’s AI-focused fund), OCV Partners, and Kickstart Fund; the company's other investors include UP2398, Santa Barbara Venture Partners, Quarry Ventures, Array Ventures, Fifty Years, GMO VenturePartners, Hike Ventures, and Precursor Ventures.
Founded in 2018, Rad AI's first product, called Omni, automates the impression section of a radiologist's report, in which they summarize and synthesize the conclusions of what they've seen. The technology can write in each radiologist’s preferred language, meaning that it automatically generates language that sounds like that person. The radiologist can then review the impression section, and then sign off.
"There are many incidental findings discovered through medical imaging, a number of which are significant and need to be followed up according to national consensus guidelines and best practices. This includes nodules and masses that could turn out to be cancer, aneurysms that are enlarging over time and may eventually require surgery, and a wide range of other findings that will require treatment," Chang told me.
At the moment, he said, the vast majority of these findings are lost to follow-up because the recommended time interval for follow-up is often in 6 months, or even a year. There's also the fact that, if the original imaging study is done in the ER, or while the patient is in the hospital, the result is reported only to the ER physician, and not to the patient's outpatient provider, even though they'd be the one to order that follow-up study.
Continuity was designed to solve that by automatically identifying and categorizing follow-up recommendations, and then assessing clinical appropriateness based on health system and national guidelines. It also handles customized communication with the patient and their outpatient provider, helps automate follow-up exam ordering for provider confirmation, and streamlines exam scheduling for patients.
"As this ensures that potential cancers and other significant findings are identified and treated earlier, this significantly improves patient outcomes and reduces health system liability, while also driving new financial value for health systems and radiology practices. Continuity integrates directly into health systems' EMR, and also has a platform available for outpatient imaging networks," Chang said.
By automatically generating customized impression text and appropriate follow-up recommendations, Rad AI is able to reduce the number of words radiologists dictate by 30 to 35% per day. On top of that, it also saved them up to an hour per 9-hour shift, while also helping improve report accuracy and consistency, which then results in better care for the patient.
Continuity, meanwhile, results in a rate of successful follow-up imaging and referrals of 70 to 75%, up from from an average of 25% at most health systems. This also improves patient outcomes, reduces health system liability, and creates new value for health systems and radiology practices.
"We've found that the combination of the radiologist's expertise and Rad AI Omni results in more accurate reports than the radiologist on his or her own, reducing error rates and helping ensure that the report answers the main clinical questions asked by the ordering clinician," said Chang.
Rad AI is currently working with seven of the 10 largest private radiology practices in the U.S. and the company plans to use the new funding to expand Rad AI Continuity's presence in the market, and to double the size of its team over the next 12 months, including its engineering teams, clinical data science, product team, and sales and marketing teams.
"We became a fully remote company as a result of the pandemic, which has had an unexpectedly positive impact on team culture and team growth. With team members across the country, we're better able to provide prompt support to our customers," Chang told me.
"Our engineering teams have more set meeting schedules and fewer interruptions, and can thus be much more effective. Our sales team can schedule far more demos and follow-up meetings remotely than in person, which has helped drive our rapid growth this year. Weekly all-hands meetings can accommodate the entire team when done remotely, as compared to being in a physical location; and we bring the team together for company off-sites twice per year, to continue building our company culture and team relationships."
It will also toward building out new features and functionality for both Omni and Continuity; for Omni, that includes billing language improvements, automatic error correction, management of report quality metrics, and for Continuity, that include care campaign management, additional EMR and RIS integrations, further product customizability for health systems.
Finally, the funding will also be used to continue developing Rad AI's third product, which the company will be announcing in coming months; while Chang didn't offer many details he did say that it "will save even more time for radiologists and further improve the quality of care, while allowing us to expand internationally to countries with very different radiology workflows and far fewer radiologists."
"Globally, the volume of medical imaging is expected to double over the next 8 years; new technologies, applied seamlessly into existing workflow, will change the nature of diagnostic care across the world, and make a truly positive impact on peoples' lives and health everywhere."
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