Main Street Radiology Adopts Agamon's AI-Engine to Boost Follow-Ups Automatically
NEW YORK, Nov. 23, 2021 /PRNewswire/ -- Medical imaging services provider Main Street Radiology (MSR) is building stronger relationships with referring physicians and seeing more patients return for vital follow-up exams with the help of health-tech startup Agamon's deep learning & Natural Language Processing (NLP) technology.
Dr. Ari Jonisch, President at MSR, said thousands of patients come through MSR's doors each month, referred by thousands of physicians, "and we're always looking for new ways we can provide our patients and referring physicians with the most advanced service and unparalleled care".
"We've been searching for a solution to help us detect actionable findings in textual reports automatically, accurately, and at scale. Furthermore, we wanted to personalize the way we communicated with referring physicians and patients, to prompt more patients to attend the follow-up exams that our radiologists recommend."p>"Since deploying the Agamon Coordinate care navigation platform, we've been able to increase the likelihood that a patient returns for the appropriate follow-up examination at the appropriate time. While accomplishing this important goal, we have been able to reduce administrative costs and administrative workload. The number of patient follow-ups scheduled has risen by 50%," said Dr. Jonisch.
Agamon CEO Michal Meiri said accuracy, automation and personalization were key factors behind MSR's immediate follow-up adherence results.
"The NLP models that power Agamon Coordinate have been trained on tens of millions of radiology reports. Our technology can read the unique language of radiologists and detect information that's often missed. Our platform currently identifies follow-up recommendations with over 97% accuracy, which means we can automate the whole process that follows an exam being dictated — including alerting the referring physician or patient about a recommended follow-up in a timely manner, using their preferred communication methods, such as text or email, and finally alerting MSR whether a follow-up has or hasn't been scheduled."
"With Agamon, MSR can bypass issues that come with manual data entry, such as inefficiency, low accuracy, and scaling challenges, while providing patients with personalized care — automatically, without a nurse navigator."
"We're excited to be working with such a forward-thinking organization that's at the forefront of technology adoption and patient-centric care," said Michal.
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