More-than-1-in-4-follow-up-recommendations-were-going-undetected

Before Agamon Coordinate, more than 1 in 4 follow-up recommendations were being missed.

Here’s how the Agamon Coordinate platform enabled a leading healthcare provider to communicate and track recommended radiology follow-ups automatically, and lead to 28.6% more follow-up recommendations than the internally-developed, manual system.

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A radiology provider came to us with a problem...

The radiology department of a leading healthcare provider in the midwest, USA, had developed a process to catch recommendations that their radiologists weren't flagging. Trying to create a more effective workflow that would increase follow-up adherence, improve care transitions, and reduce malpractice liability risks. But the new process (which involved running a keyword search over reports and reviewing the findings manually) was taking three full-time administrators to manage, was an extra burden on IT, was open to human error and hard to scale. Follow-up communications were running eight months behind.

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We deployed Agamon Coordinate, and here’s what happened

Efficient communication and higher standard of care

Agamon Coordinate uses Natural Language Processing (NLP) to automatically extract follow-up recommendations and related insights from radiology reports. The extracted follow-ups can be managed, communicated and tracked through the platform dashboard. Ensuring timely follow-up examinations and allowing patients to make the best decisions about their care.

‍Reduced liability risk

Agamon safeguards against missed follow-up recommendations and communication errors. The platform increased follow-up extraction accuracy from 87% to 97.6%, and found 28.6% more follow-up recommendations than the internally-developed system. The dashboard makes it easier to verify follow-ups and send communication to ER, referring physicians, patients, etc, in a timely manner.

Zero headcount cost to scale

Agamon Coordinate can process thousands of radiology reports in just a few seconds. Deploying the platform meant reduced workload for radiologists and the operations team, and scalability to more locations. The platform software is managed by Agamon, which means less burden on the healthcare provider’s IT team.

Increased revenue

Deploying Agamon not only reduced staffing costs, but also meant a more accurate extraction of follow-up recommendations. The healthcare provider can now communicate with and bring back 28.6% more patients whose recommended follow- ups were previously being missed.

‍How Agamon Coordinate works

Agamon Coordinate is the only follow-up tool available in the USA that truly uses proprietary deep learning models. This technology enables intelligent and efficient

identification of recommendations buried within radiology reports, including insights like modality (MRI, CT, etc) and time frame (when the next follow-up is due, how often).

• ‍Automation means less workload for your team Automating the follow-up process means providers can close the loop on radiology imaging exams without a nurse navigator or other management staff, and without adding any extra steps to your radiologists’ workflow.‍
• Accuracy you can count on. The NLP models that power Agamon technology were trained with millions of radiology reports, and we continue to enhance them. At the moment of publication, our models are achieving over 97.6% accuracy.‍
• Your data is secure. Agamon complies with the strictest security policies and regulations. Our platform is HIPAA compliant, trusted by leading healthcare organizations across the USA.

‍Benefit from the network effect

Organizations build their own solutions to try to save time and money. But often, as they grow to serve more patients and operate in more locations, their internal solution fails. Unable to keep up with larger volume and complexity. Technology companies are technology specialists. We build a solution to solve a problem, and get the opportunity to use every deployment as a chance to iterate and improve our products. On top of learning from deployments and user feedback, the deep learning models that power Agamon technology also benefit from the “data network effect” — growing smarter as they get exposed to more data (in this case, tens of millions of radiology reports).

Every time we deploy Agamon into a new organization, that new customer benefits from the features and functionality gained with every previous deployment.

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