Studies indicate that more than 50% of patients requiring follow-up care fall through the cracks, leading to poor patient outcomes, including late diagnosis. This is often due to challenges in communicating imaging results and follow-up recommendations to referring physicians, PCPs, and patients. Subsequently, these gaps in care coordination lead to missed revenue and increased clinicians’ liability risks.
This session will be built around the use of AI and NLP, and how Agamon is able to extract actionable insights from radiology reports and close the loop automatically.
We will present real-world data, from several hospitals and health systems across the US, showing how our solution increased the number of returning patients, while reducing workload.
* Identify the magnitude of ‘lost to follow-ups’ through deep learning technology
* Learn how the implementation of an automated system helped bring back patients, increasing care quality and revenue, while decreasing costs and labor dramatically
* See how implementing Agamon’s seamless solution can help gain revenue in a short timespan