Griffin Health, a 160-bed acute care hospital in Derby, Connecticut, has made significant strides in enhancing follow-up care for patients flagged through imaging studies. Serving over 130,000 residents in the Lower Naugatuck Valley Region, the hospital faced a familiar healthcare challenge: ensuring patients received timely additional imaging after a radiology report uncovered potential concerns. With nearly half of follow-up imaging across the sector left incomplete, the organisation sought a solution that would not only identify at-risk patients but also help clinicians act swiftly and effectively. By embedding an artificial intelligence platform into existing care navigation workflows, Griffin Health has achieved substantial gains in patient safety and clinical outcomes.
Bridging the Follow-Up Gap
Like many health systems, Griffin Health previously relied on manual procedures to manage the transition from initial radiology findings to diagnostic resolution. Incidental findings, such as lung nodules, often required additional imaging to confirm or rule out disease, but the hospital struggled with fragmented systems and inconsistent communication. Manual notifications to ordering providers existed, yet there was no reliable infrastructure to ensure follow-up studies were scheduled, completed or interpreted in a timely manner. Staff could not always confirm whether a patient attended their recommended appointment, creating a safety risk that could no longer be tolerated.
The limitations were clear. With care navigators already working at capacity, there was no scalable way to manage the volume of follow-ups. Without a centralised method to track each case from detection to diagnosis, the hospital faced delays in care that could impact patient outcomes. Griffin Health needed a solution that would coordinate tasks across departments, guide patients through the process and ensure that no case was left unresolved.
Integrating AI into Clinical Practice
To address this challenge, Griffin Health adopted a digital platform capable of leveraging AI to identify radiology reports that recommended further imaging. The system was integrated into the hospital’s existing clinical infrastructure, designed to support rather than replace current processes. It began by analysing imaging reports in real time, scanning for language or patterns that suggested a potential issue requiring follow-up. Once detected, each case was assigned to a relevant care pathway based on clinical need and urgency.
The platform did more than alert clinicians. It automated the outreach required to ensure follow-up imaging orders were entered into the electronic health record. It tracked whether appointments had been scheduled and completed, providing visibility into each step of the process. For care coordinators, this meant gaining access to a central dashboard where the status of each patient could be monitored. From the initial finding to the final result, every action and outcome could now be followed in one place.
This approach allowed Griffin Health to shift from a reactive model to a proactive one. Instead of relying on staff to chase updates or identify missed steps, the system orchestrated workflows that guided both patients and providers. Barriers to follow-up could be detected early, and patients could be contacted directly when necessary. The ability to manage multiple cases simultaneously, while maintaining clarity on each one, greatly enhanced the hospital’s capacity to deliver safe, timely care.
Tangible Gains in Patient Outcomes
The implementation of AI yielded measurable improvements. The closure rate for incidental findings rose by 50%, indicating that more patients completed the care process from detection to diagnosis. This reduction in unresolved cases meant fewer individuals were left without necessary follow-up, significantly lowering clinical risk. The follow-up completion rate also increased by 17%, reflecting a higher number of patients who attended their scheduled imaging studies.
Importantly, the system also helped identify individuals eligible for lung cancer screening. Eighteen patients were enrolled in the hospital’s screening programme based on the AI platform’s analysis. These patients might not have been captured through standard processes, highlighting the potential of technology to uncover opportunities for early intervention. For those affected, the impact could mean earlier diagnosis and better treatment outcomes.
Through this transformation, Griffin Health demonstrated how a digital system could reinforce human-led care. By automating routine tasks and ensuring information was available at the right time, the hospital empowered its clinical teams to act decisively. Staff no longer had to rely on fragmented records or memory to follow up on critical findings. Instead, they could depend on a coordinated infrastructure that tracked progress and kept the loop closed.
Must Read: Digital Health and Sustainability: A New Era for Cancer Screening
Griffin Health’s use of AI within care navigation illustrates the potential of technology to solve entrenched problems in healthcare delivery. By focusing on the specific issue of follow-up imaging after radiology findings, the hospital improved both operational efficiency and patient safety. The platform was not implemented as a standalone tool, but as part of a carefully integrated approach that complemented existing workflows and supported the efforts of clinicians and care coordinators.
The result is a more reliable and responsive system that ensures patients do not fall through the cracks. With improvements in closure rates, follow-up compliance and cancer screening enrolment, Griffin Health’s experience underscores the value of embedding AI into routine clinical practice. Rather than adding complexity, the technology has helped simplify care processes, allowing staff to focus on what matters most: delivering timely, accurate and life-saving care.
Source: Healthcare IT News
Image Credit: iStock
