A custom large language model significantly improved the identification of patients needing follow-up imaging by analyzing radiologists’ notes.
Key Details
- 1A new LLM-based tool was developed at Parkland Health to flag patients who require follow-up imaging.
- 2Traditional EHR macros and structured notes failed to efficiently capture needed follow-up recommendations.
- 3The AI model reads clinical impressions from radiologist notes to extract and standardize follow-up indications.
- 4More than 500,000 radiology studies are performed annually at the health system, emphasizing the scale and need for automation.
- 5Integration into the EHR enables real-time flagging and streamlined workflow for ensuring follow-up imaging.
Why It Matters
Missed follow-ups are a key source of diagnostic errors in radiology. This LLM-driven tool helps close communication gaps, ensuring at-risk patients receive necessary imaging, which could improve outcomes and reduce diagnostic delays.
Source
Radiology Business
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