AI-Assisted Chart Review to Understand Disease Flares in Systemic Lupus Erythematosus

OBJECTIVES: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease affecting multiple organs and systems. Accurately determining the frequency and pattern of disease flares, as measured by SLE Disease Activity Index (SLEDAI), is crucial for SLE care and research. However, flare reports often reside in free-text clinical notes, lacking standardized coding and necessitating manual review. To overcome this, we utilized an advanced Artificial Intelligence (AI) model to identify SLE flares from clinical notes.

METHODS: We obtained de-identified medical records between 1/1/2012 and 05/31/2022 from Temple University Health System’s Epic electronic health record system. The dataset included patients aged 18 years or above, with at least one rheumatology visit, one ICD-10-CM code of M32.*, and SLE mentioned in a clinical note. We developed a multi-step automated pipeline using GPT-4 and prompt engineering to accurately identify flares based on(1) SLEDAI score changes (A >3 change in SLEDAI mentioned in the physician notes is considered a mild-moderate flare while a >10 change in SLEDA is considered a severe flare), (2) physician's reports in the notes, or (3) our of SLEDAI changes according to new symptoms and lab results documented in physician notes.

RESULTS: From 2,085 rheumatology notes of 408 unique patients, GPT-4 identified 606 flare events. Of these, 7 were based on A review of 200 randomly selected notes by trained medical professionals showed a 97.5% agreement with GPT-4 results.

CONCLUSIONS: Our findings affirm that advanced AI technology can significantly enhance chart review efficiency while maintaining high accuracy in identifying and characterizing SLE flares, even in cases of missed flare diagnosis. Large language models, represented by GPT-4.0 show promise in accelerating SLE research using electronic health records data.