Somani Sulaiman, Kim Dale Daniel, Perez-Guerrero Eduardo, Ngo Summer, Seto Tina, Al-Kindi Sadeer, Hernandez-Boussard Tina, Rodriguez Fatima
Department of Medicine Stanford University Stanford CA USA.
School of Medicine Stanford University Stanford CA USA.
J Am Heart Assoc. 2025 Apr;14(7):e040419. doi: 10.1161/JAHA.124.040419. Epub 2025 Mar 27.
Rates of oral anticoagulation (OAC) nonprescription in atrial fibrillation approach 50%. Understanding reasons for OAC nonprescription may reduce gaps in guideline-recommended care. We aimed to identify reasons for OAC nonprescription from clinical notes using large language models.
We identified all patients and associated clinical notes in our health care system with a clinician-billed visit for atrial fibrillation without another indication for OAC and stratified them on the basis of active OAC prescriptions. Three annotators labeled reasons for OAC nonprescription in clinical notes on 10% of all patients ("annotation set"). We engineered prompts for a generative large language model (Generative Pre-trained Transformer 4) and trained a discriminative large language model (ClinicalBERT) to identify reasons for OAC nonprescription and selected the best-performing model to predict reasons for the remaining 90% of patients ("inference set").
A total of 35 737 patients were identified, of which 7712 (21.6%) did not have active OAC prescriptions. A total of 910 notes across 771 patients were annotated. Generative Pre-trained Transformer 4 outperformed ClinicalBERT (macro-F1 score across all reasons of 0.79, compared with 0.69 for ClinicalBERT). Using Generative Pre-trained Transformer 4 on the inference set, 61.1% of notes had documented reasons for OAC nonprescription, most commonly the alternative use of an antiplatelet agent (23.3%), therapeutic inertia (21.0%), and low burden of atrial fibrillation (17.1%).
This is the first study using large language models to extract documented reasons for OAC nonprescription from clinical notes in patients with atrial fibrillation and reveals guideline-discordant practices and actionable insights for the development of health system interventions to reduce OAC nonprescription.
心房颤动患者中口服抗凝药（OAC）未处方率接近50%。了解OAC未处方的原因可能会减少指南推荐治疗中的差距。我们旨在使用大语言模型从临床记录中识别OAC未处方的原因。
我们在医疗保健系统中识别出所有因心房颤动就诊且无其他OAC适应证的患者及其相关临床记录，并根据OAC的现行处方情况进行分层。三名注释员对所有患者的10%的临床记录标注OAC未处方的原因（“注释集”）。我们为生成式大语言模型（生成式预训练变换器4）设计提示，并训练判别式大语言模型（ClinicalBERT）以识别OAC未处方的原因，然后选择表现最佳的模型来预测其余90%患者的原因（“推理集”）。
共识别出35737例患者，其中7712例（21.6%）没有OAC现行处方。对771例患者的910份记录进行了注释。生成式预训练变换器4的表现优于ClinicalBERT（所有原因的宏观F1分数为0.79，而ClinicalBERT为0.69）。在推理集上使用生成式预训练变换器4，61.1%的记录有OAC未处方的记录原因，最常见的是抗血小板药物的替代使用（23.3%）、治疗惰性（21.0%）和心房颤动负担低（17.1%）。
这是第一项使用大语言模型从心房颤动患者的临床记录中提取OAC未处方的记录原因的研究，揭示了与指南不一致的做法以及对制定卫生系统干预措施以减少OAC未处方的可操作见解。
