用大模型辅助生成诊断线索，自动恢复电子病历缺失数据。
On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records
- 用大模型迭代优化临床诊断线索清单，替代人工制定
- 在1000名患者数据上，恢复效果媲美专家审阅
- 适合大规模医疗数据清洗，提升数据质量监控效率
电子健康记录（EHR）数据常存在缺失和错误。此前我们设计了一种‘增强版’病历审查流程，通过辅助诊断（锚点）来推断缺失值（例如，糖化血红蛋白缺失可能因血糖控制不良而异常）。但病历审查成本高、耗时长，限制了可审查患者数量。本文研究基于ICD-10编码的路线图驱动算法的准确性和可扩展性，以模拟专家审查。在一家大型学习型医疗系统中，对100名患者的病历进行审查，评估不同路线图下的算法表现；随后在1000名患者的更大样本中应用最终算法，该算法结合了临床专家认可的大语言模型生成的新线索。结果显示，该算法在不同路线图下恢复的缺失数据量与专家审查相当甚至更多。结论表明，经大模型增强的临床驱动算法可达到与专家审查相似的准确性，并可规模化应用于大规模数据集。未来可扩展至监测数据合理性等其他维度。
原文摘要 · Abstract (English)
Objective: Electronic health records (EHR) data are prone to missingness and errors. Previously, we devised an "enriched" chart review protocol where a "roadmap" of auxiliary diagnoses (anchors) was used to recover missing values in EHR data (e.g., a diagnosis of impaired glycemic control might imply that a missing hemoglobin A1c value would be considered unhealthy). Still, chart reviews are expensive and time-intensive, which limits the number of patients whose data can be reviewed. Now, we investigate the accuracy and scalability of a roadmap-driven algorithm, based on ICD-10 codes (International Classification of Diseases, 10th revision), to mimic expert chart reviews and recover missing values. Materials and Methods: In addition to the clinicians' original roadmap from our previous work, we consider new versions that were iteratively refined using large language models (LLM) in conjunction with clinical expertise to expand the list of auxiliary diagnoses. Using chart reviews for 100 patients from the EHR at an extensive learning health system, we examine algorithm performance with different roadmaps. Using the larger study of $1000$ patients, we applied the final algorithm, which used a roadmap with clinician-approved additions from the LLM. Results: The algorithm recovered as much, if not more, missing data as the expert chart reviewers, depending on the roadmap. Discussion: Clinically-driven algorithms (enhanced by LLM) can recover missing EHR data with similar accuracy to chart reviews and can feasibly be applied to large samples. Extending them to monitor other dimensions of data quality (e.g., plausability) is a promising future direction.
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