定制开源LLM用于跨异构EHR系统的定量药物属性提取
Customizing Open Source LLMs for Quantitative Medication Attribute Extraction across Heterogeneous EHR Systems
AI总结：
该研究提出一个定制开源LLM的框架，从异构EHR系统中提取统一的MOUD处方属性并计算标准化覆盖指标，在五诊所数据上实现高准确率，支持隐私保护的跨站点分析。
摘要：
协调电子健康记录（EHR）系统中的药物数据是监测阿片类药物使用障碍（MOUD）药物的持续障碍。在异构EHR系统中，关键的处方属性分散在不同格式的字段和自由文本注释中。我们提出了一个实用框架，定制开源大型语言模型（LLM），包括Llama、Qwen、Gemma和MedGemma，从异构的、特定站点的数据中提取统一的MOUD处方属性集（处方日期、药物名称、持续时间、总数量、每日数量和续药次数），并计算每位患者的标准化药物覆盖指标——MOUD天数。我们的流程直接以固定的JSON模式处理记录，随后进行轻量级归一化和跨字段一致性检查。我们使用先前标注的10,369条记录（776名患者）基准作为金标准，在来自全国OUD研究中五个诊所的处方级EHR数据（来自1,257名患者的25,605条记录）上评估该系统。性能以覆盖率（具有有效、可匹配输出的记录比例）和记录级精确匹配准确率报告。较大的模型整体表现最佳：Qwen2.5-32B在诊所间达到93.4%的覆盖率和93.0%的精确匹配准确率，MedGemma-27B达到93.1%/92.2%。简要的错误审查突出了三个常见问题和修复方法：使用药物内规范填补缺失的剂量字段，通过设定文档化时间表的持续时间来处理每月/每周注射剂（如Vivitrol），以及添加单位检查以防止质量单位（如“250 g”）被误读为每日计数。通过移除脆弱的、特定站点的ETL并支持本地、隐私保护的部署，该方法能够在真实环境中实现MOUD暴露、依从性和保留的一致跨站点分析。
英文摘要：
Harmonizing medication data across Electronic Health Record (EHR) systems is a persistent barrier to monitoring medications for opioid use disorder (MOUD). In heterogeneous EHR systems, key prescription attributes are scattered across differently formatted fields and freetext notes. We present a practical framework that customizes open source large language models (LLMs), including Llama, Qwen, Gemma, and MedGemma, to extract a unified set of MOUD prescription attributes (prescription date, drug name, duration, total quantity, daily quantity, and refills) from heterogeneous, site specific data and compute a standardized metric of medication coverage, \emph{MOUD days}, per patient. Our pipeline processes records directly in a fixed JSON schema, followed by lightweight normalization and cross-field consistency checks. We evaluate the system on prescription level EHR data from five clinics in a national OUD study (25{,}605 records from 1{,}257 patients), using a previously annotated benchmark of 10{,}369 records (776 patients) as the ground truth. Performance is reported as coverage (share of records with a valid, matchable output) and record-level exact-match accuracy. Larger models perform best overall: Qwen2.5-32B achieves \textbf{93.4\%} coverage with \textbf{93.0\%} exact-match accuracy across clinics, and MedGemma-27B attains \textbf{93.1\%}/\textbf{92.2\%}. A brief error review highlights three common issues and fixes: imputing missing dosage fields using within-drug norms, handling monthly/weekly injectables (e.g., Vivitrol) by setting duration from the documented schedule, and adding unit checks to prevent mass units (e.g., ``250 g'') from being misread as daily counts. By removing brittle, site-specific ETL and supporting local, privacy-preserving deployment, this approach enables consistent cross-site analyses of MOUD exposure, adherence, and retention in real-world settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
