Importance: We introduce a novel Retrieval Augmented Generation (RAG)-Large
Language Model (LLM) as a Clinical Decision Support System (CDSS) for safe
medication prescription. This model addresses the limitations of traditional
rule-based CDSS by providing relevant prescribing error alerts tailored to
patient context and institutional guidelines.
Objective: The study evaluates the efficacy of an LLM-based CDSS in
identifying medication errors across various medical and surgical case
vignettes, compared to a human expert panel. It also examines clinician
preferences among different CDSS integration modalities: junior pharmacist,
LLM-based CDSS alone, and a combination of both.
Design, Setting, and Participants: Utilizing a RAG model with GPT-4.0, the
study involved 61 prescribing error scenarios within 23 clinical vignettes
across 12 specialties. An expert panel assessed these cases using the PCNE
classification and NCC MERP index. Three junior pharmacists independently
reviewed each vignette under simulated conditions.
Main Outcomes and Measures: The study assesses the LLM-based CDSS's accuracy,
precision, recall, and F1 scores in identifying Drug-Related Problems (DRPs),
compared to junior pharmacists alone or in an assistive mode with the CDSS.
Results: The co-pilot mode of RAG-LLM significantly improved DRP
identification accuracy by 22% over solo pharmacists. It showed higher recall
and F1 scores, indicating better detection of severe DRPs, despite a slight
decrease in precision. Accuracy varied across categories when pharmacists had
access to RAG-LLM responses.
Conclusions: The RAG-LLM based CDSS enhances medication error identification
accuracy when used with junior pharmacists, especially in detecting severe
DRPs.

我们介绍了一种基于检索增强生成（RAG）- 大型语言模型（LLM）的临床决策支持系统（CDSS），用于安全药物处方。该模型通过提供与患者背景和机构指南相适应的相关处方错误警报来解决传统基于规则的 CDSS 的局限性。本研究评估了基于 LLM 的 CDSS 在识别各种医疗和外科病例中的药物错误方面的有效性，与人工专家小组进行对比。同时，研究还检验了临床医生对不同 CDSS 集成模式的偏好：初级药师、仅基于 LLM 的 CDSS 和两者的组合。

基于新型大型语言模型的临床决策支持系统在 12 个临床专科中的药物安全发展和测试

Development and Testing of a Novel Large Language Model-Based Clinical  Decision Support Systems for Medication Safety in 12 Clinical Specialties

Medication errors most commonly occur at the ordering or prescribing stage,
potentially leading to medical complications and poor health outcomes. While it
is possible to catch these errors using different techniques; the focus of this
work is on textual and contextual analysis of prescription information to
detect and prevent potential medication errors. In this paper, we demonstrate
how to use BERT-based contextual language models to detect anomalies in written
or spoken text based on a data set extracted from real-world medical data of
thousands of patient records. The proposed models are able to learn patterns of
text dependency and predict erroneous output based on contextual information
such as patient data. The experimental results yield accuracy up to 96.63% for
text input and up to 79.55% for speech input, which is satisfactory for most
real-world applications.

本文介绍如何使用基于 BERT 的语言模型来检测药物处方信息中的异常情况，通过数据集中的真实医疗数据，训练模型以学习文本依赖模式，并基于上下文信息预测药物处方的错误输出，实验结果表明模型的准确率可达 96.63%。

使用上下文语言模型进行药物误用检测

Medication Error Detection Using Contextual Language Models

Identifying prescription medications is a frequent task for patients and
medical professionals; however, this is an error-prone task as many pills have
similar appearances (e.g. white round pills), which increases the risk of
medication errors. In this paper, we introduce ePillID, the largest public
benchmark on pill image recognition, composed of 13k images representing 9804
appearance classes (two sides for 4902 pill types). For most of the appearance
classes, there exists only one reference image, making it a challenging
low-shot recognition setting. We present our experimental setup and evaluation
results of various baseline models on the benchmark. The best baseline using a
multi-head metric-learning approach with bilinear features performed remarkably
well; however, our error analysis suggests that they still fail to distinguish
particularly confusing classes. The code and data are available at
this https URL

本论文介绍了 ePillID-- 最大的公共药丸图像识别基准，用于减少相似药丸种类的误用，通过低拍摄速率识别，提高基于度量学习方法的多头模型的精度。