Abstract

Objective

To improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks.

Materials and Methods

Data from electronic health records were collated over a period of 18 months. Inferred scores at a patient level (probability of a patient’s set of active orders to require a pharmacist review) were calculated using a hybrid approach (machine learning and a rule-based expert system). A clinical pharmacist analyzed randomly selected prescription orders over a 2-week period to corroborate our findings. Predicted scores were compared with the pharmacist’s review using the area under the receiving-operating characteristic curve and area under the precision-recall curve. These metrics were compared with existing tools: computerized alerts generated by a clinical decision support (CDS) system and a literature-based multicriteria query prioritization technique. Data from 10 716 individual patients (133 179 prescription orders) were used to train the algorithm on the basis of 25 features in a development dataset.

Results

While the pharmacist analyzed 412 individual patients (3364 prescription orders) in an independent validation dataset, the areas under the receiving-operating characteristic and precision-recall curves of our digital system were 0.81 and 0.75, respectively, thus demonstrating greater accuracy than the CDS system (0.65 and 0.56, respectively) and multicriteria query techniques (0.68 and 0.56, respectively).

Discussion

Our innovative digital tool was notably more accurate than existing techniques (CDS system and multicriteria query) at intercepting potential prescription errors.

Conclusions

By primarily targeting high-risk patients, this novel hybrid decision support system improved the accuracy and reliability of prescription checks in a hospital setting.

中文翻译：

---

基于机器学习的临床决策支持系统，用于识别药物错误风险高的处方。

摘要

目的

为了通过减少开处方错误的风险来提高患者安全性和临床结果，我们在确定处方检查优先级时测试了混合临床决策支持系统的准确性。

材料和方法

电子健康记录中的数据经过18个月的整理。使用混合方法（机器学习和基于规则的专家系统）来计算患者级别的推断得分（患者的一组有效订单需要药剂师审核的概率）。一位临床药剂师在2周的时间内对随机选择的处方药订单进行了分析，以证实我们的发现。使用接收操作特征曲线下的面积和精确召回曲线下的面积，将预测的分数与药剂师的评论进行比较。这些指标与现有工具进行了比较：由临床决策支持（CDS）系统生成的计算机警报和基于文献的多标准查询优先排序技术。

结果

药剂师在一个独立的验证数据集中分析了412名患者（3364个处方订单），而我们的数字系统的接收操作特征曲线和精确召回曲线下的面积分别为0.81和0.75，因此比CDS系统具有更高的准确性（分别为0.65和0.56）和多准则查询技术（分别为0.68和0.56）。

讨论区

我们的创新数字工具在拦截潜在处方错误方面比现有技术（CDS系统和多标准查询）更加准确。

结论

通过主要针对高风险患者，这种新颖的混合决策支持系统提高了医院环境中处方检查的准确性和可靠性。