Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events
Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events
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DOI：
10.1016/j.ijmedinf.2017.05.005
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发表时间：
2017-08-01
影响因子：
4.9
通讯作者：
Ratwani, Raj R.
中科院分区：
文献类型：
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作者：
Fong, Allan;Harriott, Nicole;Ratwani, Raj R.
目的：许多医疗保健提供者已经实施了患者安全事件报告系统，以更好地了解和改善患者安全。审查和分析这些报告往往是耗时和资源密集型的，因为这两个报告的数量和长度的自由文本的描述在reports.Methods：自然语言处理（NLP）专家与临床专家合作的病人安全委员会，以协助识别和分析药物相关的病人安全事件。开发了不同的NLP算法方法来识别四种类型的药物相关患者安全事件，并对模型进行了比较。生成了表现良好的NLP模型，以将药物相关事件分类为药房递送延迟、分发错误、Pyxis差异和处方者错误，其中曲线下的接收者操作特征面积为0.96、0.87、0.96、0.98、0.99、0.9分别为0.81。我们还发现，在没有解决方案文本的情况下对简报进行建模通常会提高模型性能。这些模型被集成到一个仪表板的可视化，以支持病人安全委员会的审查process.Conclusions：我们展示了各种NLP模型的能力和使用两个文本包含策略在药物相关的病人安全事件分类。NLP模型和可视化可以用于提高患者安全事件数据审查和分析的效率。
Objectives: Many healthcare providers have implemented patient safety event reporting systems to better understand and improve patient safety. Reviewing and analyzing these reports is often time consuming and resource intensive because of both the quantity of reports and length of free-text descriptions in the reports.Methods: Natural language processing (NLP) experts collaborated with clinical experts on a patient safety committee to assist in the identification and analysis of medication related patient safety events. Different NLP algorithmic approaches were developed to identify four types of medication related patient safety events and the models were compared.Results: Well performing NLP models were generated to categorize medication related events into pharmacy delivery delays, dispensing errors, Pyxis discrepancies, and prescriber errors with receiver operating characteristic areas under the curve of 0.96, 0.87, 0.96, and 0.81 respectively. We also found that modeling the brief without the resolution text generally improved model performance. These models were integrated into a dashboard visualization to support the patient safety committee review process.Conclusions: We demonstrate the capabilities of various NLP models and the use of two text inclusion strategies at categorizing medication related patient safety events. The NLP models and visualization could be used to improve the efficiency of patient safety event data review and analysis.