Outlier-based detection of unusual patient-management actions: An ICU study.
Outlier-based detection of unusual patient-management actions: An ICU study.
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DOI：
10.1016/j.jbi.2016.10.002
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发表时间：
2016-12
影响因子：
4.5
通讯作者：
Clermont, Gilles
中科院分区：
文献类型：
--
作者：
Hauskrecht, Milos;Batal, Iyad;Hong, Charmgil;Quang Nguyen;Cooper, Gregory F.;Visweswaran, Shyam;Clermont, Gilles
医疗差错仍然是医疗保健领域的一个重大问题。本文研究了一种数据驱动的基于孤立点的监控和警报框架，该框架使用电子病历(EMRS)存储库中的数据来识别当前患者的EMR中的任何异常临床操作。我们的猜测是，这些不寻常的临床行为对应于经常发生的医疗差错，足以证明它们的检测和警报是合理的。我们的方法通过使用电子病历存储库来学习将患者状态与患者管理操作联系起来的统计模型。我们在24,658例重症监护病房(ICU)患者的EMR数据中评估了这种方法。总共有16,500个病例被用来训练统计模型，以便在给定患者状态的情况下订购药物和实验室测试，总结患者的临床病史。这些模型被应用于8,158例ICU患者的单独测试集，并用于生成警报。这些模型产生的240个警报的子集由18名ICU临床医生评估和评估。警报的总体真阳性率(TPAR)从0.44到0.71不等。药物订单警报的TPAR具体范围为0.31至0.61，实验室订单警报的TPAR为0.44至0.75。这些结果支持基于离群值的警报作为一种很有前途的新方法，用于基于过去EMR数据自动生成的数据驱动临床警报。
Medical errors remain a significant problem in healthcare. This paper investigates a data-driven outlier-based monitoring and alerting framework that uses data in the Electronic Medical Records (EMRs) repositories of past patient cases to identify any unusual clinical actions in the EMR of a current patient. Our conjecture is that these unusual clinical actions correspond to medical errors often enough to justify their detection and alerting. Our approach works by using EMR repositories to learn statistical models that relate patient states to patient-management actions. We evaluated this approach on the EMR data for 24,658 intensive care unit (ICU) patient cases. A total of 16,500 cases were used to train statistical models for ordering medications and laboratory tests given the patient state summarizing the patient’s clinical history. The models were applied to a separate test set of 8,158 ICU patient cases and used to generate alerts. A subset of 240 alerts generated by the models were evaluated and assessed by eighteen ICU clinicians. The overall true positive rates for the alerts (TPARs) ranged from 0.44 to 0.71. The TPAR for medication order alerts specifically ranged from 0.31 to 0.61 and for laboratory order alerts from 0.44 to 0.75. These results support outlier-based alerting as a promising new approach to data-driven clinical alerting that is generated automatically based on past EMR data.
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DOI：
10.1197/jamia.m1556
发表时间：
2004-11-01
影响因子：
6.4
作者：
Hsieh, TC;Kuperman, GJ;Gandhi, TK
通讯作者：
Gandhi, TK
影响因子：
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作者：
Rozich, JD;Haraden, CR;Resar, RK
通讯作者：
Resar, RK
影响因子：
120.7
作者：
CLASSEN, DC;PESTOTNIK, SL;BURKE, JP
通讯作者：
BURKE, JP
影响因子：
8.8
作者：
Rothschild, JM;Landrigan, CP;Bates, DW
通讯作者：
Bates, DW
影响因子：
4.5
作者：
Hauskrecht, Milos;Batal, Iyad;Valko, Michal;Visweswaran, Shyam;Cooper, Gregory F.;Clermont, Gilles
通讯作者：
Clermont, Gilles