Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting.

G. Segal; A. Segev; A. Brom; Y. Lifshitz; Y. Wasserstrum; E. Zimlichman

Study

Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting.

August 21, 2019

Segal G, Segev A, Brom A, et al. J Am Med Inform Assoc. 2019;26(12):1560-1565.

View more articles from the same authors.

Alerts designed to prevent inappropriate prescribing of medications are frequently overridden and contribute to alert fatigue. This study describes the use of machine learning to improve the clinical relevance of medication error alerts in the inpatient setting.

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Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting.

Remote Response Team and Customized Alert Settings Help Improve Management of Sepsis

Clinician collaboration to improve clinical decision support: the Clickbusters initiative.

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Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting.

Remote Response Team and Customized Alert Settings Help Improve Management of Sepsis

Clinician collaboration to improve clinical decision support: the Clickbusters initiative.

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