A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.

Jennifer Corny; Asok Rajkumar; Olivier Martin; Xavier Dode; Jean-Patrick Lajonchère; Olivier Billuart; Yvonnick Bézie; Anne Buronfosse

Study

Emerging Classic

A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.

October 21, 2020

Corny J, Rajkumar A, Martin O, et al. J Am Med Inform Assoc. 2020;27(11):1695–1704.

View more articles from the same authors.

Machine learning can improve the accuracy of clinical decision support (CDS) tools. This single-site study used data from the electronic health record (EHR) and clinical pharmacist review to test the accuracy of a hybrid CDS system to identify prescriptions with high risk of medication error. The machine-learning based approach was more accurate than existing techniques such as the traditional CDS system and can improve the reliability of prescription checks in an inpatient setting.

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Specific to High-Risk Drugs

A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.

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

eSIMPLER: a dynamic, electronic health record-integrated checklist for clinical decision support during PICU daily rounds.

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

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A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.

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

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

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