Predicting self-intercepted medication ordering errors using machine learning.

Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medicatio...

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Autores principales:	Christopher Ryan King, Joanna Abraham, Bradley A Fritz, Zhicheng Cui, William Galanter, Yixin Chen, Thomas Kannampallil
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6e8d139f7ba04454a0bbe4ad17b15801
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id	oai:doaj.org-article:6e8d139f7ba04454a0bbe4ad17b15801
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spelling	oai:doaj.org-article:6e8d139f7ba04454a0bbe4ad17b158012021-12-02T20:07:00ZPredicting self-intercepted medication ordering errors using machine learning.1932-620310.1371/journal.pone.0254358https://doaj.org/article/6e8d139f7ba04454a0bbe4ad17b158012021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254358https://doaj.org/toc/1932-6203Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, we described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. However, these traditional techniques require expert guidance and may perform poorly compared to newer approaches. In this paper, we update that analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors. We retrieved patient demographics (race/ethnicity, sex, age), clinician characteristics, type of medication order (inpatient, prescription, home medication by history), and order content. We compared logistic regression, random forest, boosted decision trees, and artificial neural network models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The dataset included 5,804,192 medication orders, of which 28,695 (0.5%) were voided. ML correctly classified voids at reasonable accuracy; with a positive predictive value of 10%, ~20% of errors were included. Gradient boosted decision trees achieved the highest AUROC (0.7968) and AUPRC (0.0647) among all models. Logistic regression had the poorest performance. Models identified predictive factors with high face validity (e.g., student orders), and a decision tree revealed interacting contexts with high rates of errors not identified by previous regression models. Prediction models using order-entry information offers promise for error surveillance, patient safety improvements, and targeted clinical review. The improved performance of models with complex interactions points to the importance of contextual medication ordering information for understanding contributors to medication errors.Christopher Ryan KingJoanna AbrahamBradley A FritzZhicheng CuiWilliam GalanterYixin ChenThomas KannampallilPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254358 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christopher Ryan King Joanna Abraham Bradley A Fritz Zhicheng Cui William Galanter Yixin Chen Thomas Kannampallil Predicting self-intercepted medication ordering errors using machine learning.
description	Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, we described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. However, these traditional techniques require expert guidance and may perform poorly compared to newer approaches. In this paper, we update that analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors. We retrieved patient demographics (race/ethnicity, sex, age), clinician characteristics, type of medication order (inpatient, prescription, home medication by history), and order content. We compared logistic regression, random forest, boosted decision trees, and artificial neural network models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The dataset included 5,804,192 medication orders, of which 28,695 (0.5%) were voided. ML correctly classified voids at reasonable accuracy; with a positive predictive value of 10%, ~20% of errors were included. Gradient boosted decision trees achieved the highest AUROC (0.7968) and AUPRC (0.0647) among all models. Logistic regression had the poorest performance. Models identified predictive factors with high face validity (e.g., student orders), and a decision tree revealed interacting contexts with high rates of errors not identified by previous regression models. Prediction models using order-entry information offers promise for error surveillance, patient safety improvements, and targeted clinical review. The improved performance of models with complex interactions points to the importance of contextual medication ordering information for understanding contributors to medication errors.
format	article
author	Christopher Ryan King Joanna Abraham Bradley A Fritz Zhicheng Cui William Galanter Yixin Chen Thomas Kannampallil
author_facet	Christopher Ryan King Joanna Abraham Bradley A Fritz Zhicheng Cui William Galanter Yixin Chen Thomas Kannampallil
author_sort	Christopher Ryan King
title	Predicting self-intercepted medication ordering errors using machine learning.
title_short	Predicting self-intercepted medication ordering errors using machine learning.
title_full	Predicting self-intercepted medication ordering errors using machine learning.
title_fullStr	Predicting self-intercepted medication ordering errors using machine learning.
title_full_unstemmed	Predicting self-intercepted medication ordering errors using machine learning.
title_sort	predicting self-intercepted medication ordering errors using machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6e8d139f7ba04454a0bbe4ad17b15801
work_keys_str_mv	AT christopherryanking predictingselfinterceptedmedicationorderingerrorsusingmachinelearning AT joannaabraham predictingselfinterceptedmedicationorderingerrorsusingmachinelearning AT bradleyafritz predictingselfinterceptedmedicationorderingerrorsusingmachinelearning AT zhichengcui predictingselfinterceptedmedicationorderingerrorsusingmachinelearning AT williamgalanter predictingselfinterceptedmedicationorderingerrorsusingmachinelearning AT yixinchen predictingselfinterceptedmedicationorderingerrorsusingmachinelearning AT thomaskannampallil predictingselfinterceptedmedicationorderingerrorsusingmachinelearning
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Predicting self-intercepted medication ordering errors using machine learning.

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