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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0254358 |
| _version_ | 1811185864444739584 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T13:37:38Z |
| format | Article |
| id | doaj.art-6e8d139f7ba04454a0bbe4ad17b15801 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T13:37:38Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6e8d139f7ba04454a0bbe4ad17b158012022-12-22T04:21:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01167e025435810.1371/journal.pone.0254358Predicting self-intercepted medication ordering errors using machine learning.Christopher Ryan KingJoanna AbrahamBradley A FritzZhicheng CuiWilliam GalanterYixin ChenThomas KannampallilCurrent 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.https://doi.org/10.1371/journal.pone.0254358 |
| spellingShingle | 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. PLoS ONE |
| title | 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_short | Predicting self-intercepted medication ordering errors using machine learning. |
| title_sort | predicting self intercepted medication ordering errors using machine learning |
| url | https://doi.org/10.1371/journal.pone.0254358 |
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