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Identifying Health Information Technology Related Safety Event Reports from Patient Safety Event Report Databases.

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Citation: Journal of Biomedical Informatics. 2018 Sep 10PMID: 30213556Institution: MedStar Institute for InnovationDepartment: National Center for Human Factors in HealthcareForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: IN PROCESS -- NOT YET INDEXEDYear: 2018 ISSN:

1532-0464

Name of journal: Journal of biomedical informaticsAbstract: CONCLUSION: The feature-constraint model provides a method to identify HIT-related patient safety hazards using a method that is applicable across healthcare systems with variability in their PSE report structures.Copyright (c) 2018. Published by Elsevier Inc.DISCUSSION: A difference-based scoring, prioritization, and feature selection approach can be used to generate simplified models with high performance. A feature-constraint model may be more easily shared across healthcare organizations seeking to analyze their respective datasets and customized for local variations in PSE reporting practices.METHODS: 5,287 PSE reports manually coded as likely or unlikely related to HIT were used to train unigram, bigram, and combined logistic regression and support vector machine models using five-fold cross validation. A difference-based scoring approach was used to prioritize and select unigram and bigram features by their relative importance to likely and unlikely HIT reports. A held-out set of 2,000 manually coded reports were used for testing.OBJECTIVE: The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model features. A feature-constraint model was developed and evaluated to support the analysis of PSE reports.RESULTS: Unigram models tended to perform better than bigram and combined models. A 300-unigram logistic regression had comparable classification performance to a 4030-unigram SVM model but with a faster relative run-time. The 300-unigram logistic regression model evaluated with the testing data had an AUC of 0.931 and a F1-score of 0.765.All authors: Adams KT, Fong A, Gaunt MJ, Howe JL, Kellogg K, Ratwani RMFiscal year: FY2019Digital Object Identifier:

https://dx.doi.org/10.1016/j.jbi.2018.09.007

Date added to catalog: 2018-09-28

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Title notes ( 7 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Journal Article	MedStar Authors Catalog	Article	30213556	Available		30213556

CONCLUSION: The feature-constraint model provides a method to identify HIT-related patient safety hazards using a method that is applicable across healthcare systems with variability in their PSE report structures.

DISCUSSION: A difference-based scoring, prioritization, and feature selection approach can be used to generate simplified models with high performance. A feature-constraint model may be more easily shared across healthcare organizations seeking to analyze their respective datasets and customized for local variations in PSE reporting practices.

METHODS: 5,287 PSE reports manually coded as likely or unlikely related to HIT were used to train unigram, bigram, and combined logistic regression and support vector machine models using five-fold cross validation. A difference-based scoring approach was used to prioritize and select unigram and bigram features by their relative importance to likely and unlikely HIT reports. A held-out set of 2,000 manually coded reports were used for testing.

OBJECTIVE: The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model features. A feature-constraint model was developed and evaluated to support the analysis of PSE reports.

RESULTS: Unigram models tended to perform better than bigram and combined models. A 300-unigram logistic regression had comparable classification performance to a 4030-unigram SVM model but with a faster relative run-time. The 300-unigram logistic regression model evaluated with the testing data had an AUC of 0.931 and a F1-score of 0.765.

English