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Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events.

MedStar author(s):

Citation: International Journal of Medical Informatics. 104:120-125, 2017 AugPMID: 28529113Institution: MedStar Institute for InnovationForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Drug-Related Side Effects and Adverse Reactions/pc [Prevention & Control] | *Medication Errors/pc [Prevention & Control] | *Natural Language Processing | *Patient Safety | *Pharmaceutical Preparations | Advisory Committees | Data Interpretation, Statistical | Humans | Risk ManagementYear: 2017 ISSN:

1386-5056

Name of journal: International journal of medical informaticsAbstract: CONCLUSIONS: We demonstrate the capabilities of various NLP models and the use of two text inclusion strategies at categorizing medication related patient safety events. The NLP models and visualization could be used to improve the efficiency of patient safety event data review and analysis.Copyright � 2017 Elsevier B.V. All rights reserved.METHODS: Natural language processing (NLP) experts collaborated with clinical experts on a patient safety committee to assist in the identification and analysis of medication related patient safety events. Different NLP algorithmic approaches were developed to identify four types of medication related patient safety events and the models were compared.OBJECTIVES: Many healthcare providers have implemented patient safety event reporting systems to better understand and improve patient safety. Reviewing and analyzing these reports is often time consuming and resource intensive because of both the quantity of reports and length of free-text descriptions in the reports.RESULTS: Well performing NLP models were generated to categorize medication related events into pharmacy delivery delays, dispensing errors, Pyxis discrepancies, and prescriber errors with receiver operating characteristic areas under the curve of 0.96, 0.87, 0.96, and 0.81 respectively. We also found that modeling the brief without the resolution text generally improved model performance. These models were integrated into a dashboard visualization to support the patient safety committee review process.All authors: Foley H, Fong A, Harriott N, Morrissey R, Ratwani RR, Walters DMFiscal year: FY2017Digital Object Identifier:

https://dx.doi.org/10.1016/j.ijmedinf.2017.05.005

Date added to catalog: 2017-05-26

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Title notes ( 6 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Journal Article	MedStar Authors Catalog	Article	28529113	Available		28529113

CONCLUSIONS: We demonstrate the capabilities of various NLP models and the use of two text inclusion strategies at categorizing medication related patient safety events. The NLP models and visualization could be used to improve the efficiency of patient safety event data review and analysis.

METHODS: Natural language processing (NLP) experts collaborated with clinical experts on a patient safety committee to assist in the identification and analysis of medication related patient safety events. Different NLP algorithmic approaches were developed to identify four types of medication related patient safety events and the models were compared.

OBJECTIVES: Many healthcare providers have implemented patient safety event reporting systems to better understand and improve patient safety. Reviewing and analyzing these reports is often time consuming and resource intensive because of both the quantity of reports and length of free-text descriptions in the reports.

RESULTS: Well performing NLP models were generated to categorize medication related events into pharmacy delivery delays, dispensing errors, Pyxis discrepancies, and prescriber errors with receiver operating characteristic areas under the curve of 0.96, 0.87, 0.96, and 0.81 respectively. We also found that modeling the brief without the resolution text generally improved model performance. These models were integrated into a dashboard visualization to support the patient safety committee review process.

English