Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports

Abstract Background Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analys...

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Main Authors: Marja Härkänen, Jussi Paananen, Trevor Murrells, Anne Marie Rafferty, Bryony Dean Franklin
Format: Article
Language:English
Published: BMC 2019-11-01
Series:BMC Health Services Research
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12913-019-4597-9
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spelling doaj-78c9e1193ccc469794f91e1edd53a23a2020-11-25T04:05:58ZengBMCBMC Health Services Research1472-69632019-11-011911910.1186/s12913-019-4597-9Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reportsMarja Härkänen0Jussi Paananen1Trevor Murrells2Anne Marie Rafferty3Bryony Dean Franklin4Department of Nursing Science, University of Eastern FinlandInstitute of Biomedicine, University of Eastern FinlandFlorence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King’s College LondonFlorence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King’s College LondonCentre for Medication Safety and Service Quality, Imperial College Healthcare NHS Trust, Charing Cross HospitalAbstract Background Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analysing only the names of the medications implicated in a specific unstructured medication field does not give information of the associated factors and risk areas, but when analysing unstructured free text descriptions, the information about the medication involved and associated risk factors may be buried within other non-relevant text. Thus, the aim of this study was to extract medication names most commonly used in free text descriptions of medication administration incident reports to identify terms most frequently associated with risk for each of these medications using text mining. Method Free text descriptions of medication administration incidents (n = 72,390) reported in 2016 to the National Reporting and Learning System for England and Wales were analysed using SAS® Text miner. Analysis included text parsing and filtering free text to identify most commonly mentioned medications, followed by concept linking, and clustering to identify terms associated with commonly mentioned medications and the associated risk areas. Results The following risk areas related to medications were identified: 1. Allergic reactions to antibacterial drugs, 2. Intravenous administration of antibacterial drugs, 3. Fentanyl patches, 4. Checking and documenting of analgesic doses, 5. Checking doses of anticoagulants, 6. Insulin doses and blood glucose, 7. Administration of intravenous infusions. Conclusions Interventions to increase medication administration safety should focus on checking patient allergies and medication doses, especially for intravenous and transdermal medications. High-risk medications include insulin, analgesics, antibacterial drugs, anticoagulants, and potassium chloride. Text mining may be useful for analysing large free text datasets and should be developed further.http://link.springer.com/article/10.1186/s12913-019-4597-9Incident reportsMedication administrationText miningClusteringRisk
collection DOAJ
language English
format Article
sources DOAJ
author Marja Härkänen
Jussi Paananen
Trevor Murrells
Anne Marie Rafferty
Bryony Dean Franklin
spellingShingle Marja Härkänen
Jussi Paananen
Trevor Murrells
Anne Marie Rafferty
Bryony Dean Franklin
Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports
BMC Health Services Research
Incident reports
Medication administration
Text mining
Clustering
Risk
author_facet Marja Härkänen
Jussi Paananen
Trevor Murrells
Anne Marie Rafferty
Bryony Dean Franklin
author_sort Marja Härkänen
title Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports
title_short Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports
title_full Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports
title_fullStr Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports
title_full_unstemmed Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports
title_sort identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports
publisher BMC
series BMC Health Services Research
issn 1472-6963
publishDate 2019-11-01
description Abstract Background Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analysing only the names of the medications implicated in a specific unstructured medication field does not give information of the associated factors and risk areas, but when analysing unstructured free text descriptions, the information about the medication involved and associated risk factors may be buried within other non-relevant text. Thus, the aim of this study was to extract medication names most commonly used in free text descriptions of medication administration incident reports to identify terms most frequently associated with risk for each of these medications using text mining. Method Free text descriptions of medication administration incidents (n = 72,390) reported in 2016 to the National Reporting and Learning System for England and Wales were analysed using SAS® Text miner. Analysis included text parsing and filtering free text to identify most commonly mentioned medications, followed by concept linking, and clustering to identify terms associated with commonly mentioned medications and the associated risk areas. Results The following risk areas related to medications were identified: 1. Allergic reactions to antibacterial drugs, 2. Intravenous administration of antibacterial drugs, 3. Fentanyl patches, 4. Checking and documenting of analgesic doses, 5. Checking doses of anticoagulants, 6. Insulin doses and blood glucose, 7. Administration of intravenous infusions. Conclusions Interventions to increase medication administration safety should focus on checking patient allergies and medication doses, especially for intravenous and transdermal medications. High-risk medications include insulin, analgesics, antibacterial drugs, anticoagulants, and potassium chloride. Text mining may be useful for analysing large free text datasets and should be developed further.
topic Incident reports
Medication administration
Text mining
Clustering
Risk
url http://link.springer.com/article/10.1186/s12913-019-4597-9
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