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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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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