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

Research output: Contribution to journalArticle

Standard

Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports. / Härkänen, Marja; Paananen, Jussi; Murrells, Trevor; Rafferty, Anne Marie; Franklin, Bryony Dean.

In: BMC Health Services Research, Vol. 19, No. 1, 791, 04.11.2019.

Research output: Contribution to journalArticle

Harvard

Härkänen, M, Paananen, J, Murrells, T, Rafferty, AM & Franklin, BD 2019, 'Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports', BMC Health Services Research, vol. 19, no. 1, 791. https://doi.org/10.1186/s12913-019-4597-9

APA

Härkänen, M., Paananen, J., Murrells, T., Rafferty, A. M., & Franklin, B. D. (2019). Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports. BMC Health Services Research, 19(1), [791]. https://doi.org/10.1186/s12913-019-4597-9

Vancouver

Härkänen M, Paananen J, Murrells T, Rafferty AM, Franklin BD. Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports. BMC Health Services Research. 2019 Nov 4;19(1). 791. https://doi.org/10.1186/s12913-019-4597-9

Author

Härkänen, Marja ; Paananen, Jussi ; Murrells, Trevor ; Rafferty, Anne Marie ; Franklin, Bryony Dean. / Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports. In: BMC Health Services Research. 2019 ; Vol. 19, No. 1.

Bibtex Download

@article{cbd7eb2f54064428af6014b9144098ee,
title = "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. 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{\circledR} 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.",
keywords = "Clustering, Incident reports, Medication administration, Risk, Text mining",
author = "Marja H{\"a}rk{\"a}nen and Jussi Paananen and Trevor Murrells and Rafferty, {Anne Marie} and Franklin, {Bryony Dean}",
year = "2019",
month = "11",
day = "4",
doi = "10.1186/s12913-019-4597-9",
language = "English",
volume = "19",
journal = "BMC Health Services Research",
issn = "1472-6963",
publisher = "BMC Health Services Research",
number = "1",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports

AU - Härkänen, Marja

AU - Paananen, Jussi

AU - Murrells, Trevor

AU - Rafferty, Anne Marie

AU - Franklin, Bryony Dean

PY - 2019/11/4

Y1 - 2019/11/4

N2 - 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.

AB - 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.

KW - Clustering

KW - Incident reports

KW - Medication administration

KW - Risk

KW - Text mining

UR - http://www.scopus.com/inward/record.url?scp=85074546817&partnerID=8YFLogxK

U2 - 10.1186/s12913-019-4597-9

DO - 10.1186/s12913-019-4597-9

M3 - Article

C2 - 31684924

AN - SCOPUS:85074546817

VL - 19

JO - BMC Health Services Research

JF - BMC Health Services Research

SN - 1472-6963

IS - 1

M1 - 791

ER -

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