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Consultation analysis: use of free text versus coded text

General practice in the United Kingdom has been using electronic health records for over two decades, but coding clinical information remains poor. Lack of interest and training are considerable barriers preventing code use levels improvement. Tailored training could be the way forward, to break bar...

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Autor principal: Millares Martin, Pablo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829039/
https://www.ncbi.nlm.nih.gov/pubmed/33520588
http://dx.doi.org/10.1007/s12553-020-00517-3
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author Millares Martin, Pablo
author_facet Millares Martin, Pablo
author_sort Millares Martin, Pablo
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description General practice in the United Kingdom has been using electronic health records for over two decades, but coding clinical information remains poor. Lack of interest and training are considerable barriers preventing code use levels improvement. Tailored training could be the way forward, to break barriers in the uptake of coding; to do so it is paramount to understand coding use of the particular clinicians, to recognise their needs. It should be possible to easily assess text quantity and quality in medical consultations. A tool to measure these parameters, which could be used to tailor training needs and assess change, is demonstrated. The tool is presented and a preliminary study using a randomised sample of five recent consultations from thirteen different clinicians is used as an example. The tool, based on using a word processor and a spread-sheet, allowed quantitative analysis among clinicians while word clouds permitted a qualitative comparison between coded and free text. The average amount of free text per consultation was 68.2 words, (ranging from 25.4 and 130.2 among clinicians); an average of 6% of the text was coded (ranging from 0 to 13%). Patterns among clinicians could be identified. Using Word cloud, a different text use was demonstrated depending on its purpose. Some free text could be turned into code but nomenclature probably prevented some of the codings, like the expression of time. This proof of concept demonstrated that it is possible to calculate what percentage of consultations are coded and what codes are used. This allowed understanding clinicians’ preferences; training needs and gaps in nomenclature.
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spelling pubmed-78290392021-01-25 Consultation analysis: use of free text versus coded text Millares Martin, Pablo Health Technol (Berl) Original Paper General practice in the United Kingdom has been using electronic health records for over two decades, but coding clinical information remains poor. Lack of interest and training are considerable barriers preventing code use levels improvement. Tailored training could be the way forward, to break barriers in the uptake of coding; to do so it is paramount to understand coding use of the particular clinicians, to recognise their needs. It should be possible to easily assess text quantity and quality in medical consultations. A tool to measure these parameters, which could be used to tailor training needs and assess change, is demonstrated. The tool is presented and a preliminary study using a randomised sample of five recent consultations from thirteen different clinicians is used as an example. The tool, based on using a word processor and a spread-sheet, allowed quantitative analysis among clinicians while word clouds permitted a qualitative comparison between coded and free text. The average amount of free text per consultation was 68.2 words, (ranging from 25.4 and 130.2 among clinicians); an average of 6% of the text was coded (ranging from 0 to 13%). Patterns among clinicians could be identified. Using Word cloud, a different text use was demonstrated depending on its purpose. Some free text could be turned into code but nomenclature probably prevented some of the codings, like the expression of time. This proof of concept demonstrated that it is possible to calculate what percentage of consultations are coded and what codes are used. This allowed understanding clinicians’ preferences; training needs and gaps in nomenclature. Springer Berlin Heidelberg 2021-01-24 2021 /pmc/articles/PMC7829039/ /pubmed/33520588 http://dx.doi.org/10.1007/s12553-020-00517-3 Text en © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Millares Martin, Pablo
Consultation analysis: use of free text versus coded text
title Consultation analysis: use of free text versus coded text
title_full Consultation analysis: use of free text versus coded text
title_fullStr Consultation analysis: use of free text versus coded text
title_full_unstemmed Consultation analysis: use of free text versus coded text
title_short Consultation analysis: use of free text versus coded text
title_sort consultation analysis: use of free text versus coded text
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829039/
https://www.ncbi.nlm.nih.gov/pubmed/33520588
http://dx.doi.org/10.1007/s12553-020-00517-3
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