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How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach

BACKGROUND: Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hospital-independent solution to indicate points for...

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Autores principales: Cammel, Simone A., De Vos, Marit S., van Soest, Daphne, Hettne, Kristina M., Boer, Fred, Steyerberg, Ewout W., Boosman, Hileen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251822/
https://www.ncbi.nlm.nih.gov/pubmed/32460734
http://dx.doi.org/10.1186/s12911-020-1104-5
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author Cammel, Simone A.
De Vos, Marit S.
van Soest, Daphne
Hettne, Kristina M.
Boer, Fred
Steyerberg, Ewout W.
Boosman, Hileen
author_facet Cammel, Simone A.
De Vos, Marit S.
van Soest, Daphne
Hettne, Kristina M.
Boer, Fred
Steyerberg, Ewout W.
Boosman, Hileen
author_sort Cammel, Simone A.
collection PubMed
description BACKGROUND: Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hospital-independent solution to indicate points for quality improvement. METHODS: This retrospective study used routinely collected patient experience data from two hospitals. A data-driven NLP approach was used. Free-text responses were categorized into topics, subtopics (i.e. n-grams) and labelled with a sentiment score. The indicator ‘impact’, combining sentiment and frequency, was calculated to reveal topics to improve, monitor or celebrate. The topic modelling architecture was tested on data from a second hospital to examine whether the architecture is transferable to another hospital. RESULTS: A total of 38,664 survey responses from the first hospital resulted in 127 topics and 294 n-grams. The indicator ‘impact’ revealed n-grams to celebrate (15.3%), improve (8.8%), and monitor (16.7%). For hospital 2, a similar percentage of free-text responses could be labelled with a topic and n-grams. Between-hospitals, most topics (69.7%) were similar, but 32.2% of topics for hospital 1 and 29.0% of topics for hospital 2 were unique. CONCLUSIONS: In both hospitals, NLP techniques could be used to categorize patient experience free-text responses into topics, sentiment labels and to define priorities for improvement. The model’s architecture was shown to be hospital-specific as it was able to discover new topics for the second hospital. These methods should be considered for future patient experience analyses to make better use of this valuable source of information.
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spelling pubmed-72518222020-06-07 How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach Cammel, Simone A. De Vos, Marit S. van Soest, Daphne Hettne, Kristina M. Boer, Fred Steyerberg, Ewout W. Boosman, Hileen BMC Med Inform Decis Mak Research Article BACKGROUND: Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hospital-independent solution to indicate points for quality improvement. METHODS: This retrospective study used routinely collected patient experience data from two hospitals. A data-driven NLP approach was used. Free-text responses were categorized into topics, subtopics (i.e. n-grams) and labelled with a sentiment score. The indicator ‘impact’, combining sentiment and frequency, was calculated to reveal topics to improve, monitor or celebrate. The topic modelling architecture was tested on data from a second hospital to examine whether the architecture is transferable to another hospital. RESULTS: A total of 38,664 survey responses from the first hospital resulted in 127 topics and 294 n-grams. The indicator ‘impact’ revealed n-grams to celebrate (15.3%), improve (8.8%), and monitor (16.7%). For hospital 2, a similar percentage of free-text responses could be labelled with a topic and n-grams. Between-hospitals, most topics (69.7%) were similar, but 32.2% of topics for hospital 1 and 29.0% of topics for hospital 2 were unique. CONCLUSIONS: In both hospitals, NLP techniques could be used to categorize patient experience free-text responses into topics, sentiment labels and to define priorities for improvement. The model’s architecture was shown to be hospital-specific as it was able to discover new topics for the second hospital. These methods should be considered for future patient experience analyses to make better use of this valuable source of information. BioMed Central 2020-05-27 /pmc/articles/PMC7251822/ /pubmed/32460734 http://dx.doi.org/10.1186/s12911-020-1104-5 Text en © The Author(s). 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Cammel, Simone A.
De Vos, Marit S.
van Soest, Daphne
Hettne, Kristina M.
Boer, Fred
Steyerberg, Ewout W.
Boosman, Hileen
How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach
title How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach
title_full How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach
title_fullStr How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach
title_full_unstemmed How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach
title_short How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach
title_sort how to automatically turn patient experience free-text responses into actionable insights: a natural language programming (nlp) approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251822/
https://www.ncbi.nlm.nih.gov/pubmed/32460734
http://dx.doi.org/10.1186/s12911-020-1104-5
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