Text mining in long-term care: Exploring the usefulness of artificial intelligence in a nursing home setting

Coen Hacking; Hilde Verbeek; Jan P H Hamers; Katya Sion; Sil Aarts

doi:10.1371/journal.pone.0268281

Text mining in long-term care: Exploring the usefulness of artificial intelligence in a nursing home setting

Coen Hacking^*, Hilde Verbeek, Jan P H Hamers, Katya Sion, Sil Aarts

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

OBJECTIVES: In nursing homes, narrative data are collected to evaluate quality of care as perceived by residents or their family members. This results in a large amount of textual data. However, as the volume of data increases, it becomes beyond the capability of humans to analyze it. This study aims to explore the usefulness of text mining approaches regarding narrative data gathered in a nursing home setting.

DESIGN: Exploratory study showing a variety of text mining approaches.

SETTING AND PARTICIPANTS: Data has been collected as part of the project 'Connecting Conversations': assessing experienced quality of care by conducting individual interviews with residents of nursing homes (n = 39), family members (n = 37) and care professionals (n = 49).

METHODS: Several pre-processing steps were applied. A variety of text mining analyses were conducted: individual word frequencies, bigram frequencies, a correlation analysis and a sentiment analysis. A survey was conducted to establish a sentiment analysis model tailored to text collected in long-term care for older adults.

RESULTS: Residents, family members and care professionals uttered respectively 285, 362 and 549 words per interview. Word frequency analysis showed that words that occurred most frequently in the interviews are often positive. Despite some differences in word usage, correlation analysis displayed that similar words are used by all three groups to describe quality of care. Most interviews displayed a neutral sentiment. Care professionals expressed a more diverse sentiment compared to residents and family members. A topic clustering analysis showed a total of 12 topics including 'relations' and 'care environment'.

CONCLUSIONS AND IMPLICATIONS: This study demonstrates the usefulness of text mining to extend our knowledge regarding quality of care in a nursing home setting. With the rise of textual (narrative) data, text mining can lead to valuable new insights for long-term care for older adults.

Original language	English
Article number	e0268281
Number of pages	17
Journal	PLOS ONE
Volume	17
Issue number	8
DOIs	https://doi.org/10.1371/journal.pone.0268281
Publication status	Published - 25 Aug 2022

Keywords

Aged
Artificial Intelligence
Data Mining
Family
Humans
Long-Term Care
Nursing Homes

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10.1371/journal.pone.0268281Licence: CC BY

Cite this

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title = "Text mining in long-term care: Exploring the usefulness of artificial intelligence in a nursing home setting",

abstract = "OBJECTIVES: In nursing homes, narrative data are collected to evaluate quality of care as perceived by residents or their family members. This results in a large amount of textual data. However, as the volume of data increases, it becomes beyond the capability of humans to analyze it. This study aims to explore the usefulness of text mining approaches regarding narrative data gathered in a nursing home setting.DESIGN: Exploratory study showing a variety of text mining approaches.SETTING AND PARTICIPANTS: Data has been collected as part of the project 'Connecting Conversations': assessing experienced quality of care by conducting individual interviews with residents of nursing homes (n = 39), family members (n = 37) and care professionals (n = 49).METHODS: Several pre-processing steps were applied. A variety of text mining analyses were conducted: individual word frequencies, bigram frequencies, a correlation analysis and a sentiment analysis. A survey was conducted to establish a sentiment analysis model tailored to text collected in long-term care for older adults.RESULTS: Residents, family members and care professionals uttered respectively 285, 362 and 549 words per interview. Word frequency analysis showed that words that occurred most frequently in the interviews are often positive. Despite some differences in word usage, correlation analysis displayed that similar words are used by all three groups to describe quality of care. Most interviews displayed a neutral sentiment. Care professionals expressed a more diverse sentiment compared to residents and family members. A topic clustering analysis showed a total of 12 topics including 'relations' and 'care environment'.CONCLUSIONS AND IMPLICATIONS: This study demonstrates the usefulness of text mining to extend our knowledge regarding quality of care in a nursing home setting. With the rise of textual (narrative) data, text mining can lead to valuable new insights for long-term care for older adults.",

keywords = "Aged, Artificial Intelligence, Data Mining, Family, Humans, Long-Term Care, Nursing Homes",

author = "Coen Hacking and Hilde Verbeek and Hamers, {Jan P H} and Katya Sion and Sil Aarts",

note = "Publisher Copyright: {\textcopyright} 2022 Hacking et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the originalauthor and source are credited.",

year = "2022",

month = aug,

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doi = "10.1371/journal.pone.0268281",

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T1 - Text mining in long-term care

T2 - Exploring the usefulness of artificial intelligence in a nursing home setting

AU - Hacking, Coen

AU - Verbeek, Hilde

AU - Hamers, Jan P H

AU - Sion, Katya

AU - Aarts, Sil

N1 - Publisher Copyright: © 2022 Hacking et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the originalauthor and source are credited.

PY - 2022/8/25

Y1 - 2022/8/25

N2 - OBJECTIVES: In nursing homes, narrative data are collected to evaluate quality of care as perceived by residents or their family members. This results in a large amount of textual data. However, as the volume of data increases, it becomes beyond the capability of humans to analyze it. This study aims to explore the usefulness of text mining approaches regarding narrative data gathered in a nursing home setting.DESIGN: Exploratory study showing a variety of text mining approaches.SETTING AND PARTICIPANTS: Data has been collected as part of the project 'Connecting Conversations': assessing experienced quality of care by conducting individual interviews with residents of nursing homes (n = 39), family members (n = 37) and care professionals (n = 49).METHODS: Several pre-processing steps were applied. A variety of text mining analyses were conducted: individual word frequencies, bigram frequencies, a correlation analysis and a sentiment analysis. A survey was conducted to establish a sentiment analysis model tailored to text collected in long-term care for older adults.RESULTS: Residents, family members and care professionals uttered respectively 285, 362 and 549 words per interview. Word frequency analysis showed that words that occurred most frequently in the interviews are often positive. Despite some differences in word usage, correlation analysis displayed that similar words are used by all three groups to describe quality of care. Most interviews displayed a neutral sentiment. Care professionals expressed a more diverse sentiment compared to residents and family members. A topic clustering analysis showed a total of 12 topics including 'relations' and 'care environment'.CONCLUSIONS AND IMPLICATIONS: This study demonstrates the usefulness of text mining to extend our knowledge regarding quality of care in a nursing home setting. With the rise of textual (narrative) data, text mining can lead to valuable new insights for long-term care for older adults.

AB - OBJECTIVES: In nursing homes, narrative data are collected to evaluate quality of care as perceived by residents or their family members. This results in a large amount of textual data. However, as the volume of data increases, it becomes beyond the capability of humans to analyze it. This study aims to explore the usefulness of text mining approaches regarding narrative data gathered in a nursing home setting.DESIGN: Exploratory study showing a variety of text mining approaches.SETTING AND PARTICIPANTS: Data has been collected as part of the project 'Connecting Conversations': assessing experienced quality of care by conducting individual interviews with residents of nursing homes (n = 39), family members (n = 37) and care professionals (n = 49).METHODS: Several pre-processing steps were applied. A variety of text mining analyses were conducted: individual word frequencies, bigram frequencies, a correlation analysis and a sentiment analysis. A survey was conducted to establish a sentiment analysis model tailored to text collected in long-term care for older adults.RESULTS: Residents, family members and care professionals uttered respectively 285, 362 and 549 words per interview. Word frequency analysis showed that words that occurred most frequently in the interviews are often positive. Despite some differences in word usage, correlation analysis displayed that similar words are used by all three groups to describe quality of care. Most interviews displayed a neutral sentiment. Care professionals expressed a more diverse sentiment compared to residents and family members. A topic clustering analysis showed a total of 12 topics including 'relations' and 'care environment'.CONCLUSIONS AND IMPLICATIONS: This study demonstrates the usefulness of text mining to extend our knowledge regarding quality of care in a nursing home setting. With the rise of textual (narrative) data, text mining can lead to valuable new insights for long-term care for older adults.

KW - Aged

KW - Artificial Intelligence

KW - Data Mining

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KW - Humans

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KW - Nursing Homes

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