ArticlePloS one2022
Text mining in long-term care: Exploring the usefulness of artificial intelligence in a nursing home setting.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identifying adverse reactions following COVID-19 vaccination in Korea using data from active surveillance: a text mining approach.Epidemiology and health · 2025Article
- Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare.Heliyon · 2024Article
- Article
- Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.PloS one · 2024Article
- Health professionals' sentiments towards implemented information technologies in psychiatric hospitals: a text-mining analysis.BMC health services research · 2022Article
- The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings.SAGE open nursingArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIn 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.
designExploratory study showing a variety of text mining approaches. SETTING AND
participantsData 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).
methodsSeveral 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.
resultsResidents, 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.
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