Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| Outros Autores: | , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/158001 |
Resumo: | Funding Information: We extend a special thanks to all health care professionals for their work and resilience. Publisher Copyright: ©Juan Nicolás Cuenca-Zaldívar, Maria Torrente-Regidor, Laura Martín-Losada, César Fernández-De-Las-Peñas, Lidiane Lima Florencio, Pedro Alexandre Sousa, Domingo Palacios-Ceña. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health RecordsDescriptive Studycontent text analysisCOVID-19electronic health recordspandemicHealth InformaticsHealth Information ManagementFunding Information: We extend a special thanks to all health care professionals for their work and resilience. Publisher Copyright: ©Juan Nicolás Cuenca-Zaldívar, Maria Torrente-Regidor, Laura Martín-Losada, César Fernández-De-Las-Peñas, Lidiane Lima Florencio, Pedro Alexandre Sousa, Domingo Palacios-Ceña.Background: The COVID-19 pandemic has changed the usual working of many hospitalization units (or wards). Few studies have used electronic nursing clinical notes (ENCN) and their unstructured text to identify alterations in patients' feelings and therapeutic procedures of interest. Objective: This study aimed to analyze positive or negative sentiments through inspection of the free text of the ENCN, compare sentiments of ENCN with or without hospitalized patients with COVID-19, carry out temporal analysis of the sentiments of the patients during the start of the first wave of the COVID-19 pandemic, and identify the topics in ENCN. Methods: This is a descriptive study with analysis of the text content of ENCN. All ENCNs between January and June 2020 at Guadarrama Hospital (Madrid, Spain) extracted from the CGM Selene Electronic Health Records System were included. Two groups of ENCNs were analyzed: one from hospitalized patients in post–intensive care units for COVID-19 and a second group from hospitalized patients without COVID-19. A sentiment analysis was performed on the lemmatized text, using the National Research Council of Canada, Affin, and Bing dictionaries. A polarity analysis of the sentences was performed using the Bing dictionary, SO Dictionaries V1.11, and Spa dictionary as amplifiers and decrementators. Machine learning techniques were applied to evaluate the presence of significant differences in the ENCN in groups of patients with and those without COVID-19. Finally, a structural analysis of thematic models was performed to study the abstract topics that occur in the ENCN, using Latent Dirichlet Allocation topic modeling. Results: A total of 37,564 electronic health records were analyzed. Sentiment analysis in ENCN showed that patients with subacute COVID-19 have a higher proportion of positive sentiments than those without COVID-19. Also, there are significant differences in polarity between both groups (Z=5.532, P<.001) with a polarity of 0.108 (SD 0.299) in patients with COVID-19 versus that of 0.09 (SD 0.301) in those without COVID-19. Machine learning modeling reported that despite all models presenting high values, it is the neural network that presents the best indicators (>0.8) and with significant P values between both groups. Through Structural Topic Modeling analysis, the final model containing 10 topics was selected. High correlations were noted among topics 2, 5, and 8 (pressure ulcer and pharmacotherapy treatment), topics 1, 4, 7, and 9 (incidences related to fever and well-being state, and baseline oxygen saturation) and topics 3 and 10 (blood glucose level and pain). Conclusions: The ENCN may help in the development and implementation of more effective programs, which allows patients with COVID-19 to adopt to their prepandemic lifestyle faster. Topic modeling could help identify specific clinical problems in patients and better target the care they receive.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNCuenca-Zaldívar, Juan NicolásTorrente-Regidor, MariaMartín-Losada, LauraFernández-De-Las-Peñas, CésarFlorencio, Lidiane LimaSousa, Pedro AlexandrePalacios-Ceña, Domingo2023-09-19T22:13:21Z2022-052022-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/10362/158001eng2291-9694PURE: 51490209https://doi.org/10.2196/38308info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:40:16Zoai:run.unl.pt:10362/158001Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:56:56.573838Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records Descriptive Study |
| title |
Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records |
| spellingShingle |
Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records Cuenca-Zaldívar, Juan Nicolás content text analysis COVID-19 electronic health records pandemic Health Informatics Health Information Management |
| title_short |
Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records |
| title_full |
Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records |
| title_fullStr |
Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records |
| title_full_unstemmed |
Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records |
| title_sort |
Exploring Sentiment and Care Management of Hospitalized Patients during the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records |
| author |
Cuenca-Zaldívar, Juan Nicolás |
| author_facet |
Cuenca-Zaldívar, Juan Nicolás Torrente-Regidor, Maria Martín-Losada, Laura Fernández-De-Las-Peñas, César Florencio, Lidiane Lima Sousa, Pedro Alexandre Palacios-Ceña, Domingo |
| author_role |
author |
| author2 |
Torrente-Regidor, Maria Martín-Losada, Laura Fernández-De-Las-Peñas, César Florencio, Lidiane Lima Sousa, Pedro Alexandre Palacios-Ceña, Domingo |
| author2_role |
author author author author author author |
| dc.contributor.none.fl_str_mv |
DEE - Departamento de Engenharia Electrotécnica e de Computadores RUN |
| dc.contributor.author.fl_str_mv |
Cuenca-Zaldívar, Juan Nicolás Torrente-Regidor, Maria Martín-Losada, Laura Fernández-De-Las-Peñas, César Florencio, Lidiane Lima Sousa, Pedro Alexandre Palacios-Ceña, Domingo |
| dc.subject.por.fl_str_mv |
content text analysis COVID-19 electronic health records pandemic Health Informatics Health Information Management |
| topic |
content text analysis COVID-19 electronic health records pandemic Health Informatics Health Information Management |
| description |
Funding Information: We extend a special thanks to all health care professionals for their work and resilience. Publisher Copyright: ©Juan Nicolás Cuenca-Zaldívar, Maria Torrente-Regidor, Laura Martín-Losada, César Fernández-De-Las-Peñas, Lidiane Lima Florencio, Pedro Alexandre Sousa, Domingo Palacios-Ceña. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-05 2022-05-01T00:00:00Z 2023-09-19T22:13:21Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/158001 |
| url |
http://hdl.handle.net/10362/158001 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
2291-9694 PURE: 51490209 https://doi.org/10.2196/38308 |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
| dc.format.none.fl_str_mv |
13 application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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