Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study

Background: The COVID-19 pandemic has turned the care model of health systems around the world upside down, causing the abrupt cancellation of face-to-face visits and redirection of the model toward telemedicine. Digital transformation boosts information systems—the more robust they are, the easier...

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Autores: López Seguí, Francesc, 1991-, Hernandez Guillamet, Guillem, Pifarré i Arolas, Héctor, Marin-Gomez, Francesc X., Ruiz-Comellas, Anna, Ramírez Morros, Anna Maria, Adroher i Mas, Cristina, Vidal Alaball, Josep
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/58389
Acceso en línea:http://hdl.handle.net/10230/58389
http://dx.doi.org/10.2196/29622
Access Level:acceso abierto
Palabra clave:COVID-19
Primary care
Diagnose variations
Big data
ICD10
Health system
Healthcare system
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network_name_str España
repository_id_str
dc.title.none.fl_str_mv Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
title Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
spellingShingle Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
López Seguí, Francesc, 1991-
COVID-19
Primary care
Diagnose variations
Big data
ICD10
Health system
Big data
Primary care
Healthcare system
title_short Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
title_full Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
title_fullStr Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
title_full_unstemmed Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
title_sort Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
dc.creator.none.fl_str_mv López Seguí, Francesc, 1991-
Hernandez Guillamet, Guillem
Pifarré i Arolas, Héctor
Marin-Gomez, Francesc X.
Ruiz-Comellas, Anna
Ramírez Morros, Anna Maria
Adroher i Mas, Cristina
Vidal Alaball, Josep
author López Seguí, Francesc, 1991-
author_facet López Seguí, Francesc, 1991-
Hernandez Guillamet, Guillem
Pifarré i Arolas, Héctor
Marin-Gomez, Francesc X.
Ruiz-Comellas, Anna
Ramírez Morros, Anna Maria
Adroher i Mas, Cristina
Vidal Alaball, Josep
author_role author
author2 Hernandez Guillamet, Guillem
Pifarré i Arolas, Héctor
Marin-Gomez, Francesc X.
Ruiz-Comellas, Anna
Ramírez Morros, Anna Maria
Adroher i Mas, Cristina
Vidal Alaball, Josep
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv COVID-19
Primary care
Diagnose variations
Big data
ICD10
Health system
Big data
Primary care
Healthcare system
topic COVID-19
Primary care
Diagnose variations
Big data
ICD10
Health system
Big data
Primary care
Healthcare system
description Background: The COVID-19 pandemic has turned the care model of health systems around the world upside down, causing the abrupt cancellation of face-to-face visits and redirection of the model toward telemedicine. Digital transformation boosts information systems—the more robust they are, the easier it is to monitor the health care system in a highly complex state and allow for more agile and reliable analysis. Objective: The purpose of this study was to analyze diagnoses from primary care visits and distinguish between those that had higher and lower variations, relative to the 2019 and 2020 periods (roughly before and during COVID-19), to identify clinical profiles that may have been most impaired from the least-used diagnostic codes for visits during the pandemic. Methods: We used a database from the Primary Care Services Information Technologies Information System of Catalonia. We analyzed the register of visits (n=2,824,185) and their International Classification of Diseases (ICD-10) diagnostic codes (n=3,921,974; mean 1.38 per visit), as approximations of the reasons for consultations, at 3 different grouping levels. The data were represented by a term frequency matrix and analyzed recursively in different partitions aggregated according to date. Results: The increase in non–face-to-face visits (+267%) did not counterbalance the decrease in face-to-face visits (–47%), with an overall reduction in the total number of visits of 1.36%, despite the notable increase in nursing visits (10.54%). The largest increases in 2020 were visits with diagnoses related to COVID-19 (ICD-10 codes Z20-Z29: 2.540%), along with codes related to economic and housing problems (ICD-10 codes Z55-Z65: 44.40%). Visits with most of the other diagnostic codes decreased in 2020 relative to those in 2019. The largest reductions were chronic pathologies such as arterial hypertension (ICD-10 codes I10-I16: –32.73%) or diabetes (ICD-10 codes E08-E13: –21.13%), but also obesity (E65-E68: –48.58%) and bodily injuries (ICD-10 code T14: –33.70%). Visits with mental health–related diagnostic codes decreased, but the decrease was less than the average decrease. There was a decrease in consultations—for children, adolescents, and adults—for respiratory infections (ICD-10 codes J00-J06: –40.96%). The results show large year-on-year variations (in absolute terms, an average of 12%), which is representative of the strong shock to the health system. Conclusions: The disruption in the primary care model in Catalonia has led to an explosive increase in the number of non–face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity, and bodily injuries. Instead, visits for diagnoses related to socioeconomic and housing problems have increased, which emphasizes the importance of social determinants of health in the context of this pandemic. Big data analytics with routine care data yield findings that are consistent with those derived from intuition in everyday clinical practice and can help inform decision making by health planners in order to use the next few years to focus on the least-treated diseases during the COVID-19 pandemic.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/58389
http://dx.doi.org/10.2196/29622
url http://hdl.handle.net/10230/58389
http://dx.doi.org/10.2196/29622
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Medical Internet Research. 2021 Sep;23(9):e29622
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv JMIR Publications
publisher.none.fl_str_mv JMIR Publications
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis studyLópez Seguí, Francesc, 1991-Hernandez Guillamet, GuillemPifarré i Arolas, HéctorMarin-Gomez, Francesc X.Ruiz-Comellas, AnnaRamírez Morros, Anna MariaAdroher i Mas, CristinaVidal Alaball, JosepCOVID-19Primary careDiagnose variationsBig dataICD10Health systemBig dataPrimary careHealthcare systemBackground: The COVID-19 pandemic has turned the care model of health systems around the world upside down, causing the abrupt cancellation of face-to-face visits and redirection of the model toward telemedicine. Digital transformation boosts information systems—the more robust they are, the easier it is to monitor the health care system in a highly complex state and allow for more agile and reliable analysis. Objective: The purpose of this study was to analyze diagnoses from primary care visits and distinguish between those that had higher and lower variations, relative to the 2019 and 2020 periods (roughly before and during COVID-19), to identify clinical profiles that may have been most impaired from the least-used diagnostic codes for visits during the pandemic. Methods: We used a database from the Primary Care Services Information Technologies Information System of Catalonia. We analyzed the register of visits (n=2,824,185) and their International Classification of Diseases (ICD-10) diagnostic codes (n=3,921,974; mean 1.38 per visit), as approximations of the reasons for consultations, at 3 different grouping levels. The data were represented by a term frequency matrix and analyzed recursively in different partitions aggregated according to date. Results: The increase in non–face-to-face visits (+267%) did not counterbalance the decrease in face-to-face visits (–47%), with an overall reduction in the total number of visits of 1.36%, despite the notable increase in nursing visits (10.54%). The largest increases in 2020 were visits with diagnoses related to COVID-19 (ICD-10 codes Z20-Z29: 2.540%), along with codes related to economic and housing problems (ICD-10 codes Z55-Z65: 44.40%). Visits with most of the other diagnostic codes decreased in 2020 relative to those in 2019. The largest reductions were chronic pathologies such as arterial hypertension (ICD-10 codes I10-I16: –32.73%) or diabetes (ICD-10 codes E08-E13: –21.13%), but also obesity (E65-E68: –48.58%) and bodily injuries (ICD-10 code T14: –33.70%). Visits with mental health–related diagnostic codes decreased, but the decrease was less than the average decrease. There was a decrease in consultations—for children, adolescents, and adults—for respiratory infections (ICD-10 codes J00-J06: –40.96%). The results show large year-on-year variations (in absolute terms, an average of 12%), which is representative of the strong shock to the health system. Conclusions: The disruption in the primary care model in Catalonia has led to an explosive increase in the number of non–face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity, and bodily injuries. Instead, visits for diagnoses related to socioeconomic and housing problems have increased, which emphasizes the importance of social determinants of health in the context of this pandemic. Big data analytics with routine care data yield findings that are consistent with those derived from intuition in everyday clinical practice and can help inform decision making by health planners in order to use the next few years to focus on the least-treated diseases during the COVID-19 pandemic.JMIR Publications202320232021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/58389http://dx.doi.org/10.2196/29622reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésJournal of Medical Internet Research. 2021 Sep;23(9):e29622© Francesc Lopez Segui, Guillem Hernandez Guillamet, Héctor Pifarré Arolas, Francesc X Marin-Gomez, Anna Ruiz Comellas, Anna Maria Ramirez Morros, Cristina Adroher Mas, Josep Vidal-Alaball. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.09.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/583892026-05-29T05:05:01Z
score 15.812429

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