Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10400.22/22714 |
Resumo: | Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011–2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems. |
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Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in PortugalData qualityPublic health informaticsMedical records: evaluationHealth information managementQuantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011–2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.SAGE JournalsRepositório Científico do Instituto Politécnico do PortoPortela, Diana Amaral, RitaRodrigues, Pedro P. Freitas, Alberto Costa, Elísio Fonseca, João A. Sousa-Pinto, Bernardo 2023-04-12T15:18:14Z2023-02-172023-02-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/22714engPortela D, Amaral R, Rodrigues PP, et al. Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal. Health Information Management Journal. 2023;0(0). doi:10.1177/183335832211446631833-358310.1177/183335832211446631833-3575info: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:RCAAP2023-04-19T01:47:00Zoai:recipp.ipp.pt:10400.22/22714Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:49:41.549033Repositório 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 |
Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal |
title |
Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal |
spellingShingle |
Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal Portela, Diana Data quality Public health informatics Medical records: evaluation Health information management |
title_short |
Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal |
title_full |
Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal |
title_fullStr |
Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal |
title_full_unstemmed |
Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal |
title_sort |
Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal |
author |
Portela, Diana |
author_facet |
Portela, Diana Amaral, Rita Rodrigues, Pedro P. Freitas, Alberto Costa, Elísio Fonseca, João A. Sousa-Pinto, Bernardo |
author_role |
author |
author2 |
Amaral, Rita Rodrigues, Pedro P. Freitas, Alberto Costa, Elísio Fonseca, João A. Sousa-Pinto, Bernardo |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Portela, Diana Amaral, Rita Rodrigues, Pedro P. Freitas, Alberto Costa, Elísio Fonseca, João A. Sousa-Pinto, Bernardo |
dc.subject.por.fl_str_mv |
Data quality Public health informatics Medical records: evaluation Health information management |
topic |
Data quality Public health informatics Medical records: evaluation Health information management |
description |
Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011–2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04-12T15:18:14Z 2023-02-17 2023-02-17T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/22714 |
url |
http://hdl.handle.net/10400.22/22714 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Portela D, Amaral R, Rodrigues PP, et al. Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal. Health Information Management Journal. 2023;0(0). doi:10.1177/18333583221144663 1833-3583 10.1177/18333583221144663 1833-3575 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
SAGE Journals |
publisher.none.fl_str_mv |
SAGE Journals |
dc.source.none.fl_str_mv |
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) |
collection |
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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