Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy

Background: Population-based Health Risk Assessment (HRA) tools are strategic for the implementation of integrated care. Various HRA algorithms have been developed in the last decades worldwide. Their full adoption being limited by technical, functional, and economical factors. This study aims to ap...

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Autores: Papa, Roberta, Balducci, Francesco, Franceschini, Giulia, Pompili, Marco, De Marco, Marco, Roca Torrent, Josep, González Colom, Rubèn, Monterde, David
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
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:2445/216466
Acceso en línea:https://hdl.handle.net/2445/216466
Access Level:acceso abierto
Palabra clave:Malalties cròniques
Morbiditat
Algorismes
Bases de dades
Chronic diseases
Morbidity
Algorithms
Databases
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spelling Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in ItalyPapa, RobertaBalducci, FrancescoFranceschini, GiuliaPompili, MarcoDe Marco, MarcoRoca Torrent, JosepGonzález Colom, RubènMonterde, DavidMalalties cròniquesMorbiditatAlgorismesBases de dadesChronic diseasesMorbidityAlgorithmsDatabasesBackground: Population-based Health Risk Assessment (HRA) tools are strategic for the implementation of integrated care. Various HRA algorithms have been developed in the last decades worldwide. Their full adoption being limited by technical, functional, and economical factors. This study aims to apply the Adjusted Morbidity Groups (AMG) algorithm in the context of an Italian Region, and evaluate its performance to support decision-making processes in healthcare programming. Methods: The pilot study used five Healthcare Administrative Databases (HADs) covering the period 2015-2021. An iterative semi-automated procedure was developed to extract, filter, check and merge the data. A technical manual was developed to describe the process, designed to be standardized, reproducible and transferable. AMG algorithm was applied and descriptive analysis performed. A dashboard structure was developed to exploit the results of the tool. Results: AMG produced information on the health status of Marche citizens, highlighting the presence of chronic conditions from age 45 years. Persons with high and very high level of complexity showed elevated mortality rates and an increased use of healthcare resources. A visualization dashboard was intended to provide to relevant stakeholders accessible, updated and ready-to-use aggregated information on the health status of citizens and additional insight on the use of the healthcare services and resources by specific groups of citizens. Conclusion: The flexibility of the AMG, together with its ability to support policymakers and clinical sector, could favour its implementation in different scenarios across Europe. A clear strategy for the adoption of HRA tools and related key elements and lessons learnt for a successful transferability at the EU level were defined. HRA strategies should be considered a pillar of healthcare policies and programming to achieve person-centred care and promote the sustainability of the EU healthcare systems.BioMed Central2024202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion13 p.application/pdfhttps://hdl.handle.net/2445/216466Articles publicats en revistes (Medicina)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1186/s12889-024-20398-9BMC Public Health, 2024, vol. 24, num.1https://doi.org/10.1186/s12889-024-20398-9cc-by (c) Papa, R. et al., 2024http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2164662026-05-29T05:05:01Z
dc.title.none.fl_str_mv Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy
title Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy
spellingShingle Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy
Papa, Roberta
Malalties cròniques
Morbiditat
Algorismes
Bases de dades
Chronic diseases
Morbidity
Algorithms
Databases
title_short Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy
title_full Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy
title_fullStr Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy
title_full_unstemmed Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy
title_sort Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy
dc.creator.none.fl_str_mv Papa, Roberta
Balducci, Francesco
Franceschini, Giulia
Pompili, Marco
De Marco, Marco
Roca Torrent, Josep
González Colom, Rubèn
Monterde, David
author Papa, Roberta
author_facet Papa, Roberta
Balducci, Francesco
Franceschini, Giulia
Pompili, Marco
De Marco, Marco
Roca Torrent, Josep
González Colom, Rubèn
Monterde, David
author_role author
author2 Balducci, Francesco
Franceschini, Giulia
Pompili, Marco
De Marco, Marco
Roca Torrent, Josep
González Colom, Rubèn
Monterde, David
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Malalties cròniques
Morbiditat
Algorismes
Bases de dades
Chronic diseases
Morbidity
Algorithms
Databases
topic Malalties cròniques
Morbiditat
Algorismes
Bases de dades
Chronic diseases
Morbidity
Algorithms
Databases
description Background: Population-based Health Risk Assessment (HRA) tools are strategic for the implementation of integrated care. Various HRA algorithms have been developed in the last decades worldwide. Their full adoption being limited by technical, functional, and economical factors. This study aims to apply the Adjusted Morbidity Groups (AMG) algorithm in the context of an Italian Region, and evaluate its performance to support decision-making processes in healthcare programming. Methods: The pilot study used five Healthcare Administrative Databases (HADs) covering the period 2015-2021. An iterative semi-automated procedure was developed to extract, filter, check and merge the data. A technical manual was developed to describe the process, designed to be standardized, reproducible and transferable. AMG algorithm was applied and descriptive analysis performed. A dashboard structure was developed to exploit the results of the tool. Results: AMG produced information on the health status of Marche citizens, highlighting the presence of chronic conditions from age 45 years. Persons with high and very high level of complexity showed elevated mortality rates and an increased use of healthcare resources. A visualization dashboard was intended to provide to relevant stakeholders accessible, updated and ready-to-use aggregated information on the health status of citizens and additional insight on the use of the healthcare services and resources by specific groups of citizens. Conclusion: The flexibility of the AMG, together with its ability to support policymakers and clinical sector, could favour its implementation in different scenarios across Europe. A clear strategy for the adoption of HRA tools and related key elements and lessons learnt for a successful transferability at the EU level were defined. HRA strategies should be considered a pillar of healthcare policies and programming to achieve person-centred care and promote the sustainability of the EU healthcare systems.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
2024
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 https://hdl.handle.net/2445/216466
url https://hdl.handle.net/2445/216466
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1186/s12889-024-20398-9
BMC Public Health, 2024, vol. 24, num.1
https://doi.org/10.1186/s12889-024-20398-9
dc.rights.none.fl_str_mv cc-by (c) Papa, R. et al., 2024
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Papa, R. et al., 2024
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13 p.
application/pdf
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv Articles publicats en revistes (Medicina)
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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score 15,812429

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