Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia

This research presents a methodology for classification, forecasting and prediction of healthcare providers accredited in Colombia. For this purpose, a quantitative, descriptive and predictive analysis was carried out of 27 institutions accredited in Colombia by 2016. Consequently, the machine learn...

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Autores:: Fontalvo Herrera, Tomás José
De la Hoz Domínguez, Enrique José
Fontalvo, Orianna

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Tecnológica de Bolívar

Repositorio:: Repositorio Institucional UTB

Idioma:: eng

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dc.title.spa.fl_str_mv	Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia
title	Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia
spellingShingle	Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia Cluster-analysis Neural networks Quality Business profiles Healthcare LEMB
title_short	Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia
title_full	Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia
title_fullStr	Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia
title_full_unstemmed	Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia
title_sort	Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia
dc.creator.fl_str_mv	Fontalvo Herrera, Tomás José De la Hoz Domínguez, Enrique José Fontalvo, Orianna
dc.contributor.author.none.fl_str_mv	Fontalvo Herrera, Tomás José De la Hoz Domínguez, Enrique José Fontalvo, Orianna
dc.subject.keywords.spa.fl_str_mv	Cluster-analysis Neural networks Quality Business profiles Healthcare
topic	Cluster-analysis Neural networks Quality Business profiles Healthcare LEMB
dc.subject.armarc.none.fl_str_mv	LEMB
description	This research presents a methodology for classification, forecasting and prediction of healthcare providers accredited in Colombia. For this purpose, a quantitative, descriptive and predictive analysis was carried out of 27 institutions accredited in Colombia by 2016. Consequently, the machine learning techniques cluster analysis and artificial neural networks were used to define business profiles of the institutions under study. The method classifying, forecasting and predicting the membership of a healthcare provider to a business profile, previously created based on the high-quality patterns of accreditation. The input variables were assets, account receivable, inventory, property and equipment and the output variables health service sales and net profit. The cluster analysis defined two main groups. 1) accredited institutions in the process of financial consolidation; 2) accredited institutions financially sound. The process of forecasting and prediction through the creation of an artificial neural network yielded a 95% CI (088, 0.9975) precision in the classification, and 100% and 80% for sensitivity and specificity values respectively. The results evidence the capacity of the proposed methodology to recognise the characteristics and association patterns of HCP accredited in high quality.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-08-02T18:08:39Z
dc.date.available.none.fl_str_mv	2021-08-02T18:08:39Z
dc.date.issued.none.fl_str_mv	2021-05-11
dc.date.submitted.none.fl_str_mv	2021-07-30
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dc.identifier.citation.spa.fl_str_mv	Fontalvo-Herrera, T., Delahoz-Dominguez, E. and Fontalvo, O. (2021) ‘Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia’, Int. J. Productivity and Quality Management, Vol. 33, No. 1, pp.1–20.
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12585/10351
dc.identifier.doi.none.fl_str_mv	https://doi.org/10.1504/IJPQM.2021.115290
dc.identifier.instname.spa.fl_str_mv	Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv	Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv	Fontalvo-Herrera, T., Delahoz-Dominguez, E. and Fontalvo, O. (2021) ‘Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia’, Int. J. Productivity and Quality Management, Vol. 33, No. 1, pp.1–20. Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar
url	https://hdl.handle.net/20.500.12585/10351 https://doi.org/10.1504/IJPQM.2021.115290
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.format.extent.none.fl_str_mv	20 páginas
dc.format.medium.none.fl_str_mv	PDF
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dc.coverage.spatial.none.fl_str_mv	Colombia
dc.coverage.temporal.none.fl_str_mv	2021
dc.publisher.place.spa.fl_str_mv	Cartagena de Indias
dc.source.spa.fl_str_mv	Productivity and Quality Management, Vol. 33, No. 1, 2021
institution	Universidad Tecnológica de Bolívar
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spelling	Fontalvo Herrera, Tomás José43b145e4-93ff-42d3-b854-8fd98d59921dDe la Hoz Domínguez, Enrique José5e7439af-add5-49e6-a503-b3ab802bc74dFontalvo, Orianna99663300-d82c-4d7f-b771-ba22d3c2ce85Colombia20212021-08-02T18:08:39Z2021-08-02T18:08:39Z2021-05-112021-07-30Fontalvo-Herrera, T., Delahoz-Dominguez, E. and Fontalvo, O. (2021) ‘Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia’, Int. J. Productivity and Quality Management, Vol. 33, No. 1, pp.1–20.https://hdl.handle.net/20.500.12585/10351https://doi.org/10.1504/IJPQM.2021.115290Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis research presents a methodology for classification, forecasting and prediction of healthcare providers accredited in Colombia. For this purpose, a quantitative, descriptive and predictive analysis was carried out of 27 institutions accredited in Colombia by 2016. Consequently, the machine learning techniques cluster analysis and artificial neural networks were used to define business profiles of the institutions under study. The method classifying, forecasting and predicting the membership of a healthcare provider to a business profile, previously created based on the high-quality patterns of accreditation. The input variables were assets, account receivable, inventory, property and equipment and the output variables health service sales and net profit. The cluster analysis defined two main groups. 1) accredited institutions in the process of financial consolidation; 2) accredited institutions financially sound. The process of forecasting and prediction through the creation of an artificial neural network yielded a 95% CI (088, 0.9975) precision in the classification, and 100% and 80% for sensitivity and specificity values respectively. The results evidence the capacity of the proposed methodology to recognise the characteristics and association patterns of HCP accredited in high quality.20 páginasPDFapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Productivity and Quality Management, Vol. 33, No. 1, 2021Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombiainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Cluster-analysisNeural networksQualityBusiness profilesHealthcareLEMBCartagena de IndiasPúblico generalAlmost, J.M., Van Den Kerkhof, E.G., Strahlendorf, P., Caicco Tett, L., Noonan, J., Hayes, T., Van Hulle, H., Adam, R., Holden, J., Kent-Hillis, T., McDonald, M., Paré, G.C., Lachhar, K. and Silva e Silva, V. (2018) ‘A study of leading indicators for occupational health and safety management systems in healthcare’, BMC Health Serv. Res., Vol. 18, p.296, DOI: https://doi.org/10.1186/s12913-018-3103-0.Alolayyan, M.N., Ali, K.A.M. and Idris, F. (2013) ‘Total quality management and operational flexibility impact on hospitals performance: a structural modelling approach’, Int. J. Product. Qual. Manag., Vol. 11, No. 2, pp.212–227.Ansari, A. and Riasi, A. (2016) ‘Modelling and evaluating customer loyalty using neural networks: evidence from startup insurance companies’, Future Bus. J., Vol. 2, No. 1, pp.15–30.Askim, J., Christensen, T. and Lægreid, P. (2015) ‘Accountability and performance management: the Norwegian hospital, welfare, and immigration administration’, Int. J. Public Adm., Vol. 38, pp.971–982, DOI: https://doi.org/10.1080/01900692.2015.1069840.Carlucci, D., Renna, P. and Schiuma, G. (2013) ‘Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network’, Health Care Manag. Sci., Vol. 16, No. 1, pp.37–44.Chamboko, R. and Bravo, J.M. (2018) ‘Modelling and forecasting recurrent recovery events on consumer loans’, Int. J. Appl. Decis. Sci., Vol. 12, No. 3, pp.271–287, DOI: 10.1504/ IJADS.2019.100440.Chamboko, R. and Bravo, J.M. (2018) ‘Modelling and forecasting recurrent recovery events on consumer loans’, Int. J. Appl. Decis. Sci., Vol. 12, No. 3, pp.271–287, DOI: 10.1504/ IJADS.2019.100440.Chojaczyk, A.A., Teixeira, A.P., Neves, L.C., Cardoso, J.B. and Guedes Soares, C. (2015) ‘Review and application of artificial neural networks models in reliability analysis of steel structures’, Struct. Saf., Vol. 52, pp.78–89, DOI: https://doi.org/10.1016/j.strusafe.2014.09.002.Cong, Z., Fernandez, A., Billhardt, H. and Lujak, M. (2015) ‘Service discovery acceleration with hierarchical clustering’, Inf. Syst. 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Sci., Vol. 7, pp.38, DOI: https://doi.org/10.1504/IJAMS.2015.068056.Fontalvo Herrera, T.J., Mendoza Mendoza, A.A., Cadavid, V. and Delimiro, A. (2016) ‘Evaluación del comportamiento de los indicadores de productividad y rentabilidad en las empresas prestadores de salud del Régimen Contributivo en Colombia’, Rev. Salud Uninorte, Vol. 32, No. 3, pp.419–428.Fontalvo, T., De La Hoz, E. and De La Hoz, E. (2018) ‘Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies [Método análisis envolvente de datos y redes neuronales en la evaluación y predicción de la eficiencia técnica de pequeñas empresas exportadoras]’, Inf. Tecnol., Vol. 29, pp.267–276 [online] https://doi.org/10.4067/S0718-07642018000600267.Fontalvo-Herrera, T.J., Delahoz, E.J. and Mendoza-Mendoza, A.A. 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Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia

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