Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports

In the health sector, the reports on delivery of prescriptions and the assignment of medical appointments are generated by the Health Service Provider Institutions and delivered to the Health Service Promoting Entities. These reports usually have an incoherent structure; inconsistencies in the forma...

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Autores:
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
eng
OAI Identifier:
oai:repositorio.uptc.edu.co:001/14378
Acceso en línea:
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314
https://repositorio.uptc.edu.co/handle/001/14378
Palabra clave:
Data quality
Data quality categories
Drug delivery
Medical appointment scheduling
Conformance
Completeness
Plausibility
Health regulatory reporting
Calidad de datos
Categorías de calidad de datos
Entrega de Medicamentos
Asignación de citas médicas
Conformidad
Completitud
Plausibilidad
Salud
Reportes normativos en salud
Rights
License
Copyright (c) 2023 Daisy-Yisel Meneses-Lopez, Martha-Eliana Mendoza-Becerra, Salvador Garcia-Lopez
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dc.title.en-US.fl_str_mv Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports
dc.title.es-ES.fl_str_mv Adaptación de las categorías de calidad de datos de Kahn para reportes de entrega de medicamentos y asignación de citas médicas
title Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports
spellingShingle Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports
Data quality
Data quality categories
Drug delivery
Medical appointment scheduling
Conformance
Completeness
Plausibility
Health regulatory reporting
Calidad de datos
Categorías de calidad de datos
Entrega de Medicamentos
Asignación de citas médicas
Conformidad
Completitud
Plausibilidad
Salud
Reportes normativos en salud
title_short Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports
title_full Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports
title_fullStr Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports
title_full_unstemmed Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports
title_sort Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports
dc.subject.en-US.fl_str_mv Data quality
Data quality categories
Drug delivery
Medical appointment scheduling
Conformance
Completeness
Plausibility
Health regulatory reporting
topic Data quality
Data quality categories
Drug delivery
Medical appointment scheduling
Conformance
Completeness
Plausibility
Health regulatory reporting
Calidad de datos
Categorías de calidad de datos
Entrega de Medicamentos
Asignación de citas médicas
Conformidad
Completitud
Plausibilidad
Salud
Reportes normativos en salud
dc.subject.es-ES.fl_str_mv Calidad de datos
Categorías de calidad de datos
Entrega de Medicamentos
Asignación de citas médicas
Conformidad
Completitud
Plausibilidad
Salud
Reportes normativos en salud
description In the health sector, the reports on delivery of prescriptions and the assignment of medical appointments are generated by the Health Service Provider Institutions and delivered to the Health Service Promoting Entities. These reports usually have an incoherent structure; inconsistencies in the format; non-existent, incomplete, or non-standardized data. These problems affect data quality and hinder the reliability of the information. To address this, it is proposed to adapt Kahn's data quality categories, to these reports, considering that the health sector accepts them categories and contemplates not only the structure and domain of the data but also its completeness and plausibility (credibility). This research followed the methodology of Pratt’s Iterative Research Pattern, studies related to the subject were observed, and the attributes of prescription delivery and appointment assignment were analyzed to understand the problem and its implications in detail. We then adapted the data quality categories proposed by Kahn, taking into account the problems identified in these reports. Subsequently, a group of health experts evaluated the proposed adaptation using the focus group technique. The results, according to their perception, showed that the prescription delivery report obtained 66.7% in the “Completely Agree” category and 33.3% in the “Agree” category; medical appointment assignment had 73.3% in “Completely Agree” and 26.7% in “Agree”, according to the Likert scale. In conclusion, this research contributes to strengthening the data quality of these reports by providing guidelines to improve the reliability of the information.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2024-07-05T19:12:11Z
dc.date.available.none.fl_str_mv 2024-07-05T19:12:11Z
dc.date.none.fl_str_mv 2023-09-30
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a307
status_str publishedVersion
dc.identifier.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314
dc.identifier.uri.none.fl_str_mv https://repositorio.uptc.edu.co/handle/001/14378
url https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314
https://repositorio.uptc.edu.co/handle/001/14378
dc.language.none.fl_str_mv eng
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314/13528
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314/13813
dc.rights.en-US.fl_str_mv Copyright (c) 2023 Daisy-Yisel Meneses-Lopez, Martha-Eliana Mendoza-Becerra, Salvador Garcia-Lopez
http://creativecommons.org/licenses/by/4.0
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf224
rights_invalid_str_mv Copyright (c) 2023 Daisy-Yisel Meneses-Lopez, Martha-Eliana Mendoza-Becerra, Salvador Garcia-Lopez
http://creativecommons.org/licenses/by/4.0
http://purl.org/coar/access_right/c_abf224
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
text/xml
dc.publisher.en-US.fl_str_mv Universidad Pedagógica y Tecnológica de Colombia
dc.source.en-US.fl_str_mv Revista Facultad de Ingeniería; Vol. 32 No. 65 (2023): July-September 2023 (Continuous Publication); e16314
dc.source.es-ES.fl_str_mv Revista Facultad de Ingeniería; Vol. 32 Núm. 65 (2023): Julio-Septiembre 2023 (Publicación Continua); e16314
dc.source.none.fl_str_mv 2357-5328
0121-1129
institution Universidad Pedagógica y Tecnológica de Colombia
repository.name.fl_str_mv Repositorio Institucional UPTC
repository.mail.fl_str_mv repositorio.uptc@uptc.edu.co
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spelling 2023-09-302024-07-05T19:12:11Z2024-07-05T19:12:11Zhttps://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314https://repositorio.uptc.edu.co/handle/001/14378In the health sector, the reports on delivery of prescriptions and the assignment of medical appointments are generated by the Health Service Provider Institutions and delivered to the Health Service Promoting Entities. These reports usually have an incoherent structure; inconsistencies in the format; non-existent, incomplete, or non-standardized data. These problems affect data quality and hinder the reliability of the information. To address this, it is proposed to adapt Kahn's data quality categories, to these reports, considering that the health sector accepts them categories and contemplates not only the structure and domain of the data but also its completeness and plausibility (credibility). This research followed the methodology of Pratt’s Iterative Research Pattern, studies related to the subject were observed, and the attributes of prescription delivery and appointment assignment were analyzed to understand the problem and its implications in detail. We then adapted the data quality categories proposed by Kahn, taking into account the problems identified in these reports. Subsequently, a group of health experts evaluated the proposed adaptation using the focus group technique. The results, according to their perception, showed that the prescription delivery report obtained 66.7% in the “Completely Agree” category and 33.3% in the “Agree” category; medical appointment assignment had 73.3% in “Completely Agree” and 26.7% in “Agree”, according to the Likert scale. In conclusion, this research contributes to strengthening the data quality of these reports by providing guidelines to improve the reliability of the information.En el sector de la salud, los reportes de entrega de medicamentos y asignación de citas médicas son generados por las Instituciones Prestadoras de Servicios de Salud y entregados a las Entidades Promotoras de Servicios de Salud. Estos reportes no suelen tener una estructura coherente, presentan inconsistencias en el formato, datos inexistentes, incompletos o no normalizados. Estos problemas afectan la calidad de estos y dificultan la confiabilidad de la información. Con el objetivo de abordar este problema, se propone adaptar las Categorías de Calidad de Datos de Kahn a estos reportes, teniendo en cuenta que estas son aceptadas por el sector salud y no solo contemplan la estructura y dominio del dato, sino también la completitud y plausibilidad (credibilidad) del mismo. Para llevar a cabo esta investigación se siguió la metodología del Patrón de Investigación Iterativa de Pratt, se observaron estudios relacionados con el tema y se analizaron los atributos de los reportes de entrega de medicamentos y asignación de citas médicas para comprender en detalle el problema y sus implicaciones. Luego, se adaptaron las categorías de calidad de datos propuestos por Kahn teniendo en cuenta los problemas identificados en estos reportes y, posteriormente, dicha adaptación fue evaluada por un grupo de expertos en el sector salud mediante la técnica de grupo focal. Los resultados, según la percepción de los expertos, demostraron que la adaptación realizada para el reporte de entrega de medicamentos obtuvo un 66.7% en la categoría “Completamente de Acuerdo” y 33.3% en “De Acuerdo”; para asignación de citas médicas un 73.3% en “Completamente de Acuerdo” y un 26.7% en “De Acuerdo” según la escala de Likert. En conclusión, esta investigación contribuye al fortalecimiento de la calidad de los datos de estos reportes en el sector salud y proporciona pautas para mejorar la confiabilidad de la información.application/pdftext/xmlengengUniversidad Pedagógica y Tecnológica de Colombiahttps://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314/13528https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314/13813Copyright (c) 2023 Daisy-Yisel Meneses-Lopez, Martha-Eliana Mendoza-Becerra, Salvador Garcia-Lopezhttp://creativecommons.org/licenses/by/4.0http://purl.org/coar/access_right/c_abf224http://purl.org/coar/access_right/c_abf2Revista Facultad de Ingeniería; Vol. 32 No. 65 (2023): July-September 2023 (Continuous Publication); e16314Revista Facultad de Ingeniería; Vol. 32 Núm. 65 (2023): Julio-Septiembre 2023 (Publicación Continua); e163142357-53280121-1129Data qualityData quality categoriesDrug deliveryMedical appointment schedulingConformanceCompletenessPlausibilityHealth regulatory reportingCalidad de datosCategorías de calidad de datosEntrega de MedicamentosAsignación de citas médicasConformidadCompletitudPlausibilidadSaludReportes normativos en saludKahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment ReportsAdaptación de las categorías de calidad de datos de Kahn para reportes de entrega de medicamentos y asignación de citas médicasinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a307http://purl.org/coar/version/c_970fb48d4fbd8a85Meneses-Lopez, Daisy-YiselMendoza-Becerra, Martha-ElianaGarcia-Lopez, Salvador001/14378oai:repositorio.uptc.edu.co:001/143782025-07-18 11:53:44.101metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCrepositorio.uptc@uptc.edu.co