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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| Format: | Online |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2023
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314 |
| _version_ | 1801706103919083520 |
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| author | Meneses-Lopez, Daisy-Yisel Mendoza-Becerra, Martha-Eliana Garcia-Lopez, Salvador |
| author_facet | Meneses-Lopez, Daisy-Yisel Mendoza-Becerra, Martha-Eliana Garcia-Lopez, Salvador |
| author_sort | Meneses-Lopez, Daisy-Yisel |
| collection | OJS |
| 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. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-16314 |
| institution | Revista Facultad de Ingeniería |
| language | eng |
| publishDate | 2023 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-163142024-01-17T01:11:56Z Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports Adaptación de las categorías de calidad de datos de Kahn para reportes de entrega de medicamentos y asignación de citas médicas Meneses-Lopez, Daisy-Yisel Mendoza-Becerra, Martha-Eliana Garcia-Lopez, Salvador 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 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. 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. Universidad Pedagógica y Tecnológica de Colombia 2023-09-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314 Revista Facultad de Ingeniería; Vol. 32 No. 65 (2023): July-September 2023 (Continuous Publication); e16314 Revista Facultad de Ingeniería; Vol. 32 Núm. 65 (2023): Julio-Septiembre 2023 (Publicación Continua); e16314 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314/13528 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314/13813 Copyright (c) 2023 Daisy-Yisel Meneses-Lopez, Martha-Eliana Mendoza-Becerra, Salvador Garcia-Lopez http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | 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 Meneses-Lopez, Daisy-Yisel Mendoza-Becerra, Martha-Eliana Garcia-Lopez, Salvador Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports |
| title | Kahn's Data Quality Categories for Prescription delivery and Medical Appointment Assignment Reports |
| title_alt | Adaptación de las categorías de calidad de datos de Kahn para reportes de entrega de medicamentos y asignación de citas médicas |
| 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_short | 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 |
| 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 |
| topic_facet | 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 |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16314 |
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