Resumo

MENESES-LOPEZ, Daisy-Yisel; MENDOZA-BECERRA, Martha-Eliana  e  GARCIA-LOPEZ, Salvador. Kahn's Data Quality Categories Adaptation for Prescription delivery and Medical Appointment Assignment Reports. Rev. Fac. ing. [online]. 2023, vol.32, n.65, e3.  Epub 12-Jan-2024. ISSN 0121-1129.  https://doi.org/10.19053/01211129.v32.n65.2023.16314.

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.

Palavras-chave : completeness; conformance; data quality; data quality categories; health; health regulatory reporting; medical appointment scheduling; medication delivery; plausibility.

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