Vargas-Herrera J, Miki J, Wong LL, Monzón JM, Villanueva R. Automated coding and selection of causes of death in Peru: a descriptive study, 2016-2019.
Epidemiol Serv Saude 2023;
32:e2023024. [PMID:
37729274 PMCID:
PMC10547024 DOI:
10.1590/s2237-96222023000300005.en]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/07/2023] [Indexed: 09/22/2023] Open
Abstract
MAIN RESULTS
It could be seen good performance of the software for the automatic selection of the underlying cause of death, increasing from 69.6% in 2016 to 78.8% in 2019. There was a correlation between this result and the use of online death certificates by physicians.
IMPLICATIONS FOR SERVICES
Automatic coding and selection of causes of death improve productivity and timeliness of information, contributing to the quality of the country's information system.
PERSPECTIVES
It is necessary to analyze the agreement between the medical terms in the software dictionaries used in South American countries in order to improve standardization and comparability of information on causes of death.
OBJECTIVE
to describe software performance in the automatic selection of the underlying cause of death in Peru, between 2016 and 2019.
METHODS
this was a descriptive study on the software performance in the automated selection of the underlying cause of death over the years (chi-square test for trend) and the correlation between the type of death certificate and software performance (correlation coefficient and coefficient of determination).
RESULTS
a total of 446,217 death certificates were analyzed; the proportion of death certificates with the underlying cause of death increased from 69.6% in 2016 to 78.8% in 2019 (p-value < 0.001); it could be seen a direct linear correlation between electronic death certificates and software performance (correlation coefficient = 0.95; R2 = 0.89).
CONCLUSION
the software showed good performance in the automatic selection of the underlying cause of death, with a significant increase between 2016 and 2019.
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