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Automated coding and selection of causes of death in Peru: a descriptive study, 2016-2019

Automatización de la codificación y selección de causas de muerte en Perú: estudio descriptivo, 2016-2019

Study contributions

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.

Abstract

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.

Keywords:
Causes of Death; Mortality Records; International Classification of Diseases; Health Information Systems; Information Technology; Descriptive Epidemiology

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