Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal.

ABSTRACT:

Background

Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task.

Objective

This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena.

Method

We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011-2015. We applied different a

SUBMITTER: Portela D 

PROVIDER: S-EPMC11408983 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature