Analyzing Publicly Reported Waiting Times Data in Portuguese Health Service

This paper presents the development and implementation of a Business Intelligence (BI) system designed to analyze publicly reported waiting time data from the Portuguese National Health Service (SNS). Data were collected through a public Application Programming Interface (API), including near real-time emergency service updates (every 15 minutes), enabling more detailed temporal analyses. The system integrates and transforms raw data into structured analytical outputs, offering insights into waiting time disparities across institutions, specialties, and regions. It also evaluates data quality issues such as refresh frequency and inconsistencies. Our results highlight systemic inefficiencies and suggest opportunities for improving public health transparency and operational planning.

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Publication Details

Journal
Procedia Computer Science
Published
2026-01-01
DOI
https://doi.org/10.1016/j.procs.2026.03.073
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
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article

Analyzing Publicly Reported Waiting Times Data in Portuguese Health Service

Diogo Silva, Daniel Teixeira, Bruno Oliveira, Vânia Oliveira et al.
Procedia Computer Science
Data-Driven Disease Surveillance
article

Analyzing Publicly Reported Waiting Times Data in Portuguese Health Service

Diogo Silva, Daniel Teixeira, Bruno Oliveira, Vânia Oliveira, João Oliveira, Luís Soares
article en

Abstract

This paper presents the development and implementation of a Business Intelligence (BI) system designed to analyze publicly reported waiting time data from the Portuguese National Health Service (SNS). Data were collected through a public Application Programming Interface (API), including near real-time emergency service updates (every 15 minutes), enabling more detailed temporal analyses. The system integrates and transforms raw data into structured analytical outputs, offering insights into waiting time disparities across institutions, specialties, and regions. It also evaluates data quality issues such as refresh frequency and inconsistencies. Our results highlight systemic inefficiencies and suggest opportunities for improving public health transparency and operational planning.

Procedia Computer ScienceVol. 278
Instituto Superior de Contabilidade e Administracao do Porto (PT)
Openalex Percentile: Top 13%
Data-Driven Disease Surveillance
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