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Hospital Emergency Room Savings via Health Line S24 in Portugal

Author

Listed:
  • Paula Simões

    (Centro de Matemática e Aplicações (CMA), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
    Centro de Investigação, Desenvolvimento e Inovação da Academia Militar (CINAMIL), 1169-203 Lisboa, Portugal
    These authors contributed equally to this work.)

  • Sérgio Gomes

    (Direção Geral de Saúde, 1049-005 Lisboa, Portugal)

  • Isabel Natário

    (Centro de Matemática e Aplicações (CMA), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
    Departamento de Matemática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
    These authors contributed equally to this work.)

Abstract

Hospital emergency departments are often overused by patients that do not really need urgent care. These admissions are one of the major factors contributing to hospital costs, which should not be allowed to compromise the response and effectiveness of the National Health Services (SNS). The aim of this study is to perform a detailed spatial health econometrics analysis of the non-urgent emergency situations (classified by Manchester triage) by area, linking them with the efficient use of the national health line, the Saude24 line (S24 line). This is evaluated through the S24 savings calls, using a savings index and its spatial effectiveness in solving the non-urgent emergency situations. A savings call is a call by a user whose initial intention was to go to an urgency department, but who. after calling the S24 line. changed his/her mind. Given the spatial nature of the data, and resorting to INLA in a Bayesian paradigm, the number of non-urgent cases in the Portuguese urgency hospital departments is modeled in an autoregressive way. The spatial structure is accounted for by a set of random effects. The model additionally includes regular covariates and a spatially lagged covariate savings index, related with the S24 savings calls. Therefore, the response in a given area depends not only on the (weighted) values of the response in its neighborhood and of the considered covariates, but also on the (weighted) values of the covariate savings index measured in each neighbor, by means of a Bayesian Poisson spatial Durbin model.

Suggested Citation

  • Paula Simões & Sérgio Gomes & Isabel Natário, 2021. "Hospital Emergency Room Savings via Health Line S24 in Portugal," Econometrics, MDPI, vol. 9(1), pages 1-10, February.
    • Handle: RePEc:gam:jecnmx:v:9:y:2021:i:1:p:8-:d:503054
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    References listed on IDEAS

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    1. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    2. Bivand, Roger & Gómez-Rubio, Virgilio & Rue, Håvard, 2015. "Spatial Data Analysis with R-INLA with Some Extensions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i20).
    3. Hughes, David & McGuire, Alistair, 2003. "Stochastic demand, production responses and hospital costs," Journal of Health Economics, Elsevier, vol. 22(6), pages 999-1010, November.
    4. Paula Simões & M. Lucília Carvalho & Sandra Aleixo & Sérgio Gomes & Isabel Natário, 2017. "A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line," Econometrics, MDPI, vol. 5(2), pages 1-23, June.
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