| Autor: |
Bruno Pereira Nunes, João Vissoci, Felipe Mendes Delpino, Pablo Stolz, Sabrina Ribeiro Farias, Bruna Borges Coelho, Indiara da Silva Viegas, Denis Carlos Carvalho Junior, Camila Sebaje da Silva Dias, Ana Paula Santana Coelho Almeida, Luiz Augusto Facchini, Alexandre Dias Porto Chiavegatto Filho |
| Jazyk: |
English<br />Portuguese |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Revista Brasileira de Epidemiologia, Vol 24 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1980-5497 |
| DOI: |
10.1590/1980-549720210050 |
| Popis: |
ABSTRACT: Objective: Emergency services are essential to the organization of the health care system. Nevertheless, they face different operational difficulties, including overcrowded services, largely explained by their inappropriate use and the repeated visits from users. Although a known situation, information on the theme is scarce in Brazil, particularly regarding longitudinal user monitoring. Thus, this project aims to evaluate the predictive performance of different machine learning algorithms to estimate the inappropriate and repeated use of emergency services and mortality. Methods: To that end, a study will be conducted in the municipality of Pelotas, Rio Grande do Sul, with around five thousand users of the municipal emergency department. Results: If the study is successful, we will provide an algorithm that could be used in clinical practice to assist health professionals in decision-making within hospitals. Different knowledge dissemination strategies will be used to increase the capacity of the study to produce innovations for the organization of the health system and services. Conclusion: A high performance predictive model may be able to help decisionmaking in the emergency services, improving quality of care. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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