Geolocalização de internações: uma solução baseada no programa estatístico R para a implantação de possibilidades de análise baseadas no sistema de informação hospitalar

Thiago Augusto Rocha, Núbia Cristina da Silva, Pedro Amaral, João Ricardo Vissoci, Erika Bárbara Thomaz, Rejane Christine Queiroz, Matthew Harris, Christopher Millet, Thomas Hone, Antonio Augusto da Silva and Luiz Augusto Facchini
Additional contact information
Thiago Augusto Rocha: Center for Research and Graduate Studies in Business Administration, Federal University of Minas Gerais
Núbia Cristina da Silva: Center for Research and Graduate Studies in Business Administration, Federal University of Minas Gerais
João Ricardo Vissoci: Duke Global Health Institute, Duke University (USA)
Erika Bárbara Thomaz: Department of Public Health, Federal University of Maranhão
Rejane Christine Queiroz: Department of Public Health, Federal University of Maranhão
Matthew Harris: Faculty of Medicine, School of Public Health, Imperial College London
Christopher Millet: Faculty of Medicine, School of Public Health, Imperial College London
Thomas Hone: Faculty of Medicine, School of Public Health, Imperial College London
Antonio Augusto da Silva: Department of Public Health, Federal University of Maranhão
Luiz Augusto Facchini: Departamento de Medicina Social, Universidade Federal de Pelotas

No 567, Textos para Discussão Cedeplar-UFMG from Cedeplar, Universidade Federal de Minas Gerais

Abstract: Objective: This work details a method of geolocation of data on hospital admission authorizations, disclosed through the hospital information system. Methods: the present work can be classified as methodological development. In order to spatializedata of the AIHs wecreateda script using the software R, based on the packages downloadDataSUS and CepR. The script was applied to survey the AIHs in Goiás, for the year 2015. After downloading and pre-processing of the data, the IAHs were spatialized and could be geolocated. Results: 97.58% of the AIHs processed in 2015 in Goiás were geolocated. Of the 295,670 AIHs processed, it was possible to extract 24,220 different Zip codewith a success rate of geolocation of 98.72%. Conclusion: The method detailed in this paper opens a new range of possibilities for the design of evaluative studies, formulation of policies and planning of health care actions.

Keywords: Hospitalization; Information Systems; Automatic Data Processing; Data Analysis; Public Health Systems Research. (search for similar items in EconPapers)
JEL-codes: I18 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2017-09
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