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# Variables du référentiel gme_public en 2021 | |
Rows: 3,742 | |
Columns: 20 | |
$ gmt <chr> "0001", "0002", "0003", "0004", "0005", "0006", "0007", "0008", "0009", "0010", "0011", "0012", "0013", "0014", "0015~ | |
$ gme <chr> "0103A1", "0103A2", "0106A0", "0106A1", "0106A2", "0106B0", "0106B1", "0106B2", "0109A0", "0109A1", "0109A2", "0109B0~ | |
$ gme_lib <chr> "Etats végétatifs chroniques \u0096 Etats pauci-relationnels - niveau 1", "Etats végétatifs chroniques \u0096 Etats p~ | |
$ gme_dzf <int> 1, 1, NA, 1, 1, NA, 1, 1, NA, 1, 1, NA, 1, 1, NA, 1, 1, NA, 36, 50, NA, 64, 127, 57, 92, 15, 36, 36, 57, NA, 1, 1, NA~ | |
$ gme_fzf <int> 1, 1, NA, 21, 21, NA, 21, 21, NA, 21, 21, NA, 21, 21, NA, 21, 21, NA, 42, 56, NA, 70, 133, 63, 98, 35, 42, 42, 63, NA~ | |
$ gme_tzb <dbl> 318.74, 364.57, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 365.40, 382.33, NA, 301.98, 344.85, 3~ | |
$ gme_tzf <dbl> 318.74, 364.57, 258.30, 2413.55, 2914.03, 268.95, 3472.11, |
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# Liste des variables RUM issues de l'export JSON de l'écran "Base des RUM" (accès Standard) | |
Columns: 37 | |
$ nas <chr> | |
$ no_rum <chr> | |
$ no_rss <chr> | |
$ code_retour <chr> | |
$ ghm <chr> | |
$ date_naissance <date> | |
$ sexe <chr> | |
$ no_um <chr> |
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# base_ccam = import écran "Base des actes CCAM" et sélection des 5 variables (nas, no_rum, code_ccam, activite_ccam, ext_doc_ccam) | |
# (nas, no_rum) = clé pour retrouver les rum concernés | |
liste_C170 <- base_ccam %>% | |
dplyr::filter(activite_ccam == "4", is.na(ext_doc_ccam)) | |
# liste_C170 = liste des actes CCAM d'anesthésie sans extension documentaire codée | |
# Exemple de 1ere ligne de liste_C170 correspondant à un codage repéré C170 | |
liste_C170[1,] |
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library(dplyr) | |
# smr = .rds via PMSISoft SMR accès Standard | |
sejour_dependance_maximale <- smr$rhs$rhs %>% | |
dplyr::select(nas,no_semaine,annee_semaine,ordre_rhs,no_um,annee_import,type_hospitalisation, | |
date_fin_sejour,gme, | |
starts_with("dependance_")) %>% | |
# filtrage HC, séjours terminés et une année (2023 par ex) | |
dplyr::filter(type_hospitalisation == "1", !is.na(date_fin_sejour), annee_import == "2023") %>% | |
dplyr::group_by(nas) %>% |