diff --git a/hospital-affiliations/v1/aff_hosp.py b/hospital-affiliations/v1/aff_hosp.py
index 47a22f6..3667ece 100755
--- a/hospital-affiliations/v1/aff_hosp.py
+++ b/hospital-affiliations/v1/aff_hosp.py
@@ -33,8 +33,8 @@
 
 # correction of the cities: convert acronyms
 def convert_acronyms(city_name):
-    stripped = city_name.replace("Saint", "St")
-    stripped = stripped.replace("Mont", "Mt")
+    stripped = city_name.replace("Saint ", "St ")
+    stripped = stripped.replace("Mont ", "Mt ")
 
     return stripped
 
@@ -55,33 +55,27 @@
 
 
 def get_corresponding_hospital_from_affiliation(affiliation):
-    affiliations_dataframe = pd.read_csv("hospital_affiliations.csv", sep=";")
+    affiliations_dataframe = pd.read_csv("hospital_affiliation_normalize.csv", sep=";")
     acronyms = config.acronyms
 
-    hospital = "N.C"
+    hospital = "n/a"
     affiliation = affiliation.lower()
+    affiliation = remove_accents(affiliation)
+
     for acronym in acronyms:
         if acronym not in affiliation:
             continue
 
-        # standarize original dataframe
-        affiliations_dataframe["contains_acronyms"] = affiliations_dataframe["Affiliation"].apply(is_hospital_affiliation)
-        affiliations_dataframe["standardized_city"] = affiliations_dataframe["Ville_canonique_Dpt"].apply(remove_department_numbers).apply(convert_acronyms).apply(remove_accents)
-
-        acronyms_dataframe = affiliations_dataframe[affiliations_dataframe["contains_acronyms"] == True]  # noqa: E712
-        if len(acronyms_dataframe) == 0:  # noqa: E712
-            continue
-
         # create new dataframe only with affiliations that contain acronyms
-        acronyms_dataframe["city_in_affiliations"] = acronyms_dataframe["standardized_city"].apply(is_city_in_affiliation, affiliation=affiliation)
+        affiliations_dataframe["city_in_affiliations"] = affiliations_dataframe["standardized_city"].apply(is_city_in_affiliation, affiliation=affiliation)
 
-        ancronyms_cities_dataframe = acronyms_dataframe[acronyms_dataframe["city_in_affiliations"] == True]  # noqa: E712
-        if len(ancronyms_cities_dataframe) == 0:
+        acronyms_cities_dataframe = affiliations_dataframe[affiliations_dataframe["city_in_affiliations"] == True]  # noqa: E712
+        if len(acronyms_cities_dataframe) == 0:
             continue
 
         # create new dataframe only with affiliations that contain acronyms and cities
-        ancronyms_cities_dataframe["ratio"] = ancronyms_cities_dataframe["Affiliation"].apply(affiliations_match_ratio, second_affiliation=affiliation)
-        hospital = ancronyms_cities_dataframe["Orga NonCnrs Acorriger"][ancronyms_cities_dataframe["ratio"].idxmax()]
+        acronyms_cities_dataframe["ratio"] = acronyms_cities_dataframe["Affiliation"].apply(affiliations_match_ratio, second_affiliation=affiliation)
+        hospital = acronyms_cities_dataframe["standardized_hospital"][acronyms_cities_dataframe["ratio"].idxmax()]
         break
 
     return hospital