diff --git a/hospital-affiliations/aff_hosp.py b/hospital-affiliations/aff_hosp.py
index 8c6f614..f280ccc 100755
--- a/hospital-affiliations/aff_hosp.py
+++ b/hospital-affiliations/aff_hosp.py
@@ -50,29 +50,33 @@
     "icans","paul strauss","paul str","paoli calmettes","inst j paoli i calmettes","claudius regaud","claudius rigaud","inst univ canc toulouse oncopole",
     "inst univ cancerol oncopole","iuct oncopole","ctr lutte contre canc","ico canc","icm, montpellier canc inst","univ inst canc toulouse oncopole","rothschild"]
     
-    hospital = ""
+    hospital = "N.C"
     affiliation = affiliation.lower()
     for acronym in acronyms:
-        if acronym in affiliation :
-            # standarize original dataframe
-            affiliations_dataframe["contains_acronyms"] = affiliations_dataframe["Affiliation"].apply(is_hospital_affiliation,list=acronyms)
-            affiliations_dataframe["standardized_city"] = affiliations_dataframe["Ville_canonique_Dpt"].apply(remove_department_numbers_and_convert_acronyms).apply(remove_accents)
-            if len(affiliations_dataframe[affiliations_dataframe['contains_acronyms'] == True]) > 0:
-                # create new dataframe only with affiliations that contain acronyms
-                dataframe_with_acronyms = affiliations_dataframe[affiliations_dataframe['contains_acronyms'] == True]
-                dataframe_with_acronyms["city_in_affiliations"] = dataframe_with_acronyms['standardized_city'].apply(is_city_in_affiliation,affiliation=affiliation)
-                if len(dataframe_with_acronyms[dataframe_with_acronyms["city_in_affiliations"] == True]) != 0:
-                    # create new dataframe only with affiliations that contain acronyms and cities
-                    dataframe_with_acronyms_and_cities = dataframe_with_acronyms[dataframe_with_acronyms["city_in_affiliations"] == True]
-                    dataframe_with_acronyms_and_cities["ratio"] = dataframe_with_acronyms_and_cities["Affiliation"].apply(affiliations_match_ratio,second_affiliation=affiliation)
-                    hospital = dataframe_with_acronyms_and_cities["Orga NonCnrs Acorriger"][dataframe_with_acronyms_and_cities["ratio"].idxmax()]
-                else:
-                    hospital = "N.C"
-            else:
-                hospital = "N.C"
-            break
-    if hospital == "":
-        hospital = "N.C"
+        if acronym not in affiliation :
+            continue
+
+        # standarize original dataframe
+        affiliations_dataframe["contains_acronyms"] = affiliations_dataframe["Affiliation"].apply(is_hospital_affiliation,list=acronyms)
+        affiliations_dataframe["standardized_city"] = affiliations_dataframe["Ville_canonique_Dpt"].apply(remove_department_numbers_and_convert_acronyms).apply(remove_accents)
+        
+        if len(affiliations_dataframe[affiliations_dataframe['contains_acronyms'] == True]) == 0:
+            continue
+
+        # create new dataframe only with affiliations that contain acronyms
+        dataframe_with_acronyms = affiliations_dataframe[affiliations_dataframe['contains_acronyms'] == True]
+        dataframe_with_acronyms["city_in_affiliations"] = dataframe_with_acronyms['standardized_city'].apply(is_city_in_affiliation,affiliation=affiliation)
+        
+        if len(dataframe_with_acronyms[dataframe_with_acronyms["city_in_affiliations"] == True]) == 0:
+            continue
+
+        # create new dataframe only with affiliations that contain acronyms and cities
+        dataframe_with_acronyms_and_cities = dataframe_with_acronyms[dataframe_with_acronyms["city_in_affiliations"] == True]
+        dataframe_with_acronyms_and_cities["ratio"] = dataframe_with_acronyms_and_cities["Affiliation"].apply(affiliations_match_ratio,second_affiliation=affiliation)
+        hospital = dataframe_with_acronyms_and_cities["Orga NonCnrs Acorriger"][dataframe_with_acronyms_and_cities["ratio"].idxmax()]             
+        
+        break
+
     return hospital
 
 for line in sys.stdin:
diff --git a/hospital-affiliations/input_data.txt b/hospital-affiliations/input_data.txt
index 9a74acb..2a7b0e1 100644
--- a/hospital-affiliations/input_data.txt
+++ b/hospital-affiliations/input_data.txt
@@ -1,5 +1,5 @@
 {"id" :"3","value": "Hop La Pitie Salpetriere, AP HP, Serv Med Interne, Ctr Natl Reference Histiocytoses, Paris, France"}
-{"id" :"4","value": "Hop Necker Enfants Malad, Gen Pediat Dept, Paris, France"}
+{"id" :"4","value": "UMR CNRS 7021, Illkirch Graffenstaden, France"}
 {"id" :"5","value": "Bergonie Inst, Dept Radiat Oncol, Bordeaux, France"}
 {"id" :"6","value": "CHU Besancon, Serv Neurol, 2 Blvd Fleming, F-25030 Besancon, France"}
 {"id" :"7","value": "CHU St Etienne, Ave Albert Raimond, F-42055 St Etienne, France"}
\ No newline at end of file