diff --git a/main.py b/main.py
index e5439de10676ba11089a3a711f8f5d7c4e75e9d6..a6ad4ccbea1fd22aaf152854506ca6486397d32c 100644
--- a/main.py
+++ b/main.py
@@ -134,11 +134,11 @@ def map_file(df, target_code_type, out, concepts, meta_columns=[], no_translate=
 	#Append to out df
 	if len(codes) > 0:
 		codes = pd.DataFrame({
-			"code":codes
+			"CONCEPT":codes
 		})
 		codes = codes.join(df_meta)
 		for concept in concepts:
-			codes["MELDB_concept"] = np.repeat(concept.strip(), len(codes))
+			codes["CONCEPT_SET"] = np.repeat(concept.strip(), len(codes))
 			out = pd.concat([out, codes])
 	return out
 	
@@ -200,7 +200,7 @@ def omop_publish_concept_sets(out, db_path, vocab_output, vocab_type):
 	conn = sqlite3.connect(db_path)
 	cur = conn.cursor()
 
-	for concept_set_name, grp in out.groupby("MELDB_concept"):		
+	for concept_set_name, grp in out.groupby("CONCEPT_SET"):		
 		#Create Concept_Set
 		if not sql_row_exist(conn, "CONCEPT_SET", "concept_set_name", concept_set_name):
 			cur.execute(f"INSERT INTO CONCEPT_SET (concept_set_name, vocabulary_id) VALUES ('{concept_set_name}', 'MELDB');")
@@ -214,7 +214,7 @@ def omop_publish_concept_sets(out, db_path, vocab_output, vocab_type):
 		concept_set_id = cur.fetchone()[0]
 		
 		#Get corresponing Concept_id (OMOP) for each Concept_code (e.g. SNOMED)
-		concept_codes = "'"+"', '".join(list(grp["code"].astype(str)))+"'"
+		concept_codes = "'"+"', '".join(list(grp["CONCEPT"].astype(str)))+"'"
 		query = f"SELECT concept_id FROM CONCEPT WHERE vocabulary_id = ? AND concept_code IN ({concept_codes});"
 		cur.execute(query, (vocab_type, ))
 		df_out = pd.DataFrame(cur.fetchall(), columns=["concept_id"])
@@ -329,8 +329,8 @@ def run_all(mapping_file, target_code_type,
 	
 	#Final Processing
 	out = out.reset_index(drop=True)
-	out = out.drop_duplicates(subset=["MELDB_concept", "code"])
-	out = out.sort_values(by=["MELDB_concept", "code"])
+	out = out.drop_duplicates(subset=["CONCEPT_SET", "CONCEPT"])
+	out = out.sort_values(by=["CONCEPT_SET", "CONCEPT"])
 	
 	#Merge with Concept Types in Summary Excel File
 	summary_config = mapping["concepts"]
@@ -346,9 +346,9 @@ def run_all(mapping_file, target_code_type,
 			summary_cols_all += v
 
 	summary_df = summary_df[summary_cols_all] #select all relevant columns 
-	summary_df = summary_df.rename(columns={summary_config["columns"]["concept_name"]: "MELDB_concept"})
+	summary_df = summary_df.rename(columns={summary_config["columns"]["concept_name"]: "CONCEPT_SET"})
 	summary_df = summary_df.drop_duplicates() #remove duplicates
-	out = out.merge(summary_df, how="left", on='MELDB_concept')
+	out = out.merge(summary_df, how="left", on='CONCEPT_SET')
 	
 	# Save Output File
 	print(bcolors.HEADER, "---"*5, "OUTPUT", "---"*5, bcolors.ENDC)
@@ -379,7 +379,7 @@ def run_all(mapping_file, target_code_type,
 	if os.path.exists(log_errors_path):
 		error_df = pd.read_csv(log_errors_path)
 		error_df = error_df.drop_duplicates() #Remove Duplicates from Error file
-		error_df = error_df.sort_values(by=["SOURCE", "CODE_TYPE", "CODE"])
+		error_df = error_df.sort_values(by=["SOURCE", "VOCABULARY", "CODE"])
 		error_df.to_csv(log_errors_path, index=False)
 	
 
diff --git a/publish.py b/publish.py
index bfd7301394b0e357cbee27e2d0c5886574ac1c80..f39a2316fa845dd0f112abdee16a2fa6fbf22da9 100644
--- a/publish.py
+++ b/publish.py
@@ -9,8 +9,8 @@ def main(config):
 	else:
 		raise Exception("Concepts file must be '.csv' filetype")
 	
-	for name, concept in df.groupby("MELDB_concept"):
-		concept = concept.sort_values(by="code") #sort rows
+	for name, concept in df.groupby("CONCEPT_SET"):
+		concept = concept.sort_values(by="CONCEPT") #sort rows
 		concept = concept.dropna(how='all', axis=1)  #remove empty cols
 		concept = concept.reindex(sorted(concept.columns), axis=1) #sort cols alphabetically
 
diff --git a/report.py b/report.py
index d43e263ede3977c191fd226e5635afb6852cd3d9..c36a298f2cf7727f26d76f5fec1dfd6ca1c8898d 100644
--- a/report.py
+++ b/report.py
@@ -111,9 +111,9 @@ def test_concept_changes(config, report):
 		report.write(f"`{out1}` to `{out2}`\n")
 		
 		df1 = pd.read_csv(out1)
-		df1 = df1[["code","MELDB_concept"]].groupby("MELDB_concept").count()
+		df1 = df1[["CONCEPT","CONCEPT_SET"]].groupby("CONCEPT_SET").count()
 		df2 = pd.read_csv(out2)
-		df2 = df2[["code","MELDB_concept"]].groupby("MELDB_concept").count()
+		df2 = df2[["CONCEPT","CONCEPT_SET"]].groupby("CONCEPT_SET").count()
 
 		#Added/Removed Concepts
 		report.write("- Removed Concepts {}\n".format(list(set(df1.index) - set(df2.index))))
@@ -121,10 +121,10 @@ def test_concept_changes(config, report):
 
 		#Changed Concepts
 		diff = df2 - df1 #diff in counts 
-		diff = diff[(~(diff["code"] == 0.0)) & diff["code"].notna()] #get non-zero counts
+		diff = diff[(~(diff["CONCEPT"] == 0.0)) & diff["CONCEPT"].notna()] #get non-zero counts
 		s = "\n"
 		for concept, row in diff.iterrows():
-			s += "\t - {} {}\n".format(concept, row["code"])
+			s += "\t - {} {}\n".format(concept, row["CONCEPT"])
 		report.write("- Changed Concepts {}\n\n".format(s))
 		
 # ✅ ❌