Update logic/care_gap_engine.py

logic/care_gap_engine.py

@@ -12,7 +12,7 @@ def normalize_bool(value):
             return False
     elif isinstance(value, (int, float)):
         return bool(value)
-    return value
+    return False
 
 def normalize_gender(value):
     if isinstance(value, str):
@@ -23,7 +23,19 @@ def normalize_gender(value):
             return "M"
     elif isinstance(value, int):
         return "F" if value == 2 else "M"
-    return value
+    return "U"  # Unknown
+
+def normalize_float(value, default=0.0):
+    try:
+        return float(value)
+    except:
+        return default
+
+def normalize_readmissions(value):
+    try:
+        return int(value) if int(value) >= 0 else 0
+    except:
+        return 0
 
 def evaluate_care_gaps(df: pd.DataFrame, config):
     today = datetime.today()
@@ -34,29 +46,30 @@ def evaluate_care_gaps(df: pd.DataFrame, config):
         gaps = []
 
         gender = normalize_gender(row.get('gender'))
+        age = normalize_float(row.get('age'))
+        systolic_bp = normalize_float(row.get('systolic_bp'))
+        hba1c_value = normalize_float(row.get('hba1c_value'))
+        last_mammo = pd.to_datetime(row.get('last_mammogram', None), errors='coerce')
+        last_colono = pd.to_datetime(row.get('last_colonoscopy', None), errors='coerce')
 
-        if gender == 'F' and rules['Breast Cancer Screening']['min_age'] <= row['age'] <= rules['Breast Cancer Screening']['max_age']:
-            if not row['last_mammogram'] or (today - pd.to_datetime(row['last_mammogram'], errors='coerce')).days > rules['Breast Cancer Screening']['interval_days']:
+        if gender == 'F' and rules['Breast Cancer Screening']['min_age'] <= age <= rules['Breast Cancer Screening']['max_age']:
+            if pd.isna(last_mammo) or (today - last_mammo).days > rules['Breast Cancer Screening']['interval_days']:
                 gaps.append("Breast Cancer Screening")
 
-        if rules['Colorectal Cancer Screening']['min_age'] <= row['age'] <= rules['Colorectal Cancer Screening']['max_age']:
-            if not row['last_colonoscopy'] or (today - pd.to_datetime(row['last_colonoscopy'], errors='coerce')).days > rules['Colorectal Cancer Screening']['interval_days']:
+        if rules['Colorectal Cancer Screening']['min_age'] <= age <= rules['Colorectal Cancer Screening']['max_age']:
+            if pd.isna(last_colono) or (today - last_colono).days > rules['Colorectal Cancer Screening']['interval_days']:
                 gaps.append("Colorectal Cancer Screening")
 
-        if row.get('systolic_bp', '') != "" and pd.to_numeric(row['systolic_bp'], errors='coerce') > rules['Blood Pressure Control']['bp_threshold']:
+        if systolic_bp > rules['Blood Pressure Control']['bp_threshold']:
             gaps.append("Blood Pressure Control")
 
-        if row.get('hba1c_value', '') != "" and pd.to_numeric(row['hba1c_value'], errors='coerce') >= 9:
+        if hba1c_value >= 9:
             gaps.append("Diabetes: Poor HbA1c Control")
 
         if not normalize_bool(row.get('FollowUp_Scheduled')) or not normalize_bool(row.get('Primary_Care_Established')):
             gaps.append("Follow-Up Care")
 
-        previous_readm = row.get('Previous_Readmissions', '')
-        if isinstance(previous_readm, str):
-            if previous_readm.isdigit() and int(previous_readm) >= 3:
-                gaps.append("Readmission Risk")
-        elif isinstance(previous_readm, (int, float)) and previous_readm >= 3:
+        if normalize_readmissions(row.get('Previous_Readmissions')) >= 3:
             gaps.append("Readmission Risk")
 
         results.append({