Update logic/financial_model.py

logic/financial_model.py

@@ -1,26 +1,33 @@
-# File: logic/financial_model.py
-
-def estimate_financial_recovery(care_gaps_df, patient_data, base_rate, sdoh_keys):
-    results = []
-    hcc_weights = {
-        'HCC18': 0.4, 'HCC85': 0.5, 'HCC19': 0.3
-    }
-
-    for _, row in care_gaps_df.iterrows():
-        patient = patient_data[patient_data['patient_id'] == row['patient_id']].iloc[0]
-        hccs = [h.strip() for h in str(patient.get('hcc_codes', '')).split(';') if h.strip() in hcc_weights]
-        risk_score = sum(hcc_weights[h] for h in hccs)
-
-        sdoh_count = sum(1 for key in sdoh_keys if str(patient.get(key, '')).strip().lower() == 'yes')
-        sdoh_modifier = 1 + 0.05 * sdoh_count  # Each SDOH adds 5% to the opportunity
-
-        care_gap_count = len(row['care_gaps'])
-        expected_gain = base_rate * (risk_score * 0.02 * care_gap_count) * sdoh_modifier
-
-        results.append({
-            'patient_id': row['patient_id'],
-            'risk_score': round(risk_score, 2),
-            'projected_gain': round(expected_gain, 2)
-        })
-
-    return pd.DataFrame(results)
+# File: logic/financial_model.py
+
+def normalize_bool(value):
+    if isinstance(value, str):
+        value = value.strip().lower()
+        return value in ["yes", "true", "1"]
+    return bool(value)
+
+def estimate_financial_recovery(care_gaps_df, patient_data, base_rate, sdoh_keys):
+    results = []
+    hcc_weights = {
+        'HCC18': 0.4, 'HCC85': 0.5, 'HCC19': 0.3
+    }
+
+    for _, row in care_gaps_df.iterrows():
+        patient = patient_data[patient_data['patient_id'] == row['patient_id']].iloc[0]
+        hccs = [h.strip() for h in str(patient.get('hcc_codes', '')).split(';') if h.strip() in hcc_weights]
+        risk_score = sum(hcc_weights[h] for h in hccs)
+
+        sdoh_count = sum(1 for key in sdoh_keys if normalize_bool(patient.get(key)))
+        sdoh_modifier = 1 + 0.05 * sdoh_count  # Each SDOH adds 5% to the opportunity
+
+        care_gap_count = len(row['care_gaps'])
+        expected_gain = base_rate * (risk_score * 0.02 * care_gap_count) * sdoh_modifier
+
+        results.append({
+            'patient_id': row['patient_id'],
+            'risk_score': round(risk_score, 2),
+            'projected_gain': round(expected_gain, 2),
+            'sdoh_flags': sdoh_count
+        })
+
+    return pd.DataFrame(results)