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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logo.png filter=lfs diff=lfs merge=lfs -text

mock_ehr_service.py

@@ -0,0 +1,19 @@
+# We need to tell Python to look one level up for our files
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+# Now we can import our patient data
+from patient_data import PATIENT_ZERO_DATA, PATIENT_ONE_DATA
+
+def get_patient_data(patient_id: str):
+    """
+    Simulates fetching a patient's record from an EHR database.
+    """
+    print(f"--- [EHR Service] Fetching data for patient_id: {patient_id} ---")
+    if patient_id == "P0-12345":
+        return PATIENT_ZERO_DATA
+    elif patient_id == "P1-67890":
+        return PATIENT_ONE_DATA
+    else:
+        return None # Return nothing if the patient isn't found

orchestrator.py

@@ -0,0 +1,26 @@
+# Click the "Copy" button in the top-right corner of this box.
+from services.mock_ehr_service import get_patient_data
+from services.mock_ai_service import get_readmission_prediction
+import json
+
+def run_patient_risk_analysis(patient_id: str):
+    """
+    Orchestrates the process of fetching patient data and getting a prediction.
+    """
+    print(f"--- [Orchestrator] Starting analysis for patient_id: {patient_id} ---")
+
+    # Step 1: Get patient data from the mock EHR service
+    patient_data = get_patient_data(patient_id)
+
+    if not patient_data:
+        print(f"--- [Orchestrator] Analysis failed: No data found for patient_id {patient_id} ---")
+        return None
+
+    # Step 2: Get a prediction from the mock AI service
+    prediction_json = get_readmission_prediction(patient_data)
+    prediction_data = json.loads(prediction_json) # Convert the JSON string to a dictionary
+
+    print(f"--- [Orchestrator] Analysis complete for patient_id: {patient_id} ---")
+
+    # Step 3: Return the final analysis data
+    return prediction_data

patient_data.py

@@ -0,0 +1,49 @@
+PATIENT_ZERO_DATA = {
+    "patient_id": "P0-12345",
+    "demographics": {
+        "age": 72,
+        "sex": "Male"
+    },
+    "recent_admission": {
+        "admission_date": "2024-06-15",
+        "discharge_date": "2024-06-22",
+        "primary_diagnosis": "Acute Myocardial Infarction",
+        "comorbidities": ["Hypertension", "Type 2 Diabetes", "Chronic Kidney Disease"],
+        "procedure_history": ["Coronary Artery Bypass Grafting"]
+    },
+    "lab_results": {
+        "hemoglobin_a1c": "7.8%",
+        "creatinine_level": "1.9 mg/dL",
+        "ejection_fraction": "45%"
+    },
+    "medications": [
+        "Lisinopril",
+        "Metformin",
+        "Aspirin",
+        "Atorvastatin"
+    ]
+}
+
+PATIENT_ONE_DATA = {
+    "patient_id": "P1-67890",
+    "demographics": {
+        "age": 55,
+        "sex": "Female"
+    },
+    "recent_admission": {
+        "admission_date": "2024-07-01",
+        "discharge_date": "2024-07-05",
+        "primary_diagnosis": "Pneumonia",
+        "comorbidities": ["Asthma"],
+        "procedure_history": []
+    },
+    "lab_results": {
+        "hemoglobin_a1c": "5.5%",
+        "creatinine_level": "0.8 mg/dL",
+        "ejection_fraction": "60%"
+    },
+    "medications": [
+        "Albuterol",
+        "Amoxicillin"
+    ]
+}

services/mock_ai_service.py

@@ -0,0 +1,45 @@
+
+import json
+
+def get_readmission_prediction(patient_data: dict):
+    """
+    Simulates calling the MedGemma API to get a readmission risk prediction.
+    """
+    patient_id = patient_data.get("patient_id")
+    print(f"--- [AI Service] Analyzing data for patient_id: {patient_id} ---")
+
+    # Based on the patient, return a different mock prediction
+    if patient_id == "P0-12345":
+        # Simulate a high-risk patient
+        mock_response = {
+            "risk_score": 0.85,
+            "confidence": "High",
+            "key_factors": [
+                "History of Acute Myocardial Infarction",
+                "High Hemoglobin A1c (7.8%)",
+                "Chronic Kidney Disease"
+            ],
+            "recommended_interventions": [
+                "Follow-up cardiology appointment within 7 days",
+                "Medication reconciliation by pharmacist",
+                "Telehealth check-in on day 3 post-discharge"
+            ]
+        }
+    elif patient_id == "P1-67890":
+        # Simulate a low-risk patient
+        mock_response = {
+            "risk_score": 0.15,
+            "confidence": "High",
+            "key_factors": [
+                "No major chronic conditions",
+                "Successful treatment for Pneumonia"
+            ],
+            "recommended_interventions": [
+                "Standard primary care follow-up within 30 days"
+            ]
+        }
+    else:
+        mock_response = {"error": "Patient data not valid"}
+
+    # The 'json.dumps' part formats the dictionary nicely, like a real API would
+    return json.dumps(mock_response, indent=4)