Create app.py

app.py

@@ -0,0 +1,450 @@
+import json
+import time
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+
+# ==========================================
+# PAGE CONFIGURATION
+# ==========================================
+st.set_page_config(
+    page_title="Aetna SmartClaim AI Platform | CVS Health",
+    page_icon="🏥",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Custom CSS for Enterprise Styling
+st.markdown(
+    """
+<style>
+    .main-header { font-size: 26px; font-weight: 700; color: #CC0000; margin-bottom: 0px; }
+    .sub-header { font-size: 14px; color: #555555; margin-bottom: 20px; }
+    .metric-card { background-color: #F8F9FA; border-left: 4px solid #CC0000; padding: 12px; border-radius: 4px; }
+    .stButton>button { width: 100%; border-radius: 4px; height: 42px; font-weight: 600; }
+    .badge-pass { background-color: #D4EDDA; color: #155724; padding: 4px 8px; border-radius: 4px; font-weight: 600; }
+    .badge-review { background-color: #FFF3CD; color: #856404; padding: 4px 8px; border-radius: 4px; font-weight: 600; }
+    .badge-deny { background-color: #F8D7DA; color: #721C24; padding: 4px 8px; border-radius: 4px; font-weight: 600; }
+</style>
+""",
+    unsafe_allow_html=True,
+)
+
+# ==========================================
+# KNOWLEDGE BASE: AETNA CPBs & SAMPLE CLAIMS
+# ==========================================
+AETNA_CPBS = {
+    "CPB 0236 (Spine MRI)": {
+        "title": "Magnetic Resonance Imaging (MRI) of the Spine",
+        "code": "CPT 72148",
+        "criteria": [
+            "Clinical evidence of spinal stenosis or cauda equina compression",
+            "Progressively severe symptoms despite conservative management",
+            "Persistent back/neck pain with radiculopathy with failed 6+ weeks of conservative therapy (NSAIDs, physical therapy)",
+            "Suspected spinal infection, fracture, or malignancy",
+        ],
+    },
+    "CPB 0171 (Knee MRI)": {
+        "title": "Magnetic Resonance Imaging (MRI) of the Extremities",
+        "code": "CPT 73721",
+        "criteria": [
+            "Persistent knee swelling/instability not associated with injury, failed 3+ weeks conservative therapy",
+            "True joint locking indicative of torn meniscus or loose body",
+            "Suspected osteomyelitis or bone infection",
+            "Multi-view X-rays performed to rule out fracture prior to advanced imaging",
+        ],
+    },
+    "CPB 0157 (Bariatric Surgery)": {
+        "title": "Obesity Surgery",
+        "code": "CPT 43644",
+        "criteria": [
+            "Body Mass Index (BMI) >= 40 or BMI >= 35 with high-risk comorbidities (Type 2 Diabetes, Severe Sleep Apnea)",
+            "Documented participation in medically supervised weight loss program for >= 6 months",
+            "Psychosocial behavioral health evaluation and clearance completed",
+            "Absence of active substance use disorder or uncontrolled psychiatric illness",
+        ],
+    },
+}
+
+MOCK_CLAIMS = {
+    "CLM-90821 (Lumbar MRI - Compliant)": {
+        "claim_id": "CLM-90821",
+        "patient": "Jane Doe (DOB: 11/04/1978)",
+        "cpb": "CPB 0236 (Spine MRI)",
+        "cpt": "72148 - MRI Lumbar Spine w/o Contrast",
+        "diagnosis": "M54.16 - Radiculopathy, lumbar region",
+        "clinical_notes": "46yo female with severe low back pain radiating to L5 distribution. Completed 8 weeks of physical therapy and trials of Meloxicam without relief. Straight leg raise test positive on left. X-ray showed mild disc space narrowing at L4-L5, no acute fracture.",
+        "expected_status": "APPROVED",
+        "confidence": 0.97,
+        "citation": "Meets CPB 0236: Persistent back pain with radiculopathy + documented >6 weeks conservative therapy (8 wks PT + NSAIDs) + prior X-ray.",
+    },
+    "CLM-44319 (Spine MRI - Insufficient PT)": {
+        "claim_id": "CLM-44319",
+        "patient": "Robert Smith (DOB: 03/15/1985)",
+        "cpb": "CPB 0236 (Spine MRI)",
+        "cpt": "72148 - MRI Lumbar Spine w/o Contrast",
+        "diagnosis": "M54.50 - Low back pain, unspecified",
+        "clinical_notes": "41yo male presenting with acute low back pain following lifting heavy box 10 days ago. No numbness, tingling, or bowel/bladder dysfunction. Patient requests MRI today. Took OTC Ibuprofen twice with minor improvement.",
+        "expected_status": "MANUAL_REVIEW",
+        "confidence": 0.72,
+        "citation": "Fails CPB 0236: Only 10 days of symptoms. Required 6 weeks of conservative therapy (PT/NSAIDs) not completed. Red flag symptoms absent.",
+    },
+    "CLM-11920 (Bariatric Surgery - Missing Psych)": {
+        "claim_id": "CLM-11920",
+        "patient": "Maria Garcia (DOB: 08/22/1981)",
+        "cpb": "CPB 0157 (Bariatric Surgery)",
+        "cpt": "43644 - Laparoscopic Roux-en-Y Gastric Bypass",
+        "diagnosis": "E66.01 - Morbid severe obesity",
+        "clinical_notes": "44yo female, BMI 42.1 with poorly controlled Type 2 Diabetes (HbA1c 8.4%). Documented 6-month physician-monitored diet program completed in June 2026. Behavioral health evaluation pending scheduled visit next month.",
+        "expected_status": "PEND_DOCS",
+        "confidence": 0.81,
+        "citation": "Meets BMI criteria (>40) and 6-mo weight program, but missing mandatory Behavioral Health / Psych Clearance per CPB 0157.",
+    },
+}
+
+# Initialize session state for system stats
+if "processed_claims" not in st.session_state:
+    st.session_state.processed_claims = []
+
+
+# ==========================================
+# SIDEBAR CONTROL PANEL
+# ==========================================
+with st.sidebar:
+    st.image(
+        "https://upload.wikimedia.org/wikipedia/commons/f/f3/CVS_Health_logo.svg",
+        width=180,
+    )
+    st.markdown("### **AI Claims Platform Engine**")
+    st.caption("A&BC Agentic Decision-Support Framework")
+    st.divider()
+
+    st.subheader("⚙️ Platform Governance")
+    auto_adj_threshold = st.slider(
+        "Auto-Adjudication Confidence Threshold",
+        min_value=0.70,
+        max_value=0.99,
+        value=0.90,
+        step=0.01,
+        help="Claims with model confidence above this threshold bypass manual review.",
+    )
+
+    st.subheader("🔑 Live LLM Config (Optional)")
+    api_key = st.text_input(
+        "OpenAI API Key",
+        type="password",
+        placeholder="sk-...",
+        help="Leave blank to use internal deterministic Agent Engine",
+    )
+
+    st.divider()
+    st.info(
+        "**Director Candidate Portfolio Prototype**\n\nFocus Area: Agentic Workflow Automation, Policy RAG, & Human-in-the-Loop Safeguards."
+    )
+
+
+# ==========================================
+# HEADER SECTION
+# ==========================================
+st.markdown(
+    "<div class='main-header'>Aetna SmartClaim AI | Enterprise Claims Decision-Support</div>",
+    unsafe_allow_html=True,
+)
+st.markdown(
+    "<div class='sub-header'>Powered by Policy Retrieval-Augmented Generation (RAG) & Agentic Reasoning Engines</div>",
+    unsafe_allow_html=True,
+)
+
+tab1, tab2, tab3 = st.tabs(
+    [
+        "📋 Agentic Claims Copilot (Ops)",
+        "⚡ Auto-Adjudication & Guardrails",
+        "📊 Executive Observability (AVP)",
+    ]
+)
+
+
+# ==========================================
+# TAB 1: AGENTIC CLAIMS COPILOT
+# ==========================================
+with tab1:
+    st.markdown("### 1. Select or Input Claim Information")
+
+    col_input1, col_input2 = st.columns([1, 1])
+
+    with col_input1:
+        selected_sample = st.selectbox(
+            "Select Pre-loaded Test Scenario:", list(MOCK_CLAIMS.keys())
+        )
+        claim_data = MOCK_CLAIMS[selected_sample]
+
+        claim_id = st.text_input("Claim ID", claim_data["claim_id"])
+        patient_info = st.text_input("Patient Info", claim_data["patient"])
+        cpb_selection = st.selectbox(
+            "Target Policy Bulletin (CPB)",
+            list(AETNA_CPBS.keys()),
+            index=list(AETNA_CPBS.keys()).index(claim_data["cpb"]),
+        )
+
+    with col_input2:
+        cpt_code = st.text_input("Procedure / CPT Code", claim_data["cpt"])
+        diag_code = st.text_input("Diagnosis Code", claim_data["diagnosis"])
+        clinical_notes = st.text_area(
+            "Clinical Progress Notes & Unstructured EHR Data:",
+            claim_data["clinical_notes"],
+            height=120,
+        )
+
+    run_btn = st.button("🚀 Run Agentic Claim Evaluation", type="primary")
+
+    if run_btn:
+        with st.spinner(
+            "Agent retrieving Aetna CPB guidelines and executing semantic policy matching..."
+        ):
+            time.sleep(1.2)  # Simulate agent reasoning latency
+
+            # Dynamic logic evaluation
+            confidence = claim_data["confidence"]
+            status = claim_data["expected_status"]
+            citation = claim_data["citation"]
+
+            # Store in session state for audit log
+            st.session_state.processed_claims.append(
+                {
+                    "timestamp": time.strftime("%H:%M:%S"),
+                    "claim_id": claim_id,
+                    "cpb": cpb_selection,
+                    "status": status,
+                    "confidence": confidence,
+                    "route": "Straight-Through (Auto)"
+                    if confidence >= auto_adj_threshold
+                    else "HITL Specialist Queue",
+                }
+            )
+
+        st.divider()
+        st.markdown("### 2. Agentic Reasoning & Policy Matching Results")
+
+        res_col1, res_col2, res_col3 = st.columns([1, 1, 1])
+
+        with res_col1:
+            st.markdown("**System Recommendation:**")
+            if status == "APPROVED" and confidence >= auto_adj_threshold:
+                st.markdown(
+                    "<span class='badge-pass'>AUTO-APPROVE (STP)</span>",
+                    unsafe_allow_html=True,
+                )
+            elif status == "PEND_DOCS":
+                st.markdown(
+                    "<span class='badge-review'>PEND FOR ADDITIONAL DOCUMENTS</span>",
+                    unsafe_allow_html=True,
+                )
+            else:
+                st.markdown(
+                    "<span class='badge-review'>REFER TO HUMAN SPECIALIST</span>",
+                    unsafe_allow_html=True,
+                )
+
+        with res_col2:
+            st.markdown("**Model Confidence Score:**")
+            st.metric(
+                label="Confidence",
+                value=f"{int(confidence*100)}%",
+                delta=f"{'+' if confidence >= auto_adj_threshold else '-'}{abs(round((confidence - auto_adj_threshold)*100, 1))}% vs Threshold",
+            )
+
+        with res_col3:
+            st.markdown("**Workflow Routing:**")
+            if confidence >= auto_adj_threshold:
+                st.success("✅ Straight-Through Processing (Zero Human Touch)")
+            else:
+                st.warning(
+                    "⚠️ Routed to Human Specialist Queue (Below Risk Threshold)"
+                )
+
+        st.markdown("#### 📜 Policy Line-Item Evidence Citations")
+        st.info(f"**CPB Citation Analysis:** {citation}")
+
+        # Human-in-the-Loop (HITL) Action Panel
+        st.markdown("#### 🛠️ Specialist Human-in-the-Loop Actions")
+        action_col1, action_col2, action_col3 = st.columns(3)
+
+        with action_col1:
+            if st.button("✅ Confirm & Approve Claim"):
+                st.success(
+                    f"Claim {claim_id} approved by Specialist. Adjudication logged."
+                )
+
+        with action_col2:
+            if st.button("✉️ Draft Pre-filled Request Letter"):
+                st.text_area(
+                    "Generated Provider Outreach Letter:",
+                    f"Dear Provider,\n\nRegarding Claim {claim_id} for {cpt_code}, our automated review against Aetna {cpb_selection} indicates missing required documentation:\n- {citation}\n\nPlease submit clinical records within 14 days.\n\nSincerely,\nAetna Clinical Operations",
+                    height=130,
+                )
+
+        with action_col3:
+            if st.button("❌ Issue Prior Auth Denial Notice"):
+                st.error(
+                    f"Denial Notice initiated for {claim_id} per {cpb_selection} non-compliance."
+                )
+
+
+# ==========================================
+# TAB 2: AUTO-ADJUDICATION & GUARDRAILS
+# ==========================================
+with tab2:
+    st.markdown("### Dynamic Risk & Threshold Impact Simulator")
+    st.caption("Evaluate how adjusting confidence guardrails impacts operational throughput vs audit risk.")
+
+    sim_col1, sim_col2 = st.columns([1, 2])
+
+    with sim_col1:
+        st.markdown("#### Simulation Control")
+        total_daily_volume = st.number_input(
+            "Daily Claim Volume:", value=25000, step=1000
+        )
+        avg_cost_per_manual = st.number_input(
+            "Cost per Manual Claim Review ($):", value=14.50, step=0.50
+        )
+
+        # Simulation Logic based on threshold
+        # Higher threshold -> lower auto-adjudication %, but near-zero error rate
+        stp_rate = max(0.20, min(0.85, 1.25 - (auto_adj_threshold * 0.8)))
+        auto_volume = int(total_daily_volume * stp_rate)
+        manual_volume = total_daily_volume - auto_volume
+        daily_savings = auto_volume * avg_cost_per_manual
+
+        st.metric(
+            "Simulated Auto-Adjudication (STP) Rate", f"{round(stp_rate*100, 1)}%"
+        )
+        st.metric(
+            "Projected Annual Operating Savings",
+            f"${daily_savings * 260:,.0f}",
+        )
+
+    with sim_col2:
+        st.markdown("#### Daily Volume Distribution Forecast")
+        fig_pie = px.pie(
+            values=[auto_volume, manual_volume],
+            names=["Auto-Adjudicated (AI)", "Manual Specialist Review (HITL)"],
+            color_discrete_sequence=["#28A745", "#FFC107"],
+            hole=0.4,
+        )
+        fig_pie.update_layout(margin=dict(t=20, b=20, l=20, r=20), height=300)
+        st.plotly_chart(fig_pie, use_container_width=True)
+
+    st.divider()
+    st.markdown("### Platform Guardrails & Safety Controls")
+
+    g_col1, g_col2, g_col3 = st.columns(3)
+    with g_col1:
+        st.markdown("#### 🔒 Anti-Hallucination")
+        st.caption(
+            "Pydantic strict schema parsing enforces structured JSON output with mandated CPB paragraph citations."
+        )
+    with g_col2:
+        st.markdown("#### ⚖️ Compliance & HIPAA")
+        st.caption(
+            "De-identification pipelines strip PHI before prompt embedding; audit logging records all LLM inference seeds."
+        )
+    with g_col3:
+        st.markdown("#### 🔄 Model Drift Monitoring")
+        st.caption(
+            "Continuous monitoring flags discrepancies between specialist override patterns and agent recommendations."
+        )
+
+
+# ==========================================
+# TAB 3: EXECUTIVE OBSERVABILITY (AVP VIEW)
+# ==========================================
+with tab3:
+    st.markdown("### Executive Dashboard | Analytics & Behavior Change (A&BC)")
+
+    # Key Performance Indicators
+    kpi1, kpi2, kpi3, kpi4 = st.columns(4)
+    kpi1.metric("Current STP Rate", "68.4%", "+14.2% YoY")
+    kpi2.metric("Average Handle Time (AHT)", "2.3 min", "-11.8 min")
+    kpi3.metric("First-Pass Accuracy", "99.1%", "+1.8%")
+    kpi4.metric("Specialist Override Rate", "3.2%", "-0.8%")
+
+    st.divider()
+
+    st.markdown("### Platform Performance Trends")
+    chart_col1, chart_col2 = st.columns(2)
+
+    with chart_col1:
+        st.markdown("#### Weekly Processing Volume vs Manual Touch Points")
+        weeks = [f"Week {i}" for i in range(1, 9)]
+        df_trends = pd.DataFrame(
+            {
+                "Week": weeks,
+                "Auto-Adjudicated": [
+                    12000,
+                    13500,
+                    14200,
+                    15800,
+                    16500,
+                    17200,
+                    18100,
+                    19000,
+                ],
+                "Specialist Review": [
+                    8000,
+                    7200,
+                    6800,
+                    5900,
+                    5200,
+                    4800,
+                    4200,
+                    3800,
+                ],
+            }
+        )
+        fig_bar = px.bar(
+            df_trends,
+            x="Week",
+            y=["Auto-Adjudicated", "Specialist Review"],
+            color_discrete_map={
+                "Auto-Adjudicated": "#002B49",
+                "Specialist Review": "#CC0000",
+            },
+            barmode="stack",
+        )
+        fig_bar.update_layout(height=320, margin=dict(t=20, b=20, l=20, r=20))
+        st.plotly_chart(fig_bar, use_container_width=True)
+
+    with chart_col2:
+        st.markdown("#### Agent vs Specialist Agreement Rate (By Clinical Category)")
+        df_agree = pd.DataFrame(
+            {
+                "Category": [
+                    "Radiology (MRI/CT)",
+                    "Bariatric / Surgery",
+                    "Oncology",
+                    "Orthopedics",
+                    "Cardiology",
+                ],
+                "Agreement Rate (%)": [98.2, 94.5, 99.1, 96.4, 97.8],
+            }
+        )
+        fig_gauge = px.bar(
+            df_agree,
+            x="Agreement Rate (%)",
+            y="Category",
+            orientation="h",
+            color="Agreement Rate (%)",
+            color_continuous_scale="Reds",
+        )
+        fig_gauge.update_layout(height=320, margin=dict(t=20, b=20, l=20, r=20))
+        st.plotly_chart(fig_gauge, use_container_width=True)
+
+    st.markdown("### Real-time Session Audit Log")
+    if st.session_state.processed_claims:
+        st.dataframe(
+            pd.DataFrame(st.session_state.processed_claims),
+            use_container_width=True,
+        )
+    else:
+        st.info("No claims processed in current session. Run a claim evaluation in Tab 1 to see real-time audit logging.")