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| 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="SmartClaim AI Platform", | |
| 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, | |
| ) | |
| # ========================================== | |
| # SESSION STATE INITIALIZATION | |
| # ========================================== | |
| if "processed_claims" not in st.session_state: | |
| st.session_state.processed_claims = [] | |
| if "evaluated" not in st.session_state: | |
| st.session_state.evaluated = False | |
| if "show_letter" not in st.session_state: | |
| st.session_state.show_letter = False | |
| if "action_status" not in st.session_state: | |
| st.session_state.action_status = None | |
| if "eval_results" not in st.session_state: | |
| st.session_state.eval_results = {} | |
| # ========================================== | |
| # 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.", | |
| }, | |
| } | |
| # ========================================== | |
| # SIDEBAR CONTROL PANEL | |
| # ========================================== | |
| with st.sidebar: | |
| st.image( | |
| "https://upload.wikimedia.org/wikipedia/commons/f/f3/Health_logo.svg", | |
| width=180, | |
| ) | |
| st.markdown("### **AI Claims Platform Engine**") | |
| st.caption("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( | |
| "**Prototype**\n\nFocus Area: Agentic Workflow Automation, Policy RAG, & Human-in-the-Loop Safeguards." | |
| ) | |
| # ========================================== | |
| # HEADER SECTION | |
| # ========================================== | |
| st.markdown( | |
| "<div class='main-header'> 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(1.0) | |
| # Store in Session State | |
| st.session_state.evaluated = True | |
| st.session_state.show_letter = False | |
| st.session_state.action_status = None | |
| st.session_state.eval_results = { | |
| "claim_id": claim_id, | |
| "patient": patient_info, | |
| "cpb": cpb_selection, | |
| "cpt": cpt_code, | |
| "confidence": claim_data["confidence"], | |
| "status": claim_data["expected_status"], | |
| "citation": claim_data["citation"], | |
| } | |
| # Add to persistent audit log | |
| st.session_state.processed_claims.append( | |
| { | |
| "timestamp": time.strftime("%H:%M:%S"), | |
| "claim_id": claim_id, | |
| "cpb": cpb_selection, | |
| "status": claim_data["expected_status"], | |
| "confidence": claim_data["confidence"], | |
| "route": "Straight-Through (Auto)" | |
| if claim_data["confidence"] >= auto_adj_threshold | |
| else "HITL Specialist Queue", | |
| } | |
| ) | |
| # Render results if evaluation has occurred | |
| if st.session_state.evaluated and st.session_state.eval_results: | |
| res = st.session_state.eval_results | |
| confidence = res["confidence"] | |
| status = res["status"] | |
| 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:** {res['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.session_state.action_status = f"✅ Claim {res['claim_id']} approved by Specialist. Adjudication logged." | |
| st.session_state.show_letter = False | |
| with action_col2: | |
| if st.button("✉️ Draft Pre-filled Request Letter"): | |
| st.session_state.show_letter = True | |
| st.session_state.action_status = None | |
| with action_col3: | |
| if st.button("❌ Issue Prior Auth Denial Notice"): | |
| st.session_state.action_status = f"❌ Denial Notice initiated for {res['claim_id']} per {res['cpb']} non-compliance." | |
| st.session_state.show_letter = False | |
| # Display persistent action statuses or pre-filled letter | |
| if st.session_state.action_status: | |
| st.info(st.session_state.action_status) | |
| if st.session_state.show_letter: | |
| st.markdown("##### ✉️ Pre-filled Provider Outreach Letter") | |
| letter_text = f"Dear Provider,\n\nRegarding Claim {res['claim_id']} for {res['cpt']}, our automated review against Aetna {res['cpb']} indicates missing required documentation:\n- {res['citation']}\n\nPlease submit clinical records within 14 days.\n\nSincerely,\nAetna Clinical Operations" | |
| st.text_area("Generated Outreach Draft:", value=letter_text, height=150) | |
| # ========================================== | |
| # 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 ($):", value=14.50, step=0.50 | |
| ) | |
| stp_rate = max(0.20, min(0.85, 1.25 - (auto_adj_threshold * 0.8))) | |
| auto_volume = int(total_daily_volume * stp_rate) | |
| manual_volume = total_daily_volume - auto_volume | |
| daily_savings = auto_volume * avg_cost_per_manual | |
| st.metric( | |
| "Simulated Auto-Adjudication (STP) Rate", f"{round(stp_rate*100, 1)}%" | |
| ) | |
| st.metric( | |
| "Projected Annual Operating Savings", | |
| f"${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)") | |
| 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.") |