data_generator.py

import pandas as pd
import numpy as np
import os
import random
from datetime import datetime, timedelta

def create_data_folder():
    if not os.path.exists('Data'):
        os.makedirs('Data')

def generate_claims_data(n=20000):
    np.random.seed(42)
    service_lines = ['Cardiology', 'Pulmonology', 'Orthopedics', 'Neurology', 'General Surgery', 'Internal Medicine', 'Oncology', 'Endocrinology', 'Gastroenterology']
    drgs = np.random.randint(100, 999, size=200)
    
    # Base data
    total_charges = np.random.uniform(5000, 150000, n)
    service_line_choices = [random.choice(service_lines) for _ in range(n)]
    complexity_choices = np.random.choice(['MCC', 'CC', 'Non-CC'], n, p=[0.25, 0.45, 0.3])
    
    # Logic-based Denial Generation for ML Robustness
    # 1. High charges > 80k have high denial risk
    # 2. Oncology and Cardiology have higher base risk
    # 3. MCC complexity significantly adds risk
    is_denied = []
    for i in range(n):
        risk = 0.02 # Baseline 2%
        if total_charges[i] > 80000: risk += 0.45
        if service_line_choices[i] == 'Oncology': risk += 0.25
        if service_line_choices[i] == 'Cardiology': risk += 0.15
        if complexity_choices[i] == 'MCC': risk += 0.20
        
        # Clip risk and sample
        is_denied.append(1 if random.random() < min(risk, 0.95) else 0)

    data = {
        'Claim_ID': [f'CLM{100000+i}' for i in range(n)],
        'Patient_ID': [f'PT{random.randint(1, 5000)}' for i in range(n)],
        'Service_Line': service_line_choices,
        'DRG_Code': [random.choice(drgs) for _ in range(n)],
        'Admission_Date': [datetime(2023, 1, 1) + timedelta(days=random.randint(0, 700)) for _ in range(n)],
        'Primary_Diagnosis': [f'I{random.randint(10, 99)}' for _ in range(n)],
        'Total_Charges': total_charges,
        'Reimbursement': np.random.uniform(2000, 120000, n),
        'Is_Denied': is_denied,
        'Complexity_Level': complexity_choices
    }
    
    df = pd.DataFrame(data)
    df.to_csv('Data/claims.csv', index=False)
    print(f"Created Data/claims.csv with {n} rows and ML patterns")

def generate_cms_rules(n=250):
    rule_types = ['DRG_Logic', 'CC_MCC_Update', 'Coding_Addition', 'HCC_Revisions', 'Payment_Policy', 'Quality_Penalty', 'Telehealth', 'Site_of_Care', 'OPPS_Bundling', 'NCD_LCD_Update', 'Value_Based_Program']
    targets = ['Cardiology', 'Pulmonology', 'Orthopedics', 'Neurology', 'General Surgery', 'Internal Medicine', 'Oncology', 'Endocrinology', 'Gastroenterology', 'Urology', 'Nephrology']
    changes = ['Weight Decrease', 'Weight Increase', 'Reclassification', 'New CPT Codes', 'Weight Shift', 'Site-of-care shift', 'Inclusion Shift', 'Readmission Adjustment', 'Rate Standardization', 'APC Bundling', 'Coverage Determination', 'Penalty Increase']
    
    rules = []
    # 1. Orthopedic Bundle (User Example 1)
    rules.append({
        'Rule_ID': 'R2025_BUND_01',
        'Type': 'OPPS_Bundling',
        'Target': 'Orthopedics',
        'Change': 'APC Bundling',
        'Impact_Score': 0.85,
        'Description': "CMS 2025 OPPS Update: Orthopedic supply costs (HCPCS C1713) are now 'packaged' into APC 5114 flat fee. Separate pass-through billing is no longer permitted."
    })
    
    # 2. Sepsis Reclassification (User Demo Question)
    rules.append({
        'Rule_ID': 'R2025_SEPSIS_02',
        'Type': 'DRG_Logic',
        'Target': 'Internal Medicine',
        'Change': 'Reclassification',
        'Impact_Score': 0.78,
        'Description': "2025 Sepsis Reclassification: Sepsis with Major Complications (MCC) now requires documented 'Organ System Failure' for DRG 871. Failure to document results in downcode to DRG 872, risking $4,200 loss per case."
    })

    # 3. TKA Outpatient Shift
    rules.append({
        'Rule_ID': 'R2025_TKA_03',
        'Type': 'Site_of_Care',
        'Target': 'Orthopedics',
        'Change': 'Site-of-care shift',
        'Impact_Score': 0.92,
        'Description': "2025 TKA Shift: Total Knee Arthroplasty (TKA) procedures are being shifted from Inpatient (IPPS) to Outpatient (OPPS) for healthy populations. Projected reimbursement drop from $14k to $9k per case."
    })

    # 4. New Code Addition (Requirement 3B)
    rules.append({
        'Rule_ID': 'R2025_CODE_05',
        'Type': 'Coding_Addition',
        'Target': 'Cardiology',
        'Change': 'New CPT Codes',
        'Impact_Score': 0.45,
        'Description': "CMS 2025 update adds new descriptor codes for remote cardiac monitoring. $250 increase in reimbursement per patient per month for eligible heart failure cases."
    })

    # 5. NCD Coverage Policy (Requirement 1)
    rules.append({
        'Rule_ID': 'R2025_NCD_06',
        'Type': 'NCD_LCD_Update',
        'Target': 'Neurology',
        'Change': 'Coverage Determination',
        'Impact_Score': 0.72,
        'Description': "New National Coverage Determination (NCD) for Alzheimer's therapeutics. Stringent clinical criteria for coverage must be met for Medicare payment eligibility."
    })

    # 6. Value-Based Penalty (Requirement 3E)
    rules.append({
        'Rule_ID': 'R2025_VBP_07',
        'Type': 'Value_Based_Program',
        'Target': 'General Surgery',
        'Change': 'Penalty Increase',
        'Impact_Score': 0.55,
        'Description': "MSSP Shared Savings Update: Increased weighting for surgical site infection metrics. Organizations in the bottom 25th percentile face up to 2% billing reduction."
    })

    for i in range(n - 6):
        rule_type = random.choice(rule_types)
        target = random.choice(targets)
        change = random.choice(changes)
        impact = round(random.uniform(0.1, 0.95), 2)
        
        rules.append({
            'Rule_ID': f'R2025_{100+i}',
            'Type': rule_type,
            'Target': target,
            'Change': change,
            'Impact_Score': impact,
            'Description': f"2025 {rule_type} update for {target}. Focus on {change} with a systemic impact of {impact*100}%."
        })
        
    df = pd.DataFrame(rules)
    df.to_csv('Data/cms_rules_2025.csv', index=False)
    print(f"Created Data/cms_rules_2025.csv with {n} rules")

def generate_chargemaster(n=2500):
    """Generates Temple's Chargemaster (CDM) table with batch records."""
    service_lines = ['Orthopedics', 'Cardiology', 'Pulmonology', 'Oncology', 'Internal Medicine', 'Neurology', 'General Surgery', 'Endocrinology', 'Gastroenterology']
    
    codes = []
    descriptions = []
    sl_list = []
    status_list = []
    price_list = []
    
    # 1. Inject the "Conflict Case" (Requirement: "Revenue Loss from Missing a CMS Code Change")
    # At least 15% of records will be related to our "Orthopedic Bundling" example
    for i in range(350):
        codes.append(f'HCPCS_C1713_{i}')
        descriptions.append('Orthopedic Implant (Plate/Screw) - Unit A')
        sl_list.append('Orthopedics')
        status_list.append('Pass-Through') # THE CONFLICT: Should be 'Packaged'
        price_list.append(7000)
        
    # 2. Add other Orthopedic codes
    for i in range(400):
        codes.append(f'CPT_27447_{i}')
        descriptions.append('Total Knee Arthroplasty (TKA)')
        sl_list.append('Orthopedics')
        status_list.append('Active')
        price_list.append(15000)

    # 3. Fill the rest with random data
    for i in range(n - len(codes)):
        sl = random.choice(service_lines)
        codes.append(f'CODE_{10000+i}')
        descriptions.append(f'Hospital Procedure/Supply {i}')
        sl_list.append(sl)
        status_list.append(random.choice(['Active', 'Pass-Through', 'Inactive']))
        price_list.append(random.randint(50, 45000))
        
    data = {
        'CDM_Code': codes,
        'Description': descriptions,
        'Service_Line': sl_list,
        'Status': status_list,
        'Base_Charge': price_list
    }
    df = pd.DataFrame(data)
    df.to_csv('Data/chargemaster.csv', index=False)
    print(f"Created Data/chargemaster.csv with {len(df)} records")

def generate_hcc_weights():
    conditions = [
        'Diabetes w/ Complications', 'COPD', 'CHF', 'End-Stage Renal Disease', 
        'Major Depressive Disorder', 'Morbid Obesity', 'Rheumatoid Arthritis', 
        'Vascular Disease', 'Cerebral Palsy', 'Multiple Sclerosis',
        'HIV/AIDS', 'Metastatic Cancer', 'Septicemia', 'Drug/Alcohol Use Disorder',
        'Paraplegia', 'Intestinal Obstruction', 'Severe Hematological Disorders',
        'Heart Failure', 'Acute Myocardial Infarction', 'Angina Pectoris'
    ]
    data = {
        'HCC_Code': [f'HCC{i}' for i in range(1, len(conditions) + 1)],
        'Description': conditions,
        'Old_Weight': np.random.uniform(0.1, 2.5, len(conditions)),
        'New_Weight': np.random.uniform(0.1, 2.5, len(conditions))
    }
    df = pd.DataFrame(data)
    df.to_csv('Data/hcc_weights.csv', index=False)
    print("Created Data/hcc_weights.csv")

def generate_denials_data(n=3000):
    """Generates sample denial data for executive summary KPIs."""
    np.random.seed(99)
    statuses = ['Paid', 'Denied', 'Open', 'Appealed', 'Partially Paid']
    service_lines = ['Cardiology', 'Pulmonology', 'Orthopedics', 'Neurology', 'Surgery', 'Medicine', 'Oncology', 'Endocrinology', 'Gastroenterology']
    
    data = {
        'Denial_ID': [f'DEN{50000+i}' for i in range(n)],
        'Claim_ID': [f'CLM{200000+i}' for i in range(n)],
        'Service_Line': [random.choice(service_lines) for _ in range(n)],
        'Status': [random.choice(statuses) for _ in range(n)],
        'Denied_Amount': np.random.uniform(500, 25000, n),
        'Reason': [f'Reason Code {random.randint(1, 45)}' for _ in range(n)]
    }
    df = pd.DataFrame(data)
    df.to_csv('Data/sample_denials_3000.csv', index=False)
    print("Created Data/sample_denials_3000.csv")

if __name__ == '__main__':
    # Bypass bottleneck bottleneck
    os.environ['BOTTLENECK_DISABLE'] = '1'
    create_data_folder()
    generate_claims_data()
    generate_cms_rules()
    generate_chargemaster()
    generate_hcc_weights()
    generate_denials_data()