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Analyzes and prints statistical information about products involved in severe timing issues, including product frequency counts and their associated diagnostic classes.

Purpose

This function provides a detailed analysis of problematic products in a healthcare/medical treatment context. It identifies the top 15 products most frequently involved in severe timing issues and examines the diagnostic classes associated with the top 5 most problematic products. The output helps identify patterns and relationships between specific products and diagnostic categories in cases with timing problems.

Source Code

def analyze_problematic_products(severe_cases):
    """Analyze products involved in timing issues."""
    print("\nPROBLEMATIC PRODUCTS ANALYSIS")
    print("-" * 40)
    
    product_counts = severe_cases['ProductCD'].value_counts()
    print("Top 15 products in severe timing issues:")
    for i, (product, count) in enumerate(product_counts.head(15).items(), 1):
        print(f"{i:2d}. {product}: {count} treatments")
    
    # Check if there are patterns in diagnostic classes for these products
    print("\nDiagnostic classes for top problematic products:")
    top_products = product_counts.head(5).index
    for product in top_products:
        product_data = severe_cases[severe_cases['ProductCD'] == product]
        diag_classes = product_data['DiagnosticClass'].value_counts()
        print(f"\n  {product}:")
        for diag_class, count in diag_classes.items():
            print(f"    {diag_class}: {count}")

Parameters

Parameter Details

severe_cases: A pandas DataFrame containing records of severe timing issue cases. Must include at minimum two columns: 'ProductCD' (product code/identifier) and 'DiagnosticClass' (diagnostic classification). Each row represents a treatment case with timing issues.

Return Value

This function returns None. It produces side effects by printing analysis results directly to stdout, including: (1) a ranked list of the top 15 products by frequency of occurrence in severe cases, (2) a breakdown of diagnostic classes for each of the top 5 most problematic products.

Dependencies

• pandas

Required Imports

import pandas as pd

Usage Example

import pandas as pd

# Create sample data
severe_cases = pd.DataFrame({
    'ProductCD': ['PROD_A', 'PROD_A', 'PROD_B', 'PROD_A', 'PROD_C', 'PROD_B', 'PROD_D', 'PROD_A'],
    'DiagnosticClass': ['Class1', 'Class2', 'Class1', 'Class1', 'Class3', 'Class2', 'Class1', 'Class3'],
    'TreatmentDate': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05', '2023-01-06', '2023-01-07', '2023-01-08']
})

# Analyze problematic products
analyze_problematic_products(severe_cases)

# Output will be printed to console showing:
# - Top 15 products ranked by frequency
# - Diagnostic class breakdown for top 5 products

Best Practices

• Ensure the input DataFrame contains 'ProductCD' and 'DiagnosticClass' columns before calling this function
• The function prints directly to stdout, so consider redirecting output if you need to capture results programmatically
• For large datasets, consider filtering severe_cases before passing to this function to improve performance
• This function is designed for exploratory data analysis and reporting; consider extracting the logic into a return-based function if you need to process results programmatically
• The function assumes data has already been filtered to 'severe cases' - ensure appropriate filtering is done upstream

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