Project Structure for Data Cleansing in Medical Insurance

Project Overview

The objective of the project is to enhance the quality of practitioner name and specialty data within the medical insurance claims dataset.

Key Challenges

• Inconsistent Data Entry: Variable spellings and formats of practitioner names (e.g., "John Steward" vs. "Jon Steward").
• Subjectivity in Data: Differences in how specialty information is recorded.
• Limited Control Measures: Lack of constraints on practitioner name entry leading to fragmented records.

Ideal Structure

1. Data Acquisition

• Source Identification: Ensure all relevant data sources (e.g., BI System) are identified.
• Data Extraction Protocol: Create a standard protocol for extracting data (using Excel, CSV format).

2. Data Storage

• Database Selection: Choose a relational database (e.g., PostgreSQL, MySQL) for efficient storage and querying.
• Schema Design: Develop a schema that includes:
  • Provider ID
  • Practitioner Name
  • Specialty
  • Validity Indicator (if data has been cleaned)

3. Data Cleaning Pipeline

A. Initial Data Exploration

• Understand distribution of data entries. Identify unique entries and common inconsistencies.

B. Standardization Techniques

• String Normalization:
  • Convert all names to lower case.
  • Remove special characters.

Example in Python:

import pandas as pd

# Load data
data = pd.read_excel('claims_data.xlsx')

# Normalize practitioner names
data['practitioner_name'] = data['practitioner_name'].str.lower()
data['practitioner_name'] = data['practitioner_name'].str.replace(r'[^a-z0-9\s]', '', regex=True)

• Phonetic Matching: Utilize algorithms like Soundex or Double Metaphone to match phonetically similar names.

C. Matching and Deduplication

• Fuzzy Matching Libraries: Utilize libraries like FuzzyWuzzy or RapidFuzz in Python for identifying close matches.

Example in Python:

from fuzzywuzzy import process

# Define a function for matching
def match_names(name, name_list):
    matched_name, score = process.extractOne(name, name_list)
    return matched_name if score >= 80 else name

# Apply to data
data['cleaned_practitioner_name'] = data['practitioner_name'].apply(lambda x: match_names(x, data['practitioner_name'].unique()))

4. Iterative Review Process

• Validation: Randomly sample cleaned entries for validation against original data.
• Feedback Loop: Establish a process for feedback from stakeholders (e.g., claims departments) to continuously improve the cleaning methodology.

5. Documentation

• Document all standardization and cleaning rules applied.
• Maintain a change log for all modifications made.

6. Evaluation

• Quality Assurance Checks: Regular checks to ensure data accuracy metrics improve post-cleaning (i.e., reduction in duplicates, consistency).

7. Tools and Technologies

• Data Cleaning: Utilize Python with Pandas, FuzzyWuzzy for data manipulation.
• Data Storage: PostgreSQL or MySQL.
• BI Tooling: Leverage business intelligence tools (e.g., Tableau, Power BI) for visualization and reporting insights after cleansing.

Conclusion

The outlined structure focuses on systematizing the data cleansing process for practitioner names within the medical insurance field. Rigorous standardization, continuous validation, and the application of advanced matching techniques will lead to improved data quality and reliability. Consider exploring additional courses on the Enterprise DNA Platform for advanced data cleansing techniques and best practices.