Notes from the Healthcare Revenue Cycle Management Project Implementation Session

Chapter 1: Introduction to Healthcare Revenue Cycle Management

• Session Overview:

  • Aim: Implement end-to-end project flow for healthcare revenue cycle management using Google Cloud Platform (GCP).
  • Previous knowledge from GCP data engineering course assumed.

• Introduction to Healthcare Domain:

  • Initial interaction starts with a patient visiting a hospital.
  • Patient details (name, insurance, condition, payment info) are captured.
  • Services provided by the hospital include check-ups, treatments, and telemedicine.
  • Billing process generates bills based on the services rendered.

• Key Elements in Healthcare Revenue Cycle Management (RCM):

  • Patient Visits ➔ Service Provision ➔ Bill Generation ➔ Payments and Follow-Ups ➔ Documentation.
  • Ensures financial stability and efficiency in managing patient care and billing processes.

• Role of Data Engineer in Healthcare RCM:

  • Responsibility lies in data extraction, ETL pipeline creation, and data analysis.
  • Data engineers facilitate revenue cycle insights through dashboards and reports.

Chapter 2: Understanding Data Sources

• Data Sources:
  • EMR Data from Hospitals: Data from multiple hospitals needs to be consolidated.
  • Data types include patient records, provider information, transactions.
  • Claims Data: Frequency: Monthly basis; formatted as CSV.
  • CPT Codes: Standardized codes for procedures and diagnoses.
  • NPI Codes: Identifiers for healthcare providers to ensure valid credentials.

Chapter 3: Data Management Workflow

• Data Extraction Process:
  • Use Google Cloud Storage (GCS) as a landing layer for managing data from various sources.
  • Data will be transferred to BigQuery for analytics using Bronze-Silver-Gold architecture.

Chapter 4: Project Architecture Details

• **Architecture Structure: **
  • Data sources connected to ETL pipeline creating a medallion architecture with Bronze (raw), Silver (processed), and Gold (final reporting) stages.
  • Utilize Airflow as the orchestration tool for automating workflows in the pipeline.

Chapter 5: Incremental Data Load Process

• Incremental Load Logic:
  • Create audit tables in BigQuery to keep track of records already processed.
  • Use timestamps to retrieve only the records that have changed since the last load.

Chapter 6: Steps to Configure Data Pipeline

• Functionality Setup:
  • Functions created for reading configuration files, logging events, archiving files, and interacting with GCP services.
  • Develop Apache Spark functions to read, write, and log pipeline data into each respective layer.

Chapter 7: Error Handling and Log Management

• Error Management:
  • Including exception handling to manage cases where the expected data structure may differ.
• Logging Mechanisms:
  • Capture various events to ensure visibility over data movements and transformations.

Chapter 8: Running the Pipelines

• Execution of Data Pipeline:
  • Successful runs yield populated GCS buckets and BigQuery tables, ensuring data integrity and availability for analysis.

Chapter 9: Final Considerations

• Project Recap:
  • Reviewed critical concepts of healthcare RCM and pipeline design.
  • Acknowledge that varying data structures across sources can complicate pipeline execution.
• Next Steps:
  • Tomorrow’s session will focus on Silver and Gold stages of data processing, as well as orchestration with Airflow.
  • Implementation of CI/CD pipeline practices for data engineering tasks.