Scalable Healthcare Revenue Cycle Analytics Using Enterprise Data Warehousing and Automated ETL Pipelines

  • Authors

    • Bhargavaram Potharaju Senior Consultant, Oracle Columbus, Ohio, USA. Author
    • Mahesh Addanki Technical Manager, Tata Consulting Services Hyderabad, Telangana, India. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2020PII9M3V

    Published 08-03-2020

  • Healthcare Analytics, Revenue Cycle Management, Accounts Receivable Reporting, Enterprise Data Warehouse, ETL Automation, Data Cleansing, Dashboard Optimization, Claim Aging, Payer Analytics, Cash Forecasting

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

    How to Cite

    [1]
    B. Potharaju and M. Addanki, “Scalable Healthcare Revenue Cycle Analytics Using Enterprise Data Warehousing and Automated ETL Pipelines”, IJDEIC, vol. 3, no. 2, pp. 01–21, Aug. 2020, doi: 10.67228/30715717/IJDEIC-2020PII9M3V.

  • Abstract

    Today’s healthcare organizations are fragmented, heterogeneous systems that generate huge amounts of operational, clinical and financial data, making it a major challenge to get full visibility of revenue cycle management (RCM). Poor RCM results in claim denials, delayed payments, revenue loss, and high operational costs. To address these challenges, in this paper we proposed a scalable healthcare revenue cycle analytics framework using Enterprise Data Warehousing (EDW) and automated Extract, Transform, Load (ETL) pipelines. The proposed system automatically extracts the raw data from different health information systems, cleans and standardizes the data using advanced transformations, and structures the data in a star-schema dimensional data model. The centralized architecture powers dashboards for real-time monitoring of KPIs and uses machine learning models to predict revenue using historical financial data. Performance evaluation shows that the ETL process runs much faster than the traditional manual process and the data consistency and reporting efficiency are improved significantly. In the end, this framework offers a dependable, data-driven basis that enhances audit readiness, accelerates billing processes, diminishes claims denials and supports the long-term financial viability of healthcare providers.

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