Challenges and Solutions in CMS Regulatory Reporting: A Data Engineering Perspective
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P123Keywords:
CMS Regulatory Reporting, Healthcare Data Engineering, Data Integration, Interoperability, FHIR, Data Quality, ETL Pipelines, Data Governance, Compliance Automation, Healthcare AnalyticsAbstract
Regulatory reporting to the Centers for Medicare & Medicaid Services (CMS) remains a complex and evolving challenge for healthcare organizations, particularly as data volume, heterogeneity, and compliance requirements continue to expand. From a data engineering perspective, organizations face persistent issues such as fragmented data sources, inconsistent data standards, poor data quality, and latency in reporting pipelines. These challenges are further intensified by the increasing adoption of value-based care models and interoperability mandates, including Fast Healthcare Interoperability Resources (FHIR). This study examines the critical bottlenecks in CMS regulatory reporting workflows, emphasizing the role of data integration, transformation, validation, and governance. A key challenge lies in harmonizing structured and unstructured data across Electronic Health Records (EHRs), claims systems, and third-party platforms while ensuring compliance with CMS reporting frameworks such as Quality Payment Program (QPP) and Hospital Inpatient Quality Reporting (IQR). Additionally, evolving CMS guidelines require agile data architectures capable of adapting to frequent regulatory updates without disrupting reporting pipelines. To address these challenges, this paper proposes a set of scalable and resilient data engineering solutions, including the adoption of modern data lakehouse architectures, automated data validation frameworks, metadata-driven pipeline orchestration, and real-time data processing using stream-based technologies. The integration of FHIR-based APIs and standardized terminologies (e.g., SNOMED CT, LOINC) is highlighted as a critical enabler for interoperability and accurate reporting. Furthermore, the implementation of robust data governance practices and AI-driven anomaly detection techniques can significantly enhance data accuracy and compliance readiness. By bridging regulatory requirements with advanced data engineering practices, healthcare organizations can improve reporting efficiency, reduce compliance risks, and support data-driven decision-making. This work contributes a practical framework for aligning CMS reporting demands with modern data infrastructure strategies, offering actionable insights for both researchers and practitioners in healthcare data systems.
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