Engineering Data Quality in Healthcare Systems: A Proactive Testing Approach for Medicare Compliance

Abstract

Healthcare systems supporting Medicare operate within highly regulated environments where data quality directly influences compliance, financial accuracy, and service delivery. Conventional methods treat data validation as a secondary process, which does not account for the complexities of modern distributed data structures. This article considers data quality as an essential engineering tier that spans the entire data life cycle. In this approach, the validation process is designed in order to comply with requirements, including requirements of completeness, traceability, and auditability. Reactive data testing methods present limitations, such as delayed identification of problems, inability to validate data transformations, and scalability challenges when using cloud-based systems. In response to these weaknesses, a new proactive architecture for data quality engineering is introduced, which includes source-level validation, transformation-level validation, and reconciliation across levels. The transition of quality assurance from a process to a strategic function is established. The implementation of data quality as an architectural component significantly improves the performance of error identification and auditing and increases system reliability. Therefore, it becomes a necessary condition for healthcare data management.