Cross Payload Data Quality

[raphaelvp]

• Validate each individual payload both structurally and semantically to ensure it adheres to expected implementation guides and clinical data standards. This includes checking structural elements, required fields, data types, coding systems, and ensuring clinical concepts are logically sound within a single message.

• Correlate and validate data across different message formats such as, Flat File (CSV) HL7v2, CCDA, and FHIR to create a unified clinical and administrative narrative. By linking related events, encounters, and patient information across disparate payload types, the system ensures consistency and enables more advanced multi-format validation.

• Detect all relevant forms of duplication, including structural duplicates that share identical message formatting, content-based duplicates that represent the same clinical information despite structural differences, and cross-payload duplicates where equivalent data appears across Flat File (CSV) HL7v2, CCDA, and FHIR messages. This ensures the platform can suppress redundant data and prevent inaccurate clinical or operational interpretations.

• Provide consistent ingestion outcomes through a standardized response model, incorporating detailed OperationOutcome entries that clearly describe validation results. These outcomes must include error, warning, and informational messages that help analysts, QEs, and developers understand why a message passed, failed, or required special handling.

• Support specification of rules at the QE or tenant level, enabling different organizations to apply customized rules based on their requirements or implementation timelines. Support rule versioning capability which allows for smooth transitions when rules are updated and ensures that validation can be tailored without impacting other tenants.

• Provide scoring and analytics reporting to quantify data quality, highlight trends, detect recurring issues, and offer insights into QE or provider performance. Determine the analytics required for compliance monitoring, improvement initiatives, and transparency into the overall health of the data ingestion pipeline.