Data Quality Contracts for Multi-Cloud Healthcare: Semantic SLAs with Automated Remediation
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I1P125Keywords:
Data Quality Contracts, Semantic Slas, Multi-Cloud Healthcare, Automated Remediation, Fhir, Data Governance, Data Observability, Error Budgets, Chaos Engineering, Quality-As-A-ServiceAbstract
Multi-cloud healthcare platforms distribute ingestion, transformation, and analytics across Amazon Web Services, Microsoft Azure, and Google Cloud Platform to improve elasticity, regional resilience, and regulatory alignment. In practice, these benefits are frequently undermined by silent data-quality failures schema drift, terminology misalignment, freshness regressions, and broken references that propagate across heterogeneous pipeline components and destabilize clinical analytics and downstream AI systems. Existing approaches remain largely reactive: static checks and ad hoc expectations detect issues after propagation, while cloud provider SLAs emphasize infrastructure availability rather than semantic correctness or temporal fitness-for-use. This paper proposes Data Quality Contracts (DQCs) as semantic SLAs operationalized as SLOs with error budgets for multi-cloud healthcare data products. A DQC is a versioned, machine-readable agreement between producers and consumers specifying: (i) FHIR-aware HL7 semantic constraints (profiles, cardinalities, terminology binding), (ii) temporal objectives (freshness and end-to-end latency), (iii) completeness and referential integrity targets, and (iv) error budgets that bound acceptable quality loss and trigger escalation. We introduce a cloud-agnostic Quality-as-a-Service (QaaS) control plane that manages contract lifecycle and error-budget accounting while enforcing contracts federatedly at ingestion boundaries to minimize cross-cloud protected health information movement. When violations occur, the control plane orchestrates policy-driven remediation workflows schema reconciliation, terminology synchronization, prioritized replay, cache invalidation, and referential repair with approval gates for high-severity or low-confidence actions. We evaluate a prototype using a synthetic FHIR-compatible workload of 750,000 patients generated with Synthea across three clouds over 45 simulated days, with data-quality chaos injections spanning schema drift, vocabulary misalignment, freshness degradation, completeness regressions, and referential integrity faults. Relative to three baselines (reactive monitoring, expectations-based validation, and FHIR validator workflows), DQCs reduce mean time to remediation from 168.6 to 10.2 minutes (p<0.001), reduce false-positive alert rate from 26.6% to 14.3% (p<0.001), maintain 93.8% aggregate contract compliance during chaos, and automatically resolve 82% of violations. A 12-month operational cost model (assumptions in Section VIII) indicates reduced incident-driven toil and downtime exposure versus reactive approaches. These results suggest that contract-driven, semantics-aware governance with error budgets and automated remediation provides a practical foundation for trustworthy multi-cloud healthcare analytics and AI.
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