Cross-Site Validation of Clinical Prediction Models on Cloud Data Warehouses: Evidence, Failure Modes and Deployment Trade-offs

Abstract

Cloud-hosted clinical data pipelines can harmonise hospital records while obscuring differences in coding, missingness and data provenance. This gap has consequences for interpreting performance outside carefully curated evaluation settings. The planned investigation seeks to evaluate the transferability of prediction models across institutions using a traceable data-warehouse workflow. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The protocol specifies a multi-institution protocol with source-hospital development data, held-out partner sites and time-based external validation. Comparators will include local versus pooled models after identical coding rules, feature windows and missing-data checks. The technical pipeline must remain auditable: prior work on storage operations provides system-level context [1], while a second source identifies infrastructure conditions relevant to data continuity [2], rather than patient-level outcomes. The evidence-mapping component examines why a successful result in one technical environment may fail elsewhere. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are calibration-in-the-large, discrimination, subgroup error and reproducibility of extracted cohorts. Potential biases include data lineage, hospital-specific coding shifts, delayed ingestion and governance of cross-site access. Stress tests would distinguish outright failures, reduced confidence and cases where a simpler baseline is preferable. Additional research informs the prespecified stress tests and limits on interpretation [13–17]. The intended contribution is an auditable research protocol rather than an unsupported claim of effectiveness; related work frames its transferability limits [18–23].