Cross-Site Validation of Clinical Prediction Models on Cloud Data Warehouses: A Mixed-Evidence Evaluation

Abstract

Cloud-hosted clinical data pipelines can harmonise hospital records while obscuring differences in coding, missingness and data provenance. The difficulty is particularly visible when a system is transferred to a new setting. The central research objective is to evaluate the transferability of prediction models across institutions using a traceable data-warehouse workflow. Previous work provides context for sampling, input validation and study boundaries [3–7]. To answer the question, the study proposes a multi-institution protocol with source-hospital development data, held-out partner sites and time-based external validation. Enterprise-storage sources are used to specify constraints on data availability and provenance [1], with complementary infrastructure evidence defining practical recovery safeguards [2]. These citations are not treated as clinical effectiveness evidence. Under matched evaluation conditions, the protocol compares local versus pooled models after identical coding rules, feature windows and missing-data checks. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The mixed-evidence component combines quantitative tests with an independently coded practitioner evaluation. Performance will be judged using calibration-in-the-large, discrimination, subgroup error and reproducibility of extracted cohorts. Interpretation will account for data lineage, hospital-specific coding shifts, delayed ingestion and governance of cross-site access. Differences between numerical metrics and professional judgements would be retained in the interpretation. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].