Cross-Site Validation of Clinical Prediction Models on Cloud Data Warehouses

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 design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. 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. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. 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].