Transformer-Based Health Record Summarisation with Auditable Source Attribution: A Cross-Site Validation Agenda

Abstract

Clinical summarisation systems can sound convincing while omitting decisive events or attributing facts to the wrong encounter. The difficulty is particularly visible when a system is transferred to a new setting. The objective is to measure whether source-linked transformer summaries reduce unverifiable statements without sacrificing important clinical detail. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The proposed method is a blinded assessment of de-identified clinical narratives paired with evidence passages and structured omission/error rubrics. Comparators will include ordinary abstractive summaries, retrieval-supported summaries and provenance-constrained summaries. 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. The cross-site extension holds out an entire institution or deployment location rather than relying only on random test splits. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are claim-level factual accuracy, critical omission rate, evidence traceability and clinician review time. Potential biases include ambiguous documentation, conflicting observations, retrieval gaps and variations in note length. Between-site heterogeneity would be examined through prespecified subgroup estimates and transparent domain-shift diagnostics. 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].