Transformer-Based Health Record Summarisation with Auditable Source Attribution

Abstract

Clinical summarisation systems can sound convincing while omitting decisive events or attributing facts to the wrong encounter. An evaluation that overlooks these constraints may reach conclusions that cannot be reproduced. The planned investigation seeks 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 protocol specifies 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. 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 design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. 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. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. 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].