病歷隱私被LLM補漏

Computer Science > Computation and Language arXiv:2608.

17051 (cs) [Submitted on 17 Aug 2026] Title:Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss Authors:Daniel Palacios, Matthew Brady Neeley, Angel Adetomike Otto, Shalini Dhamodharan, John P.

Woodhouse, Chi-fan Lin, Mark Zobeck, Zhandong Liu, Hyun-Hwan Jeong View a PDF of the paper titled Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss, by Daniel Palacios and Matthew Brady Neeley and Angel Adetomike Otto and Shalini Dhamodharan and John P.

Woodhouse and Chi-fan Lin and Mark Zobeck and Zhandong Liu and Hyun-Hwan Jeong View PDF HTML (experimental) Abstract:Secondary use of electronic health records requires de-identification, yet existing systems miss \emph{institutionally situated} protected health information (PHI) such as hospital abbreviations, building names, and internal codes whose status is locally determined.

We ask whether large language models (LLMs) with in-context learning (ICL) can close this gap and control the precision--recall trade-off.

On 100 annotated pediatric oncology notes (5,322 PHI spans) from Texas Children's Hospital, we benchmarked eight LLMs against two purpose-built systems (Stanford TiDE, OpenMed PII) and two pattern-based baselines.

Each LLM ran under three prompts of increasing specificity: (1) a HIPAA-aligned baseline, (2) baseline plus the institutional PHI categories it missed, and (3) prompt 2 plus instructions against over-redacting clinical content.

We then compared 14~multi-agent and ensemble configurations against the best single prompt, with recall the primary safety metric.LLMs outperformed the purpose-built systems (best F1=0.918$\pm$0.001 vs.\ TiDE 0.779), with advantages concentrated in contextual categories.

Naming the missed categories recovered 79\% (48/61) of them, and discouraging over-redaction restored precision.No agentic architecture beat calibrated single-pass prompting (F1 0.906--0.

907), but LLM outputs surfaced 414~candidate annotation gaps; re-annotation confirmed 227~PHI spans, against which the final prompt reached recall=0.981 (F1=0.907$\pm$0.002).Well-calibrated ICL resolves both the institutional PHI gap and the precision--recall trade-off in one LLM call per note.

LLMs cost more to run than traditional methods, but that cost buys a way to audit the reference standard.LLMs are a legitimate, adaptable alternative to purpose-built de-identification systems; institution-specific prompt development should be the primary adaptation strategy.

Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.17051 [cs.CL] (or arXiv:2608.17051v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.

17051 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hyun-Hwan Jeong [view email] [v1] Mon, 17 Aug 2026 18:56:04 UTC (1,381 KB) Full-text links: Access Paper: View a PDF of the paper titled Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss, by Daniel Palacios and Matthew Brady Neeley and Angel Adetomike Otto and Shalini Dhamodharan and John P.

Woodhouse and Chi-fan Lin and Mark Zobeck and Zhandong Liu and Hyun-Hwan JeongView PDFHTML (experimental)TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-08 Change to browse by: cs cs.

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