CareTransition-Audit: A Benchmark to Audit Discharge Summaries for Efficient Care Transitions
arXiv:2604.05435v2 Announce Type: replace Abstract: Incomplete or inconsistent discharge documentation drives care fragmentation and avoidable readmissions. Despite its critical role in patient safety, auditing discharge summaries relies on manual review and does not scale. We propose an automated framework for auditing discharge summaries using large language models (LLMs). Our approach operationalizes the DISCHARGED framework into a checklist of 46 questions. Using 50 summaries from the MIMIC-IV database, with clinician ground-truth labels, we benchmark 11 LLMs. Model-assessed mean documenta
The proliferation of advanced large language models enables their application to complex, high-stakes domains like healthcare documentation, which was previously too challenging for automation.
Automating the auditing of discharge summaries addresses a critical bottleneck in healthcare, potentially improving patient safety and reducing administrative burdens through more efficient care transitions.
The reliance on manual review for auditing discharge summaries can begin to shift towards automated, LLM-driven processes, enhancing scalability and potentially reducing errors.
- · Healthcare Providers
- · LLM Developers
- · Patients
- · Health IT Companies
- · Manual Auditors (short-term)
- · Legacy Clinical Document Management Systems
Improved efficiency and accuracy in discharge summary audits, leading to better care coordination.
Reduced healthcare costs due to fewer avoidable readmissions and optimized administrative processes.
LLMs become standard tools for a wide array of healthcare quality assurance and compliance tasks, fundamentally changing clinical workflows.
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Read at arXiv cs.AI