Artificial Intelligence-Assisted Error Detection in Complex Clinical Documentation: Leveraging Large Language Models to Enhance Patient Safety in Oncology.

PURPOSE: In high-risk specialties such as oncology, errors in clinical documentation can have severe consequences, highlighting a need for enhanced safety checks. We therefore aimed to evaluate the capability of frontier large language models (LLMs) to identify and correct errors in complex clinical documentation in oncology. METHODS: We conducted a two-phase evaluation. First, we assessed LLMs (GPT o4-mini and Gemini 2.5 Pro) on 1,000 synthetic clinical hematology/oncology vignettes with controlled errors, benchmarking against human expert data for error flag detection and sentence localization. Second, we evaluated advanced LLMs and a local LLM (Gemma 3 27B) against six clinicians in detecting single, predefined, and clinically relevant errors, such as wrong risk classifications or omission of critical medication within 90 synthetic discharge summaries from oncologic patients. RESULTS: LLMs outperformed human benchmark in error flag and sentence localization tasks, with Gemini 2.5 Pro achieving top accuracies of 0.928 and 0.915, respectively. Results were robust across subgroups and scalable, with simultaneous processing of up to 50 vignettes. Within complex discharge summaries, Gemini 2.5 Pro and GPT o4-mini-high identified 97.8% and 87.8% of injected errors, respectively, substantially exceeding the 47.8% average detection rate of human specialists. Gemma 3 27B detected 35.6% of errors. Analysis of error detection overlap revealed a synergistic potential for hybrid human-artificial intelligence (AI) systems. CONCLUSION: Frontier LLMs exhibit superior error-detection capabilities and speed compared with both local models and human specialists, who are inherently time-constrained. Although synthetic data provide a controlled testbed, real-world evaluation across diverse errors and documentation styles remains critical. Advanced LLMs can serve as powerful assistants for clinical documentation reviews, substantially reducing the risk of oversight and clinician workload. Integrating LLM-driven error flagging into electronic health record workflows offers a promising strategy for enhancing documentation accuracy, treatment quality, and patient safety in oncology.