Artificial Intelligence-Generated Electronic Medical Record Summarization in Breast Surgical Oncology

Reviewing pathology, imaging, and consultation documents in oncology can be time-consuming, particularly when records originate from external facilities in different file formats. This study aimed to evaluate the impact of a Retrieval-Augmented Generation (RAG)-enabled GPT-4o summarization agent on clinical workflows and quality of outside-record summaries in breast surgical oncology.

Initial performance evaluation of a GPT-4o/RAG agent to generate summaries of oncologic reports in 50 charts followed by a prospective pilot test of sequential cases, with each AI summary evaluated using a modified Provider Documentation Summarization Quality Instrument (PDSQI-9; 1-5 Likert scale), including dichotomized ratings (low [1-3], high [4, 5]), binomial testing, frequency and type of user-reported errors, clinician-coded error criticality (treatment-impacting vs noncritical). Pre- and post-use survey of documentation burden (NASA TLX) and user experience was performed.

Among 62 cases, AI-generated summaries were rated high for accuracy, usefulness, succinctness, and source citation. Thoroughness without omission was rated low in 28 (45%) summaries. Errors were noted in 25 (40%) surveys, with 13 (52%) classified as critical (treatment-impacting). The most common error type involved imaging, reported in 17 (68%) cases. For perceived time savings, the median response was neutral, but qualitative feedback described the tool as helpful for straightforward cases and as reducing typing burden but requiring workflow adjustment and improvements for complex cases.

Although users rated RAG-enabled GPT-4o agent-generated documentation summaries favorably on several quality domains, they frequently lacked thoroughness and occasionally contained treatment-relevant errors. Human review and further iteration of the technology remain necessary before implementation.