Cross-Specialty Analysis of Japanese Perinatal EHR Narratives Using Large Language Models: Documentation Asymmetry and the Classification of Depressive States (Preprint)

BACKGROUND The perinatal period is associated with an elevated risk for depressive disorders, making timely identification a public health priority. Collaboration between obstetrics and psychiatry often remains suboptimal in clinical practice. A potential contributor to this gap is information asymmetry across specialties—qualitative and quantitative imbalances in how psychiatric symptoms are documented in electronic health records (EHRs). OBJECTIVE To quantify cross-specialty asymmetries in perinatal EHR documentation and to evaluate the performance of large language model (LLM)–based approaches for classifying depressive states from heterogeneous clinical narratives. METHODS We retrospectively collected Japanese-language EHR notes for 94 pregnant or postpartum patients with a history of psychiatric consultation from a single center. We compared the prevalence of symptom mentions by author specialty. Using stratified sampling, we then selected a cohort of 20 patients to benchmark multiple LLM-based inference pipelines for depressive-state classification. The best-performing pipeline combined LLM outputs with a rule-based layer encoding clinical knowledge for final adjudication. RESULTS Documentation practices differed markedly by clinical specialty. Mentions of “depression” were approximately 30-fold less frequent in notes authored by the obstetrics team compared with those by psychiatrists. Despite this documentation bias, the top-performing pipeline achieved an F1-score of 0.750 (95% CI 0.500–0.917) for classifying depressive states. CONCLUSIONS This study highlights the potential of LLMs to classify depressive states from heterogeneous EHRs despite significant documentation asymmetry. However, effective clinical deployment will require both technical advances (eg, bias-aware modeling) and organizational efforts to improve documentation quality and interspecialty information sharing.