JMIR Publications

ABSTRACT

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.