In mental health care, reasoning over patient journeys is a key task for clinicians.
Yet these journeys, encompassing a longitudinal progression of biological, psychological, and social events, are often spread across disparate unstructured text narratives, making temporal recovery challenging.
We present CliniCIRCA
a multi-stage LLM framework for Calendar-anchored, Imprecision-aware Reconstruction of Clinical Annals.
To our knowledge, CliniCIRCA is the first to temporally classify clinical events across unstructured discharge summaries without event-level timestamps.
From 14,882 MIMIC-III mental health admissions, we first construct a benchmark of 52 discharge summaries on which CliniCIRCA produces 15,891 temporally tagged events.
After correcting 629 errors based on a clinician-in-the-loop evaluation, we produce verified gold-standard labels.
Finally, the corrected timelines drive a temporally grounded summarization stage that compresses each source 1.52 times into a date-grouped chronological record.
We then scale the framework to generate 1,000 silver-standard timelines and evaluate them as training data.
Compared with zero- and few-shot prompting, instruction tuning generally improves five open-weight models on event extraction, temporal tagging, and summarization across silver and clinician-verified evaluations.