Background Routinely collected health data can reveal recorded care around diagnosis, but disease-specific curation does not scale across thousands of conditions. We scaled a previously developed automated framework that reduces heterogeneous clinical events into standardised, diagnosis-centred summaries of real-world care patterns. Methods In this retrospective observational study, we used EST-Health-30, a pseudonymised 30% random sample of Estonian residents with health-care records from 2012 to 2024. We constructed cohorts for ICD-10 three-character diagnostic categories using the first eligible observed diagnosis after a 3-year diagnosis-free observable lookback. For each category, we summarised recorded clinical events from a 90-day pre-index window, a 30-day post-index window, and a 365-day post-index window. An enrichment-based workflow compared earlier self-comparator periods and matched population controls, then filtered and aggregated concepts across clinical domains. Diagnosis-concept relationships were assessed by rubric-guided language-model classification, and atlas plausibility and utility by ten experts. Findings Of 1645 observed diagnostic categories, 1080 met inclusion criteria, yielding 3240 diagnosis-window summaries across 509,856 individuals. Each summary characterised diagnosis-associated events by prevalence, enrichment, timing, co-occurrence, and variation between patient groups after reducing candidate concepts by 97-98%. Among 296,303 retained diagnosis-concept pairs, the language model classified 24% as directly related, 55% as indirectly related, and 21% as noisy. Experts rated the atlas highly for generating research questions (median 6.5 of 7 [IQR 6-7]), providing information difficult to obtain conventionally (6 [5-7]), and supporting observational study design (5.5 [5-6]). Interpretation The atlas makes multidimensional diagnosis-centred patterns in longitudinal health records inspectable at population scale in order to support clinical orientation, hypothesis generation, and observational study planning. Outputs represent recorded care rather than individual patient trajectories or causal effects. Funding Estonian Research Council; European Union; Estonian Ministry of Education and Research; Innovative Medicines Initiative 2 Joint Undertaking; EFPIA.