A national-scale digital atlas of recorded care patterns across more than 1000 diagnostic categories using real-world health data: a retrospective observational study

BackgroundRoutinely 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. MethodsIn 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. FindingsOf 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{middle dot}5 of 7 [IQR 6-7]), providing information difficult to obtain conventionally (6 [5-7]), and supporting observational study design (5{middle dot}5 [5-6]). InterpretationThe 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. FundingEstonian Research Council; European Union; Estonian Ministry of Education and Research; Innovative Medicines Initiative 2 Joint Undertaking; EFPIA. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and medRxiv from database inception to Aug, 2026, without language restrictions, using terms for electronic health records, real-world data, disease trajectories, treatment and care pathways, treatment patterns, clinical-event analysis, OMOP, and population-scale atlases. We also examined relevant reviews and reference lists. Previous population-scale studies have mapped disease trajectories, treatment pathways, or care intensity, generally focusing on diagnoses alone, prespecified events, selected disease cohorts, or a single summary measure of care. OMOP-based treatment-pattern studies have likewise concentrated on specific disease groups. We found no previous population-scale resource that automatically reduced multidomain clinical events surrounding an index diagnosis across more than 1000 diagnostic categories while retaining prevalence, enrichment, timing, co-occurrence, and patient-level heterogeneity. Added value of this studyWe applied a previously clinically benchmarked concept-reduction workflow to longitudinal OMOP-mapped health data across 1080 ICD-10 diagnostic categories and three observation windows, producing 3240 diagnosis-window summaries from 509,856 individuals. The workflow reduced candidate clinical concepts by 97-98% and generated compact summaries spanning diagnoses, procedures, medications, measurements, observations, visits, and deaths. The resulting interactive atlas combines concept prevalence and enrichment with timing, co-occurrence, and exploratory patient clustering. An internal formative expert evaluation supported its use for generating research questions and planning observational studies. Implications of all the available evidenceAutomated reduction of high-dimensional health-record data can provide a scalable starting point for clinical orientation, hypothesis generation, and observational study design without requiring a separately curated event set for every disease. Applying the same workflow to other OMOP databases could enable direct comparison of diagnosis-centred treatment and care patterns between regions and health systems, while helping distinguish differences in clinical practice from differences in data capture. The atlas is exploratory and should not be interpreted as evidence of causal effects, guideline adherence, or individual patient trajectories.