Kerli Mooses

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.

Background: Health research increasingly relies on observational datasets that capture systematically different patient populations, affecting the representativeness, comparability, and transportability of study findings. Yet these differences are rarely quantified across clinical domains or explored interactively, and the workflow for pairwise dataset comparison remains fragmented. No existing OHDSI tool provides an integrated visual analytics workflow for exploratory pairwise comparison of concept prevalence patterns between datasets on the OMOP Common Data Model. Results: We present Syrona, an open-source visual analytics tool for systematic pairwise comparison of datasets and subcohorts on the OMOP Common Data Model. Syrona extracts annual prevalence for conditions, procedures, and drugs from any OMOP CDM database, computes prevalence ratios across demographic strata, and synthesizes them via multilevel meta-analysis. A coordinated dashboard - distributional overviews, stratified heatmaps, forest plots, and absolute prevalence comparisons - enables interactive exploration driven by domain-adaptive SNOMED CT and ATC filtering. Three case studies on Estonian national health data (495,000 persons, 2012–2024) demonstrate how the tool supports representativeness assessment, institutional practice comparison, and coding artifact detection. Conclusions: Syrona is open-source and applicable to any OMOP CDM database. By treating dataset comparison as a visual analytics problem rather than a tabular reporting task, it addresses a gap in the OHDSI ecosystem and supports systematic assessment of datasets in distributed observational research.

Health research increasingly relies on observational datasets that capture systematically different patient populations, affecting the representativeness, comparability, and transportability of study findings. Yet these differences are rarely quantified across clinical domains or explored interactively, and the workflow for pairwise dataset comparison remains fragmented. We present Syrona, a visual analytics workflow for systematic pairwise comparison of datasets and subcohorts on the OMOP Common Data Model. Syrona extracts annual prevalence for conditions, procedures, and drugs from any OMOP CDM database, computes prevalence ratios across demographic strata, and synthesizes them via multilevel meta-analysis. A coordinated dashboard - distributional overviews, stratified heatmaps, forest plots, and absolute prevalence comparisons - enables interactive exploration driven by domain-adaptive SNOMED CT and ATC filtering. Three case studies on Estonian national health data (495,000 persons, 2012-2024) demonstrate how the workflow supports representativeness assessment, institutional practice comparison, and coding artifact detection. Syrona is open-source and applicable to any OMOP CDM database. Explore the demo at: http://omop-apps.cloud.ut.ee/ShinyApps/Syrona/

Background: The current knowledge about medication adherence is based on studies focusing only on few health conditions and little is known about how strongly adherence is shaped by person-specific behaviour. The aim of the cohort study is to 1) evaluate the effect of multiple factors affecting medication adherence in a consistent manner across 137 active substances, and 2) calculate individual medication adherence score (IMAS), evaluate its predictive power, stability over time, and impact on health outcomes. In essence, IMAS describes persons’ medication-taking “baseline”. Methods: We utilised a representative dataset with electronic health records, claims, and dispensed medications across 137 active substances and applied continuous multiple interval measures of medication availability (CMA). To assess the effect of various demographic, health, and medication-related variables on CMA, we employed linear mixed models. Results: Here we show that the medication adherence ranged from 0.423 (albuterol, 95% CI 0.414–0.432) to 0.922 (warfarin, 95% CI 0.917–0.926). The demographic, health- and medication-related factors explained 11.6% and IMAS 22.0% of the variation in adherence. IMAS predicted adherence across medication classes, reduced the risk of overall hospitalisation (hazard ratio = 0.76, 95% CI 0.60–0.97, p < 0.05) and cause-specific incidence for 17 conditions. Conclusions: Thus, IMAS represents a person-level metric that captures baseline medication-taking behaviour across therapeutic classes and predicts both medication adherence as well as health outcomes. Our analysis suggests that medication-taking behaviour represents a broader patient-level phenomenon manifesting consistently across medications, suggesting its potential for personalised interventions in clinical practice and more efficient public health strategies and policies.

Background: For accurate medication usage statistics and medication adherence calculations, we need to have an accurate days’ supply (DS) for each prescription. Unfortunately, often the DS or the information needed for calculating the DS is not provided. Therefore, other methods need to be applied to acquire missing values or substitute incorrect values. Objective: This study aims to apply a variety of methods for managing incomplete and missing data to enhance the accuracy of calculating DS for all medications and drug forms alike. Furthermore, to describe the effect of applied methods on the medication adherence calculated on real-world data. Methods: A dataset comprising prescription records from a 10% (150,824 patients) random sample of the Estonian population between 2012 and 2019 was used. The workflow consisted of 3 steps: data cleaning, imputation, and calculation of DS. For imputation, different methods were combined, such as calculating mode-based daily dose, or using usage guidelines from the Summary of Product Characteristics or legislation. DS was calculated based on the provided daily dose or imputed value. To evaluate the impact of data cleaning, medication adherence for the baseline dataset and corrected dataset for 2 time periods, 2012-2015 and 2017-2019, was calculated and compared. Results: The drug forms with the lowest proportion of correct DS provided were insulin injections (2601/82,867, 3.1%) and intravaginal contraceptives (1692/21,145, 8%) while the highest proportion of DS was provided for inhalation medication (78,541/126,588, 62%), oral drops (52,085/98,221, 53%) and tablets, capsules, suppositories (2,828,617/6,176,585, 45.8%). As a result of applying different imputation approaches, we successfully found the DS for 98.3% (7,415,347/7,544,892) of dispensed prescriptions. For the remaining 1.7% (129,545/7,544,892) of prescriptions, DS could not be imputed nor calculated with these methods. As for the medication adherence, the distinction between 2 observed time periods was more distinct in the baseline dataset compared with the corrected dataset for most of the drug groups, indicating that the applied correction methods had lessened the stark contrast. Conclusions: In summary, our study demonstrated that with a carefully designed imputation pipeline where data-driven imputation is combined with domain knowledge and literature information, it is possible to meaningfully improve the quality of prescription datasets and generate more accurate and consistent adherence metrics across various drug forms. Nonetheless, future efforts should continue to refine imputation techniques, incorporate machine learning approaches where appropriate, and expand validation efforts using external benchmarks or clinical outcomes.

Background: For accurate medication usage statistics and medication adherence calculations, we need to have an accurate days’ supply (DS) for each prescription. Unfortunately, often the DS or information needed for calculating the DS is not provided. Therefore, other methods need to be applied to acquire missing values or substituting incorrect values. Objective: The aim of this study is to apply a variety of methods for managing incomplete and missing data to enhance the accuracy of calculating DS for all medications and drug forms alike. Furthermore, to describe the effect of applied methods on the medication adherence calculated on real-world data. Methods: A dataset comprising prescription records from a 10% random sample of the Estonian population between 2012 and 2019 was used. The workflow consisted of three steps – data cleaning, imputation and calculation of DS. For imputation, different methods were combined, such as calculating mode-based daily dose, or using usage guidelines from Summary of Product Characteristics (SPCs) or legislation. DS was calculated based on provided daily dose or imputed value. To evaluate the impact of data cleaning, medication adherence for baseline dataset and corrected dataset for two time periods 2012–2015 and 2017–2019 was calculated and compared. Results: The drug forms with the lowest proportion of correct DS provided were insulin injections (3.1%) and intravaginal contraceptives (8.0%) while the highest proportion of DS was provided for inhalation medication (57.5%), oral drops (53.0%) and tablets, capsules, suppositories (45.8%). As a result of applying different imputation approaches, we successfully found the DS for 98.3% (N=7,415,347) of dispensed prescriptions. For the remaining 1.7% (N=129,545) of prescriptions DS could not be imputed nor calculated with these methods. As for the medication adherence, the distinction between two observed time periods was more distinct in the baseline dataset compared with the corrected dataset for most of the drug groups, indicating that the applied correction methods had lessened the stark contrast. Conclusions: In summary, our study demonstrated that with a carefully designed imputation pipeline where data-driven imputation is combined with domain knowledge and literature information, it is possible to meaningfully improve the quality of prescription datasets and generate more accurate and consistent adherence metrics across various drug form. Nonetheless, future efforts should continue to refine imputation techniques, incorporate machine learning approaches where appropriate, and expand validation efforts using external benchmarks or clinical outcomes.

BACKGROUND For accurate medication usage statistics and medication adherence calculations, we need to have an accurate days’ supply (DS) for each prescription. Unfortunately, often the DS or the information needed for calculating the DS is not provided. Therefore, other methods need to be applied to acquire missing values or substitute incorrect values. OBJECTIVE This study aims to apply a variety of methods for managing incomplete and missing data to enhance the accuracy of calculating DS for all medications and drug forms alike. Furthermore, to describe the effect of applied methods on the medication adherence calculated on real-world data. METHODS A dataset comprising prescription records from a 10% (150,824 patients) random sample of the Estonian population between 2012 and 2019 was used. The workflow consisted of 3 steps: data cleaning, imputation, and calculation of DS. For imputation, different methods were combined, such as calculating mode-based daily dose, or using usage guidelines from the Summary of Product Characteristics or legislation. DS was calculated based on the provided daily dose or imputed value. To evaluate the impact of data cleaning, medication adherence for the baseline dataset and corrected dataset for 2 time periods, 2012-2015 and 2017-2019, was calculated and compared. RESULTS The drug forms with the lowest proportion of correct DS provided were insulin injections (2601/82,867, 3.1%) and intravaginal contraceptives (1692/21,145, 8%) while the highest proportion of DS was provided for inhalation medication (78,541/126,588, 62%), oral drops (52,085/98,221, 53%) and tablets, capsules, suppositories (2,828,617/6,176,585, 45.8%). As a result of applying different imputation approaches, we successfully found the DS for 98.3% (7,415,347/7,544,892) of dispensed prescriptions. For the remaining 1.7% (129,545/7,544,892) of prescriptions, DS could not be imputed nor calculated with these methods. As for the medication adherence, the distinction between 2 observed time periods was more distinct in the baseline dataset compared with the corrected dataset for most of the drug groups, indicating that the applied correction methods had lessened the stark contrast. CONCLUSIONS In summary, our study demonstrated that with a carefully designed imputation pipeline where data-driven imputation is combined with domain knowledge and literature information, it is possible to meaningfully improve the quality of prescription datasets and generate more accurate and consistent adherence metrics across various drug forms. Nonetheless, future efforts should continue to refine imputation techniques, incorporate machine learning approaches where appropriate, and expand validation efforts using external benchmarks or clinical outcomes.

The current knowledge about medication adherence (MA) is based on studies focusing only on few health conditions. We utilised a representative dataset with electronic health records, claims, and prescribed medications to 1) evaluate the effect of multiple factors affecting MA in a consistent manner across 137 ingredients, and 2) calculate individual medication adherence score (IMAS), evaluate its predictive power, stability over time, and impact on health outcomes. The MA ranged from 0.423 (albuterol, 95% CI 0.414–0.432) to 0.922 (warfarin, 95% CI 0.917–0.926). The demographic, health- and medication-related factors explained 11.6% and IMAS 22% of the variation in adherence. IMAS predicted adherence across medication classes, reduced the risk of overall hospitalisation (Hazard ratio = 0.76, 95% CI 0.60–0.97, p<0.05) and cause-specific incidence for 17 conditions. Thus, medication-taking behaviour represents a broader patient-level phenomenon manifesting consistently across medications, suggesting its potential for personalised interventions in clinical practice and more efficient public health strategies and policies.