Academic literature on the topic 'OMOP CDM'
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Journal articles on the topic "OMOP CDM"
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Sathappan, Selva Muthu Kumaran, Young Seok Jeon, Trung Kien Dang, et al. "Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset." Applied Clinical Informatics 12, no. 04 (2021): 757–67. http://dx.doi.org/10.1055/s-0041-1732301.
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Abstract Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.
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Hallinan, Christine Mary, Roger Ward, Graeme K. Hart, et al. "Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM." BMJ Health & Care Informatics 31, no. 1 (2024): e100953. http://dx.doi.org/10.1136/bmjhci-2023-100953.
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ObjectivesIn this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers.MethodsThrough pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site.ResultsBy simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting.DiscussionAdoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data.ConclusionThe adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings.
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Lamer, Antoine, Nicolas Depas, Matthieu Doutreligne, et al. "Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study." Applied Clinical Informatics 11, no. 01 (2020): 013–22. http://dx.doi.org/10.1055/s-0039-3402754.
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Abstract Background Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP model) extends the range of available statistical analyses. Objective This study aimed to evaluate the feasibility of implementing French national electronic health records in the OMOP CDM. Methods The OMOP's specifications were used to audit the source data, specify the transformation into the OMOP CDM, implement an extract–transform–load process to feed data from the French health care system into the OMOP CDM, and evaluate the final database. Results Seventeen vocabularies corresponding to the French context were added to the OMOP CDM's concepts. Three French terminologies were automatically mapped to standardized vocabularies. We loaded nine tables from the OMOP CDM's “standardized clinical data” section, and three tables from the “standardized health system data” section. Outpatient and inpatient data from 38,730 individuals were integrated. The median (interquartile range) number of outpatient and inpatient stays per patient was 160 (19–364). Conclusion Our results demonstrated that data from the French national health care system can be integrated into the OMOP CDM. One of the main challenges was the use of international OMOP concepts to annotate data recorded in a French context. The use of local terminologies was an obstacle to conceptual mapping; with the exception of an adaptation of the International Classification of Diseases 10th Revision, the French health care system does not use international terminologies. It would be interesting to extend our present findings to the 65 million people registered in the French health care system.
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Tan, Hui Xing, Desmond Chun Hwee Teo, Dongyun Lee, et al. "Applying the OMOP Common Data Model to Facilitate Benefit-Risk Assessments of Medicinal Products Using Real-World Data from Singapore and South Korea." Healthcare Informatics Research 28, no. 2 (2022): 112–22. http://dx.doi.org/10.4258/hir.2022.28.2.112.
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Objectives: The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to a common data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefit-risk assessments in post-market regulatory evaluation and decisions.Methods: EMRs from January 2013 to December 2016 were mapped onto the Observational Medical Outcomes Partnership-CDM (OMOP-CDM) schema. Vocabulary mappings were applied to convert source data values into OMOP-CDM-endorsed terminologies. Existing analytic codes used in a prior OMOP-CDM drug utilization study were modified to conduct an illustrative analysis of oral anticoagulants used for atrial fibrillation in Singapore and South Korea, resembling a typical benefit-risk assessment. A novel visualization is proposed to represent the comparative effectiveness, safety and utilization of the drugs.Results: Over 90% of records were mapped onto the OMOP-CDM. The CDM data structures and analytic code templates simplified the querying of data for the analysis. In total, 2,419 patients from Singapore and South Korea fulfilled the study criteria, the majority of whom were warfarin users. After 3 months of follow-up, differences in cumulative incidence of bleeding and thromboembolic events were observable via the proposed visualization, surfacing insights as to the agent of preference in a given clinical setting, which may meaningfully inform regulatory decision-making.Conclusions: While the structure of the OMOP-CDM and its accessory tools facilitate real-world data analysis, extending them to fulfil regulatory analytic purposes in the post-market setting, such as benefit-risk assessments, may require layering on additional analytic tools and visualization techniques.
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Yoo, Sooyoung, Eunsil Yoon, Dachung Boo, et al. "Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model." Applied Clinical Informatics 13, no. 03 (2022): 521–31. http://dx.doi.org/10.1055/s-0042-1748144.
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Abstract Background Cancer staging information is an essential component of cancer research. However, the information is primarily stored as either a full or semistructured free-text clinical document which is limiting the data use. By transforming the cancer-specific data to the Observational Medical Outcome Partnership Common Data Model (OMOP CDM), the information can contribute to establish multicenter observational cancer studies. To the best of our knowledge, there have been no studies on OMOP CDM transformation and natural language processing (NLP) for thyroid cancer to date. Objective We aimed to demonstrate the applicability of the OMOP CDM oncology extension module for thyroid cancer diagnosis and cancer stage information by processing free-text medical reports. Methods Thyroid cancer diagnosis and stage-related modifiers were extracted with rule-based NLP from 63,795 thyroid cancer pathology reports and 56,239 Iodine whole-body scan reports from three medical institutions in the Observational Health Data Sciences and Informatics data network. The data were converted into the OMOP CDM v6.0 according to the OMOP CDM oncology extension module. The cancer staging group was derived and populated using the transformed CDM data. Results The extracted thyroid cancer data were completely converted into the OMOP CDM. The distributions of histopathological types of thyroid cancer were approximately 95.3 to 98.8% of papillary carcinoma, 0.9 to 3.7% of follicular carcinoma, 0.04 to 0.54% of adenocarcinoma, 0.17 to 0.81% of medullary carcinoma, and 0 to 0.3% of anaplastic carcinoma. Regarding cancer staging, stage-I thyroid cancer accounted for 55 to 64% of the cases, while stage III accounted for 24 to 26% of the cases. Stage-II and -IV thyroid cancers were detected at a low rate of 2 to 6%. Conclusion As a first study on OMOP CDM transformation and NLP for thyroid cancer, this study will help other institutions to standardize thyroid cancer–specific data for retrospective observational research and participate in multicenter studies.
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Moon, Hee-kyung, Sung-kook Han, and Chang-ho An. "LOD Development System for Medical Information Standard." International Journal of Engineering & Technology 7, no. 3.33 (2018): 225. http://dx.doi.org/10.14419/ijet.v7i3.33.21018.
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This paper describes Linked Open Data(LOD) development system and its application of medical information standard as Observational Medical Outcomes Partnership(OMOP) Common Data Model(CDM). The OMOP CDM allows for the systematic analysis of disparate observational database in each hospital. This paper describes a LOD instance development system based on SII. It can generate the application-specified instance development system automatically. Therefore, we applied by medical information standard as OMOP CDM to LOD development system. As a result, it was confirmed that there is no problem in applying to the standardization of medical information using the LOD development system.
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Lamer, Antoine, Osama Abou-Arab, Alexandre Bourgeois, et al. "Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study." Journal of Medical Internet Research 23, no. 10 (2021): e29259. http://dx.doi.org/10.2196/29259.
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Background Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. Objective The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. Methods Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. Results We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care. Conclusions Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse.
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Ahmadi, Najia, Yuan Peng, Markus Wolfien, Michéle Zoch, and Martin Sedlmayr. "OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review." International Journal of Molecular Sciences 23, no. 19 (2022): 11834. http://dx.doi.org/10.3390/ijms231911834.
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The current generation of sequencing technologies has led to significant advances in identifying novel disease-associated mutations and generated large amounts of data in a high-throughput manner. Such data in conjunction with clinical routine data are proven to be highly useful in deriving population-level and patient-level predictions, especially in the field of cancer precision medicine. However, data harmonization across multiple national and international clinical sites is an essential step for the assessment of events and outcomes associated with patients, which is currently not adequately addressed. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an internationally established research data repository introduced by the Observational Health Data Science and Informatics (OHDSI) community to overcome this issue. To address the needs of cancer research, the genomic vocabulary extension was introduced in 2020 to support the standardization of subsequent data analysis. In this review, we evaluate the current potential of the OMOP CDM to be applicable in cancer prediction and how comprehensively the genomic vocabulary extension of the OMOP can serve current needs of AI-based predictions. For this, we systematically screened the literature for articles that use the OMOP CDM in predictive analyses in cancer and investigated the underlying predictive models/tools. Interestingly, we found 248 articles, of which most use the OMOP for harmonizing their data, but only 5 make use of predictive algorithms on OMOP-based data and fulfill our criteria. The studies present multicentric investigations, in which the OMOP played an essential role in discovering and optimizing machine learning (ML)-based models. Ultimately, the use of the OMOP CDM leads to standardized data-driven studies for multiple clinical sites and enables a more solid basis utilizing, e.g., ML models that can be reused and combined in early prediction, diagnosis, and improvement of personalized cancer care and biomarker discovery.
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Glicksberg, Benjamin S., Boris Oskotsky, Nicholas Giangreco, et al. "ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data." JAMIA Open 2, no. 1 (2019): 10–14. http://dx.doi.org/10.1093/jamiaopen/ooy059.
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Abstract Objectives Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).
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Sibert, Nora Tabea, Johannes Soff, Sebastiano La Ferla, Maria Quaranta, Andreas Kremer, and Christoph Kowalski. "Transforming a Large-Scale Prostate Cancer Outcomes Dataset to the OMOP Common Data Model—Experiences from a Scientific Data Holder’s Perspective." Cancers 16, no. 11 (2024): 2069. http://dx.doi.org/10.3390/cancers16112069.
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To enhance international and joint research collaborations in prostate cancer research, data from different sources should use a common data model (CDM) that enables researchers to share their analysis scripts and merge results. The OMOP CDM maintained by OHDSI is such a data model developed for a federated data analysis with partners from different institutions that want to jointly investigate research questions using clinical care data. The German Cancer Society as the scientific lead of the Prostate Cancer Outcomes (PCO) study gathers data from prostate cancer care including routine oncological care data and survey data (incl. patient-reported outcomes) and uses a common data specification (called OncoBox Research Prostate) for this purpose. To further enhance research collaborations outside the PCO study, the purpose of this article is to describe the process of transferring the PCO study data to the internationally well-established OMOP CDM. This process was carried out together with an IT company that specialised in supporting research institutions to transfer their data to OMOP CDM. Of n = 49,692 prostate cancer cases with 318 data fields each, n = 392 had to be excluded during the OMOPing process, and n = 247 of the data fields could be mapped to OMOP CDM. The resulting PostgreSQL database with OMOPed PCO study data is now ready to use within larger research collaborations such as the EU-funded EHDEN and OPTIMA consortium.
Dissertations / Theses on the topic "OMOP CDM"
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Abedtash, Hamed. "An interoperable electronic medical record-based platform for personalized predictive analytics." Diss., 2017. http://hdl.handle.net/1805/13759.
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Indiana University-Purdue University Indianapolis (IUPUI)<br>Precision medicine refers to the delivering of customized treatment to patients based on their individual characteristics, and aims to reduce adverse events, improve diagnostic methods, and enhance the efficacy of therapies. Among efforts to achieve the goals of precision medicine, researchers have used observational data for developing predictive modeling to best predict health outcomes according to patients’ variables. Although numerous predictive models have been reported in the literature, not all models present high prediction power, and as the result, not all models may reach clinical settings to help healthcare professionals make clinical decisions at the point-of-care. The lack of generalizability stems from the fact that no comprehensive medical data repository exists that has the information of all patients in the target population. Even if the patients’ records were available from other sources, the datasets may need further processing prior to data analysis due to differences in the structure of databases and the coding systems used to record concepts. This project intends to fill the gap by introducing an interoperable solution that receives patient electronic health records via Health Level Seven (HL7) messaging standard from other data sources, transforms the records to observational medical outcomes partnership (OMOP) common data model (CDM) for population health research, and applies predictive models on patient data to make predictions about health outcomes. This project comprises of three studies. The first study introduces CCD-TOOMOP parser, and evaluates OMOP CDM to accommodate patient data transferred by HL7 consolidated continuity of care documents (CCDs). The second study explores how to adopt predictive model markup language (PMML) for standardizing dissemination of OMOP-based predictive models. Finally, the third study introduces Personalized Health Risk Scoring Tool (PHRST), a pilot, interoperable OMOP-based model scoring tool that processes the embedded models and generates risk scores in a real-time manner.
The final product addresses objectives of precision medicine, and has the potentials to not only be employed at the point-of-care to deliver individualized treatment to patients, but also can contribute to health outcome research by easing collecting clinical outcomes across diverse medical centers independent of system specifications.
Book chapters on the topic "OMOP CDM"
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Cha, Jiyun, Eun Kyoung Ahn, Young-Heum Yoon, and Man Young Park. "Feasibility of Applying the OMOP Common Data Model to Traditional Eastern Asian Medicine Dataset." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti231189.
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To evaluate the feasibility of applying the Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM) to databases of traditional East Asian medicine (TEAM), we composed a TEAM dataset and transformed it to the OMOP CDM. We found that some important TEAM information entities could not be transformed to the OMOP CDM (version 6.0) data fields. We suggest to develop data fields and guideline for transforming TEAM data to the OMOP CDM.
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Theron, Emmanuelle, Jean-François Gorse, and Xavier Gansel. "Usability of OMOP Common Data Model for Detailed Lab Microbiology Results." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220461.
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Anti-microbial resistance surveillance systems in Europe are limited by the inability to link laboratory data and patient data. The OMOP Common Data Model (OMOP CDM) is an option to store and use patient data in an international context supporting observational research. Detailed medical microbiology laboratory data are usually not stored in OMOP CDM. We propose here a solution to deal with the inherent complexity of microbiology data and store those in the OMOP CDM v5.4. We demonstrate the feasibility of our approach by capturing data from a microbiology in vitro diagnostic middleware, modeling in OMOP CDM 5.4 and querying for visualization.
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Zoch, Michele, Christian Gierschner, Yuan Peng, et al. "Adaption of the OMOP CDM for Rare Diseases." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210136.
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The OMOP Common Data Model (OMOP CDM) is an option to store patient data and to use these in an international context. Up to now, rare diseases can only be partly described in OMOP CDM. Therefore, it is necessary to investigate which special features in the context of rare diseases (e.g. terminologies) have to be considered, how these can be included in OMOP CDM and how physicians can use the data. An interdisciplinary team developed (1) a Transition Database for Rare Diseases by mapping Orpha Code, Alpha ID, SNOMED, ICD-10-GM, ICD-10-WHO and OMOP-conform concepts; and (2) a Rare Diseases Dashboard for physicians of a German Center of Rare Diseases by using methods of user-centered design. This demonstrated how OMOP CDM can be flexibly extended for different medical issues by using independent tools for mappings and visualization. Thereby, the adaption of OMOP CDM allows for international collaboration, enables (distributed) analysis of patient data and thus it can improve the care of people with rare diseases.
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Henke, Elisa, Stephan Lorenz, Michele Zoch, Martin Sedlmayr, and Yuan Peng. "Mapping National Vocabularies to International Standards Using OHDSI Standardized Vocabularies." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250110.
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Ensuring semantic interoperability in international studies is crucial. In this context, the mapping of national to international vocabularies is necessary. The Standardized Vocabularies of OHDSI provide such a mapping, which forms the basis for semantic interoperability in the standardized data model OMOP CDM. The aim of this paper is to provide a guideline for vocabulary mapping that supports developers in efficiently implementing the technical application of mappings into the ETL process for transforming data to OMOP CDM. By implementing materialized views and creating a decision tree, we provide a solid foundation for efficient semantic mapping in OMOP CDM. With our work, we mark an important step in realizing international observational studies based on OMOP CDM.
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Bracons Cucó, Guillem, Jessyca Gil Rojas, Petter Peñafiel Macias, et al. "OntoBridge Versus Traditional ETL: Enhancing Data Standardization into CDM Formats Using Ontologies Within the DATOS-CAT Project." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240681.
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Common Data Models (CDMs) enhance data exchange and integration across diverse sources, preserving semantics and context. Transforming local data into CDMs is typically cumbersome and resource-intensive, with limited reusability. This article compares OntoBridge, an ontology-based tool designed to streamline the conversion of local datasets into CDMs, with traditional ETL methods in adopting the OMOP CDM. We examine flexibility and scalability in the management of new data sources, CDM updates, and the adoption of new CDMs. OntoBridge showed greater flexibility in integrating new data sources and adapting to CDM updates. It was also more scalable, facilitating the adoption of various CDMs like i2b2, unlike traditional methods reliant on OMOP-specific tools developed by OHDSI. In summary, while traditional ETL provides a structured approach to data integration, OntoBridge offers a more flexible, scalable, and maintenance-efficient alternative.
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Chytas, Achilleas, Nick Bassileiades, and Pantelis Natsiavas. "Mapping OMOP-CDM to RDF: Bringing Real-World-Data to the Semantic Web Realm." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240674.
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Real-world data (RWD) (i.e., data from Electronic Healthcare Records – EHRs, ePrescription systems, patient registries, etc.) gain increasing attention as they could support observational studies on a large scale. OHDSI is one of the most prominent initiatives regarding the harmonization of RWD and the development of relevant tools via the use of a common data model, OMOP-CDM. OMOP-CDM is a crucial step towards syntactic and semantic data interoperability. Still, OMOP-CDM is based on a typical relational database format, and thus, the vision of a fully connected semantically enriched model is not fully realized. This work presents an open-source effort to map the OMOP-CDM model and the data it hosts, to an ontological model using RDF to support the FAIRness of RWD and their interlinking with Linked Open Data (LOD) towards the vision of the Semantic Web.
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Haberson Andrea, Rinner Christoph, and Gall Walter. "Standardizing Austrians Claims Data Using the OMOP Common Data Model: A Feasibility Study." In Studies in Health Technology and Informatics. IOS Press, 2019. https://doi.org/10.3233/978-1-61499-959-1-151.
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The suitability of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) for Austrian pseudonymized claims data from social security institutions and information about hospital stays is evaluated. 1,023 (99.7%) of ATC codes and 3,695 (98.6%) of ICD10 codes coincide with the OMOP vocabulary. Mappings for the local vocabularies like the Austrian pharmaceutical registration numbers, the Socio-Economic Index and professional groups, to the OMOP vocabulary do not exist. A standardization with the OMOP CDM is possible, however an initial, not negligible effort is required to adapt and incorporate the vocabulary.
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Katsch, Florian, Rada Hussein, Raffael Korntheuer, and Georg Duftschmid. "Converting HL7 CDA Based Nationwide Austrian Medication Data to OMOP CDM." In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230300.
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Austria’s national Electronic Health Record (EHR) system holds information on medication prescriptions and dispenses in highly structured HL7 Clinical Document Architecture (CDA) documents. Making these data accessible for research is desirable due to their volume and completeness. This work describes our approach of transforming the HL7 CDA data into Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and highlights a key challenge, namely mapping the Austrian drug terminology to OMOP standard concepts.
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Peng, Yuan, Azadeh Nassirian, Najia Ahmadi, Martin Sedlmayr, and Franziska Bathelt. "Towards the Representation of Genomic Data in HL7 FHIR and OMOP CDM." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210545.
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High throughput sequencing technologies have facilitated an outburst in biological knowledge over the past decades and thus enables improvements in personalized medicine. In order to support (international) medical research with the combination of genomic and clinical patient data, a standardization and harmonization of these data sources is highly desirable. To support this increasing importance of genomic data, we have created semantic mapping from raw genomic data to both FHIR (Fast Healthcare Interoperability Resources) and OMOP (Observational Medical Outcomes Partnership) CDM (Common Data Model) and analyzed the data coverage of both models. For this, we calculated the mapping score for different data categories and the relative data coverage in both FHIR and OMOP CDM. Our results show, that the patients genomic data can be mapped to OMOP CDM directly from VCF (Variant Call Format) file with a coverage of slightly over 50%. However, using FHIR as intermediate representation does not lead to further information loss as the already stored data in FHIR can be further transformed into OMOP CDM format with almost 100% success. Our findings are in favor of extending OMOP CDM with patient genomic data using ETL to enable the researchers to apply different analysis methods including machine learning algorithms on genomic data.
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You Seng Chan, Lee Seongwon, Cho Soo-Yeon, et al. "Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM)." In Studies in Health Technology and Informatics. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-830-3-467.
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It is increasingly necessary to generate medical evidence applicable to Asian people compared to those in Western countries. Observational Health Data Sciences a Informatics (OHDSI) is an international collaborative which aims to facilitate generating high-quality evidence via creating and applying open-source data analytic solutions to a large network of health databases across countries. We aimed to incorporate Korean nationwide cohort data into the OHDSI network by converting the national sample cohort into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). The data of 1.13 million subjects was converted to OMOP-CDM, resulting in average 99.1% conversion rate. The ACHILLES, open-source OMOP-CDM-based data profiling tool, was conducted on the converted database to visualize data-driven characterization and access the quality of data. The OMOP-CDM version of National Health Insurance Service-National Sample Cohort (NHIS-NSC) can be a valuable tool for multiple aspects of medical research by incorporation into the OHDSI research network.
Conference papers on the topic "OMOP CDM"
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González, Alejandro Rodríguez, Victor Robles, Juan José Cubillas Mercado, Juan Manuel Martínez Pérez, Jose Luis González Mendez, and Ernestina Menasalvas Ruiz. "ELADAIS: An Integrated Platform for High-Impact Clinical Data Extraction, Standardization and Advanced Analytics Using OMOP-CDM." In 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2025. https://doi.org/10.1109/cbms65348.2025.00151.
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Zhong, Xingjian, Yidan Sun, Amish Patel, Mallory M. Moffett, and Allison M. Dennis. "Biostable PbS/CdS/ZnS Quantum Dots for Longitudinal Shortwave Infrared Imaging." In Optical Molecular Probes, Imaging and Drug Delivery. Optica Publishing Group, 2025. https://doi.org/10.1364/omp.2025.otu3e.2.
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PbS/CdS/ZnS quantum dots demonstrate enhanced biostability over PbS/CdS for longitudinal shortwave infrared imaging, enabling non-invasive pharmacokinetic and biodistribution analyses. This optical approach enables close observation of nano-bio interactions in vivo with many fewer study animals.
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Mekha, Sarah, Enakshi Sunassee, Miguel Salgado, et al. "Multi-probe Metabolic Fluorescence Microscopy Captures Poor Tumor Immunogenicity." In Optical Molecular Probes, Imaging and Drug Delivery. Optica Publishing Group, 2025. https://doi.org/10.1364/omp.2025.om1e.3.
Full textAbstract:
Visualizing complex tumor-immune metabolic interactions is imperative to understanding cancer progression. Cocultures of 4T1 tumor cells and CD8+ T cells were imaged for glucose uptake and mitochondrial metabolism. Results point to poor immunogenic tumor phenotype.