Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model

Künnapuu, Kadri; Ioannou, Solomon; Ligi, Kadri; Kolde, Raivo; Laur, Sven; Vilo, Jaak; Reisberg, Sulev

doi:10.1093/jamiaopen/ooac021

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…Facilitators for the future implementation of the dataset include the involvement of a national organisation, specifically POIG, in its development, the harmonisation with the RCOphth's national dataset, the call for such activities from international organisations including the disease specific Multinational Interdisciplinary Working Group for Uveitis in Childhood (MIWGUC),( 16) and the broader work underway by the Observational Medical Outcomes Partnership (OMOP). (24)(25)(26) Future expansion of the dataset will be needed to ensure coverage of international concepts and care structures.…”

Section: Discussionmentioning

confidence: 99%

Standardising the structure of routinely collected data for childhood ocular inflammation: a SNOMED-CT mapping project

Kellett,

Solebo

2023

Preprint

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Background and aims Multicentre, longitudinal research methods are usually necessary for rare disease research. SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), the comprehensive and standardized terminology system can be used to enhance the interoperability of data collected across different settings. Childhood uveitis is a rare, blinding disorder, with uncertainties around disease distribution and outcome. To enhance the interoperability of uveitis data, we created a SNOMED CT coded dataset derived from a core clinical dataset. Methods Data elements were selected from a published list developed through a consensus exercise undertaken by a national disease research group, the United Kingdom's Paediatric Ocular Inflammatory Group (POIG). Items were organised using a three level priority score, based on the National Institute for Health (NIH) model for common data elements, and grouped using the Heath Level 7 (HL7) standard Fast Healthcare Interoperability Resources (FHIR) generic data structure, and then mapped across to the SNOMED CT codes. Results From the POIG consensus exercise, 160 elements were selected: 89 as high priority items, with 35 as medium and 29 as low priority items. These elements, and response items where appropriate, were grouped into Patient (n= 13 items), Observation (n= 63 items), Condition (n= 20 items), Procedure (n= 44 items), Medication (n= 18 items). There were four items for which a SNOMED CT ID could not be found. Conclusion Through this mapping activity, using international coding and terminologies, we have created a dataset for childhood onset uveitis care and research. This dataset provides a standardised vocabulary for describing clinical concepts, with a semantic interoperability which will support the exchange of data across different systems, organizations, and international or supranational groups. Future expansion of the dataset will be needed to ensure coverage of international concepts and care structures.

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Section: Discussionmentioning

confidence: 99%

Standardising the structure of routinely collected data for childhood ocular inflammation: a SNOMED-CT mapping project

Kellett,

Solebo

2023

Preprint

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Section: Methodsmentioning

confidence: 99%

“…In many countries worldwide, such as the United States, Europe, and Korea, multiinstitutional research projects are organized and utilized based on OMOP CDM on various topics, including drug side effects. [17][18][19][20][21] Implementing multicenter medical research becomes challenging as medical institutions employ different data storage methods. Therefore, a standardized structure, such as CDM, is required to use hospital data more efficiently.…”

Section: Omop Cdmmentioning

confidence: 99%

Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer

Park

Lee

Moon

et al. 2023

Technol Cancer Res Treat

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Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical data can be shielded by firewalls using distributed research networks (DRNs). We attempted to develop DRNs for multicenter research that can be easily installed and used by any institution. Patients and Methods: We propose a DRN for multicenter cancer research called the cancer research line (CAREL) and present a data catalog based on a common data model (CDM). CAREL was validated using 1723 patients with prostate cancer and 14 990 patients with lung cancer in a retrospective study. We used the attribute-value pairs and array data type JavaScript object notation (JSON) format to interface third-party security solutions such as blockchain. Results: We developed visualized data catalogs of prostate and lung cancer based on the observational medical outcomes partnership (OMOP) CDM, from which researchers can easily browse and select relevant data. We made the CAREL source code readily available for download and application for relevant purposes. In addition, it is possible to realize a multicenter research network using CAREL development sources. Conclusion: CAREL source can enable medical institutions to participate in multicenter cancer research. Our technology is open source, so small institutions that cannot afford to spend high costs can use it to develop a platform for multicenter research.

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“…For example, in Estonia, we have performed a study investigating the presence of HPV virus types and cervical cytology grades [37] and analyzed how artificial intelligence could be applied to health data for public service [38]. In addition, developing a tool for analyzing health event trajectories in any OMOP dataset [39] or participating in the study-athon of a project to harness big data in prostate cancer research [40] would not have been possible without the transformation process. We have validated the data linkage and the above describe repeatable approach in the PIONEER study, where the cohort of patients with newly diagnosed prostate cancer had an inclusion criterion requiring both a diagnosis and biopsy to be recorded [41].…”

Section: Challenge Example Solutionmentioning

confidence: 99%

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

Oja

Tamm

Mooses

et al. 2023

Preprint

Self Cite

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Objective:To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) common data model (CDM), together with challenges faced and solutions implemented.Materials and Methods:We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150,824 patients) from 2012-2019. For the sample, complete information from all three databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools.Results:In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary. Discussion: During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions.Conclusion:For a representative 10% random sample, we successfully transferred complete records from three national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence.

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Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model

Cited by 7 publications

References 19 publications

Standardising the structure of routinely collected data for childhood ocular inflammation: a SNOMED-CT mapping project

Standardising the structure of routinely collected data for childhood ocular inflammation: a SNOMED-CT mapping project

Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

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