How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

Background: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases.

Secondary use of patient data within decentralized studies using the example of rare diseases in Germany: A data scientist's exploration of process and lessons learned.

Objective: Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance.

Predictors of improvement in disease activity in childhood and adolescent Crohn's disease: an analysis of age, localization, initial severity and drug therapy - data from the Saxon Registry for Inflammatory Bowel Disease in Children in Germany (2000-2014).

The escalating worldwide prevalence of Crohn's disease (CD) among children and adolescents, coupled with a trend toward earlier onset, presents significant challenges for healthcare systems. Moreover, the chronicity of this condition imposes substantial individual burdens. Consequently, the principal objective of CD treatment revolves around rapid inducing remission.

Transition database for rare diseases and its use for clinical documentation.

Patients with rare diseases commonly suffer from severe symptoms as well as chronic and sometimes life-threatening effects. Not only the rarity of the diseases but also the poor documentation of rare diseases often leads to an immense delay in diagnosis. One of the main problems here is the inadequate coding with common classifications such as the International Statistical Classification of Diseases and Related Health Problems.

Conceptual design of a generic data harmonization process for OMOP common data model.

Background: To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen.

A systematic review and meta-regression on international trends in the incidence of ulcerative colitis in children and adolescents associated with socioeconomic and geographic factors.

Unlabelled: The incidence of ulcerative colitis (UC) among children and adolescents is rising globally, albeit with notable discrepancies across countries. This systematic review and meta-analysis aims to provide a comprehensive overview of the incidence rates of pediatric UC in various countries and explore potential influencing factors. A systematic literature search was conducted in PubMed and EMBASE (via OVID) for studies published between January 1, 1970, and December 31, 2019.

Assessing the Use of German Claims Data Vocabularies for Research in the Observational Medical Outcomes Partnership Common Data Model: Development and Evaluation Study.

Background: National classifications and terminologies already routinely used for documentation within patient care settings enable the unambiguous representation of clinical information. However, the diversity of different vocabularies across health care institutions and countries is a barrier to achieving semantic interoperability and exchanging data across sites. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enables the standardization of structure and medical terminology.

An Extract-Transform-Load Process Design for the Incremental Loading of German Real-World Data Based on FHIR and OMOP CDM: Algorithm Development and Validation.

Background: In the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium, an IT-based clinical trial recruitment support system was developed based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Currently, OMOP CDM is populated with German Fast Healthcare Interoperability Resources (FHIR) using an Extract-Transform-Load (ETL) process, which was designed as a bulk load. However, the computational effort that comes with an everyday full load is not efficient for daily recruitment.

Methods Used in the Development of Common Data Models for Health Data: Scoping Review.

Background: Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems.

Ten Topics to Get Started in Medical Informatics Research.

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field.

Context-Sensitive Common Data Models for Genetic Rare Diseases - A Concept.

Current challenges of rare diseases need to involve patients, physicians, and the research community to generate new insights on comprehensive patient cohorts. Interestingly, the integration of patient context has been insufficiently considered, but might tremendously improve the accuracy of predictive models for individual patients. Here, we conceptualized an extension of the European Platform for Rare Disease Registration data model with contextual factors.

German Claims Data for Real-World Research: Content Coverage Evaluation in OMOP CDM.

Research on real-world data is becoming increasingly important. The current restriction to clinical data in Germany limits the view of the patient. To gain comprehensive insights, claims data can be added to the existing knowledge.

An ETL-process design for data harmonization to participate in international research with German real-world data based on FHIR and OMOP CDM.

Background: International studies are increasingly needed in order to gain more unbiased evidence from real-world data. To achieve this goal across the European Union, the EMA set up the DARWIN EU project based on OMOP CDM established by the OHDSI community. The harmonization of heterogeneous local health data in OMOP CDM is an essential step to participate in such networks.

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review.

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.

An OHDSI ATLAS Extension to Support Feasibility Requests in a Research Network.

Checking the feasibility of real-world data to answer a certain research question is crucial especially in a multi-site research network. In this work we present an extension of the ATLAS user interface for the OMOP common data model that integrates into an existing national feasibility network and thus foster capabilities for future participation in international research studies.

Towards the Improvement of Clinical Guidelines Based on Real World Data.

Computerized clinical guidelines (CCG) are effective instruments for standardizing, monitoring and optimizing medical treatment processes. Nevertheless, due to barriers in flexibility, transferability and acceptance, the widespread use of CCG in clinical practice is not yet common. To overcome those issues, we present a concept on how to use real world data to evaluate CCG and to recommend improvements.

Transfer of Clinical Drug Data to a Research Infrastructure on OMOP - A FAIR Concept.

Generating evidence based on real-world data is gaining importance in research not least since the COVID-19 pandemic. The Common Data Model of Observational Medical Outcomes Partnership (OMOP) is a research infrastructure that implements FAIR principles. Although the transfer of German claim data to OMOP is already implemented, drug data is an open issue.

The Usage of OHDSI OMOP - A Scoping Review.

OHDSI, a fast growing open-science research community seeks to enable researchers from around the globe to conduct network studies based on standardized data and vocabularies. There is no comprehensive review of publications about OHDSI's standard: the OMOP Common Data Model and its usage available. In this work we aim to close this gap and provide a summary of existing publications including the analysis of its meta information such as the choice of journals, journal types, countries, as well as an analysis by topics based on a title and abstract screening.

Development of a Dashboard for Rare Diseases - A Technical Case Report.

About 30 million people suffer from a rare disease in Europe. Those affected face a variety of problems. These include the lack of information and difficult access to scientific knowledge for physicians.

[Interdisciplinary care path and potential IT support for people with rare diseases in Germany].

Introduction: Due to the high variability and, at the same time, rare occurrence of rare diseases, the diagnosis of these patients (approx. 4 million people in Germany) can turn into an odyssey. The large time interval between the appearance of symptoms and the final diagnosis of the rare disease leads to a delay in the appropriate treatment.

Adaption of the OMOP CDM for Rare Diseases.

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.