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. Instead, the ORPHAcode enables precise naming of the diseases. So far, just few approaches report in detail how the technical implementation of the ORPHAcode is done in clinical practice and for research. We present a concept and implementation of storing and mapping of ORPHAcodes. The Transition Database for Rare Diseases contains all the information of the Orphanet catalog and serves as the basis for documentation in the clinical information system as well as for monitoring Key Performance Indicators for rare diseases at the hospital. The five-step process (especially using open source tools and the DataVault 2.0 logic) for set-up the Transition Database allows the approach to be adapted to local conditions as well as to be extended for additional terminologies and ontologies.

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. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM. METHODS: For this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps. RESULTS: From 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps. CONCLUSIONS: The generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.

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.

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. Additionally, a manual search was performed to identify relevant systematic reviews. Meta-analyses and meta-regressions were employed to determine the overall incidence rate and examine potential factors that may influence it. A total of 66 studies were included in the qualitative analysis, while 65 studies were included in the meta-analysis and 50 studies were meta-regression. The study reports a rising incidence of pediatric UC in several countries but significant differences across geographic regions, with no discernible global temporal trend. In addition, our meta-regression analysis showed that geographic location and socioeconomic factors significantly influenced the incidence of UC. CONCLUSION: Our findings indicate a rising incidence of pediatric UC in numerous countries since 1970, but with significant geographical variation, potentially presenting challenges for respective healthcare systems. We have identified geographic and socioeconomic factors that contribute to the observed heterogeneity in incidence rates. These findings provide a foundation for future research and health policies, aiming to tackle the growing burden of UC among children and adolescents. WHAT IS KNOWN: • The incidence of ulcerative colitis in childhood and adolescence appears to be increasing worldwide and varies internationally. • Environmental and lifestyle factors are suspected as potential causes. WHAT IS NEW: • Our results highlight that the heterogeneity in incidence rates can be attributed to geographic and socio-economic factors.

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. It allows the mapping of national vocabularies into so-called standard concepts, representing normative expressions for international analyses and research. Within our project "Hybrid Quality Indicators Using Machine Learning Methods" (Hybrid-QI), we aim to harmonize source codes used in German claims data vocabularies that are currently unavailable in the OMOP CDM. Objective: This study aims to increase the coverage of German vocabularies in the OMOP CDM. We aim to completely transform the source codes used in German claims data into the OMOP CDM without data loss and make German claims data usable for OMOP CDM-based research. Methods: To prepare the missing German vocabularies for the OMOP CDM, we defined a vocabulary preparation approach consisting of the identification of all codes of the corresponding vocabularies, their assembly into machine-readable tables, and the translation of German designations into English. Furthermore, we used 2 proposed approaches for OMOP-compliant vocabulary preparation: the mapping to standard concepts using the Observational Health Data Sciences and Informatics (OHDSI) tool Usagi and the preparation of new 2-billion concepts (ie, concept_id >2 billion). Finally, we evaluated the prepared vocabularies regarding completeness and correctness using synthetic German claims data and calculated the coverage of German claims data vocabularies in the OMOP CDM. Results: Our vocabulary preparation approach was able to map 3 missing German vocabularies to standard concepts and prepare 8 vocabularies as new 2-billion concepts. The completeness evaluation showed that the prepared vocabularies cover 44.3% (3288/7417) of the source codes contained in German claims data. The correctness evaluation revealed that the specified validity periods in the OMOP CDM are compliant for the majority (705,531/706,032, 99.9%) of source codes and associated dates in German claims data. The calculation of the vocabulary coverage showed a noticeable decrease of missing vocabularies from 55% (11/20) to 10% (2/20) due to our preparation approach. Conclusions: By preparing 10 vocabularies, we showed that our approach is applicable to any type of vocabulary used in a source data set. The prepared vocabularies are currently limited to German vocabularies, which can only be used in national OMOP CDM research projects, because the mapping of new 2-billion concepts to standard concepts is missing. To participate in international OHDSI network studies with German claims data, future work is required to map the prepared 2-billion concepts to standard concepts.

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. Objective: The aim of this study is to extend our existing ETL process with the option of incremental loading to efficiently support daily updated data. Methods: Based on our existing bulk ETL process, we performed an analysis to determine the requirements of incremental loading. Furthermore, a literature review was conducted to identify adaptable approaches. Based on this, we implemented three methods to integrate incremental loading into our ETL process. Lastly, a test suite was defined to evaluate the incremental loading for data correctness and performance compared to bulk loading. Results: The resulting ETL process supports bulk and incremental loading. Performance tests show that the incremental load took 87.5% less execution time than the bulk load (2.12 min compared to 17.07 min) related to changes of 1 day, while no data differences occurred in OMOP CDM. Conclusions: Since incremental loading is more efficient than a daily bulk load and both loading options result in the same amount of data, we recommend using bulk load for an initial load and switching to incremental load for daily updates. The resulting incremental ETL logic can be applied internationally since it is not restricted to German FHIR profiles.

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. OBJECTIVE: This scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level. METHODS: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users' needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients' representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development. RESULTS: We finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process. CONCLUSIONS: The included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future.

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. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

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. This extended model can serve as an enhanced baseline and is well-suited for analyses using artificial intelligence models for improved predictions. The study is an initial result that will develop context-sensitive common data models for genetic rare diseases.

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. However, standardized transfer of German claims data into OMOP CDM is currently not possible. In this paper, we conducted an evaluation regarding the coverage of source vocabularies and data elements of German claims data in OMOP CDM. We point out the need to extend vocabularies and mappings to support research on German claims data.

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. Using the widespread communication standard HL7 FHIR can reduce the complexity of the transformation process to OMOP CDM. Enabling German university hospitals to participate in such networks requires an Extract, Transform and Load (ETL)-process that satisfies the following criteria: 1) transforming German patient data from FHIR to OMOP CDM, 2) processing huge amount of data at once and 3) flexibility to cope with changes in FHIR profiles. METHOD: A mapping of German patient data from FHIR to OMOP CDM was accomplished, validated by an interdisciplinary team and checked through the OHDSI Data Quality Dashboard (DQD). To satisfy criteria 2-3, we decided to use SpringBatch-Framework according to its chunk-oriented design and reusable functions for processing large amounts of data. RESULTS: We have successfully developed an ETL-process that fulfills the defined criteria of transforming German patient data from FHIR into OMOP CDM. To measure the validity of the mapping conformance and performance of the ETL-process, it was tested with 392,022 FHIR resources. The ETL execution lasted approximately-one minute and the DQD result shows 99% conformance in OMOP CDM. CONCLUSIONS: Our ETL-process has been successfully tested and integrated at 10 German university hospitals. The data harmonization utilizing international recognized standards like FHIR and OMOP fosters their ability to participate in international observational studies. Additionally, the ETL process can help to prepare more German hospitals with their data harmonization journey based on existing standards.

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. 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.

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. As a first result, we defined an algorithm to extract treatment processes based on the standardized Observational Medical Outcomes Partnership (OMOP) Common Data Model as well as their visualization using the graphical modeling language Business Process Model and Notation (BPMN).

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. This paper provides a concept to prepare electronic health record (EHR) drug data for the transfer to OMOP based on requirements analysis and descriptive statistics for profiling EHR data developed by an interdisciplinary team and also covers data quality issues. The concept not only ensures FAIR principles for research, but provides the foundation for German drug data to OMOP transfer.

[Interdisciplinary care path and potential IT support for people with rare diseases in Germany]. / Interdisziplinärer Versorgungspfad und mögliche IT-Unterstützung für Menschen mit Seltenen Erkrankungen in Deutschland.

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. The often long period of uncertainty about the cause of symptoms as well as non-specific or even wrong therapies can have negative effects on both the course of disease and the patients' quality of life. For a better understanding of the current care situation and IT landscape, the interdisciplinary care pathway for people with rare diseases will be modelled and the possible uses of IT applications identified that have the potential to improve diagnosis, treatment and therapy of rare diseases. METHODS: In order to achieve these goals, an initial care pathway was modelled on the basis of process descriptions which are commonly used in the literature, discussed in detail, and agreed upon in a first workshop with six experts from outpatient and inpatient care as well as employees of Centers for Rare Diseases. In a second workshop, ten experts analyzed the resulting care pathway with regard to the possible use of IT applications, and the identification was agreed upon. The experts included those involved in the process, in particular physicians, patients / patient representatives, health care researchers, and experts in hospital IT, IT security, and data protection. RESULTS: The two workshops resulted in process models including the specification of possible uses for IT applications. The most important steps in the care pathway for people with rare diseases in Germany include: neonatal screening, seeking medical advice, outpatient care by general practitioners, outpatient care by specialists, care by specialist outpatient clinic, care by clinic, care by a Center for Rare Diseases: case review and case conference and treatment and therapy. The discussion of the possible uses of IT applications resulted in a focus on registers (e. g. with regard to experts, treatment and therapy options) as well as on digital tools, such as "digital findings and findings platform" and "digital referral with referral tracking". DISCUSSION: Our results show that the care pathway is very heterogeneous and complex. Thus, the sub-processes show different variants with many branches and repetitions. They also illustrate that the care for people with rare diseases requires a high level of interdisciplinary collaboration; diagnosis as well as treatment and therapy often take place across sectors and in cooperation between different medical health care institutions and professions. When analyzing the current IT landscape, it becomes clear that IT applications can be used at many process steps in the care for people with rare diseases and have a high potential. Therefore, they must be used to inform decisions about the adequate diagnosis and treatment as well as communication about the clinical pictures and the patient's case between practitioners and medical care sectors. CONCLUSION: The interdisciplinary collaboration highlights the need for cooperation between the various parties involved in the process, which requires the identification and implementation of interfaces between the stakeholders and their systems. However, it is not enough to include the view of the processes; the data perspective is also required. Creating interoperability also enables the use of IT applications. The basis for this is the results obtained.

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. For a higher visibility of rare diseases and high-quality research, effective documentation and use of data are essential. The aim of this work is to optimize the processing, use and accessibility of data on rare diseases and thus increase the added value from existing information. While dashboards are already being used to visualize clinical data, it is unclear what requirements are prevalent for rare diseases and how these can be implemented with available development tools so that a highly accepted dashboard can be designed. For this purpose, based on an analysis of the current situation and a requirements analysis, a prototype dashboard for the visualization of up-to-date key figures on rare diseases was developed at the University Hospital Carl Gustav Carus in Dresden. The development was based on the user-centered design process in order to achieve a high-level user-friendliness. The requirements analysis identified parameters that stakeholders wanted to see, focusing primarily on statistical analyses. The dashboard handles the automated calculation of statistics as well as their preparation and provision. The evaluations showed the prototypical dashboard would be considered valuable and used by potential users. This work demonstrates that stakeholders are interested in access to prepared information and exemplifies a way to implement it. The dashboard can increase the usage of existing information in terms of a higher accessibility and thus improve the knowledge about rare diseases.

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. Since 2016, the number of publications has been constantly growing and the relevance of the OMOP CDM is increasing in terms of multi-country studies based on observational patient data.

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.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.