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.

Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review.

BACKGROUND: Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes. OBJECTIVE: In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata. METHODS: We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization. RESULTS: Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases. CONCLUSIONS: Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data.

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.

Fitness for Use of Anatomical Therapeutic Classification for Real World Data Research.

INTRODUCTION: Real-world data (RWD) is gaining importance in research. For instance, the European Medicines Agency (EMA) is currently in the process of establishing a cross-national research network that utilizes RWD for research. However, data harmonization across countries must be carefully considered to avoid misclassification and bias. OBJECTIVES: This paper aims to investigate the extent to which a correct assignment of RxNorm ingredients is possible for medication orders that include only ATC codes. METHODS: In this study, we analyzed 1,506,059 medication orders from the University Hospital Dresden (UKD) and merged them with the ATC vocabulary in the Observational Medical Outcomes Partnership (OMOP) including relevant relationship mappings to RxNorm. RESULTS: We identified 70.25% of all medication orders were single ingredients with direct mapping to RxNorm. However, we also identified a significant complexity in mappings for the other medication orders that was visualized in an interactive scatterplot. DISCUSSION: The majority of medication orders under observation (70.25%) are single ingredients and can be standardized to RxNorm, combination drugs pose a challenge due to the different approaches of ingredient assignments in ATC and RxNorm. The provided visualization can help research teams gain a better understanding of problematic data and further investigate identified issues.

Towards ETL Processes to OMOP CDM Using Metadata and Modularization.

OMOP common data model (CDM) is designed for analyzing large clinical data and building cohorts for medical research, which requires Extract-Transform-Load processes (ETL) of local heterogeneous medical data. We present a concept for developing and evaluating a modularized metadata-driven ETL process, which can transform data into OMOP CDM regardless of 1) the source data format, 2) its versions and 3) context of use.

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.

Opportunities of Digital Infrastructures for Disease Management-Exemplified on COVID-19-Related Change in Diagnosis Counts for Diabetes-Related Eye Diseases.

Background: Retrospective research on real-world data provides the ability to gain evidence on specific topics especially when running across different sites in research networks. Those research networks have become increasingly relevant in recent years; not least due to the special situation caused by the COVID-19 pandemic. An important requirement for those networks is the data harmonization by ensuring the semantic interoperability. Aims: In this paper we demonstrate (1) how to facilitate digital infrastructures to run a retrospective study in a research network spread across university and non-university hospital sites; and (2) to answer a medical question on COVID-19 related change in diagnostic counts for diabetes-related eye diseases. Materials and methods: The study is retrospective and non-interventional and runs on medical case data documented in routine care at the participating sites. The technical infrastructure consists of the OMOP CDM and other OHDSI tools that is provided in a transferable format. An ETL process to transfer and harmonize the data to the OMOP CDM has been utilized. Cohort definitions for each year in observation have been created centrally and applied locally against medical case data of all participating sites and analyzed with descriptive statistics. Results: The analyses showed an expectable drop of the total number of diagnoses and the diagnoses for diabetes in general; whereas the number of diagnoses for diabetes-related eye diseases surprisingly decreased stronger compared to non-eye diseases. Differences in relative changes of diagnoses counts between sites show an urgent need to process multi-centric studies rather than single-site studies to reduce bias in the data. Conclusions: This study has demonstrated the ability to utilize an existing portable and standardized infrastructure and ETL process from a university hospital setting and transfer it to non-university sites. From a medical perspective further activity is needed to evaluate data quality of the utilized real-world data documented in routine care and to investigate its eligibility of this data for research.

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).