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.

Supporting regional pandemic management by enabling self-service reporting-A case report

BACKGROUND

The COVID-19 pandemic revealed a need for better collaboration among research, care, and management in Germany as well as globally. Initially, there was a high demand for broad data collection across Germany, but as the pandemic evolved, localized data became increasingly necessary. Customized dashboards and tools were rapidly developed to provide timely and accurate information. In Saxony, the DISPENSE project was created to predict short-term hospital bed capacity demands, and while it was successful, continuous adjustments and the initial monolithic system architecture of the application made it difficult to customize and scale.

METHODS

To analyze the current state of the DISPENSE tool, we conducted an in-depth analysis of the data processing steps and identified data flows underlying users' metrics and dashboards. We also conducted a workshop to understand the different views and constraints of specific user groups, and brought together and clustered the information according to content-related service areas to determine functionality-related service groups. Based on this analysis, we developed a concept for the system architecture, modularized the main services by assigning specialized applications and integrated them into the existing system, allowing for self-service reporting and evaluation of the expert groups' needs.

RESULTS

We analyzed the applications' dataflow and identified specific user groups. The functionalities of the monolithic application were divided into specific service groups for data processing, data storage, predictions, content visualization, and user management. After composition and implementation, we evaluated the new system architecture against the initial requirements by enabling self-service reporting to the users.

DISCUSSION

By modularizing the monolithic application and creating a more flexible system, the challenges of rapidly changing requirements, growing need for information, and high administrative efforts were addressed.

CONCLUSION

We demonstrated an improved adaptation towards the needs of various user groups, increased efficiency, and reduced burden on administrators, while also enabling self-service functionalities and specialization of single applications on individual service groups.

Evaluation of Modeling Approaches for a Clinical Data Warehouse in a Highly Dynamic Environment

The availability of clinical data for researchers is crucial for an improvement of healthcare and research. For this purpose, the integration, harmonization and standardization of healthcare-data from various sources in a clinical data warehouse (CDWH) is highly relevant. Our evaluation taking into account the general conditions and requirements of the project, led us to choose the Data Vault approach for the development of a clinical data warehouse at the University Hospital Dresden (UHD).

Regional responsibility and coordination of appropriate inpatient care capacities for patients with COVID-19 - the German DISPENSE model

As of late 2019, the COVID-19 pandemic has been a challenge to health care systems worldwide. Rapidly rising local COVID-19 incidence rates, result in demand for high hospital and intensive care bed capacities on short notice. A detailed up-to-date regional surveillance of the dynamics of the pandemic, precise prediction of required inpatient capacities of care as well as a centralized coordination of the distribution of regional patient fluxes is needed to ensure optimal patient care. In March 2020, the German federal state of Saxony established three COVID-19 coordination centers located at each of its maximum care hospitals, namely the University Hospitals Dresden and Leipzig and the hospital Chemnitz. Each center has coordinated inpatient care facilities for the three regions East, Northwest and Southwest Saxony with 36, 18 and 29 hospital sites, respectively. Fed by daily data flows from local public health authorities capturing the dynamics of the pandemic as well as daily reports on regional inpatient care capacities, we established the information and prognosis tool DISPENSE. It provides a regional overview of the current pandemic situation combined with daily prognoses for up to seven days as well as outlooks for up to 14 days of bed requirements. The prognosis precision varies from 21% and 38% to 12% and 15% relative errors in normal ward and ICU bed demand, respectively, depending on the considered time period. The deployment of DISPENSE has had a major positive impact to stay alert for the second wave of the COVID-19 pandemic and to allocate resources as needed. The application of a mathematical model to forecast required bed capacities enabled concerted actions for patient allocation and strategic planning. The ad-hoc implementation of these tools substantiates the need of a detailed data basis that enables appropriate responses, both on regional scales in terms of clinic resource planning and on larger scales concerning political reactions to pandemic situations.