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.

Streamlining intersectoral provision of real-world health data: a service platform for improved clinical research and patient care.

Introduction: Obtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities - including hospitals, outpatient clinics, and physician practices - the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites. Methods: We investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term. Results: We have developed the pre-built packages "ResearchData-to-FHIR," "FHIR-to-OMOP," and "Addons," which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use. Conclusion: Our development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

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.

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.

Specification and Distribution of Vocabularies Among Consortial Partners.

Due to the variety of different software systems and disparate observational databases, the need for a uniform data representation rises. Common data models (CDM) support the harmonisation of data. A powerful but compact software setup and a minimum vocabulary set has been composed via Docker to facilitate analysis of data across ten university hospitals. The presented approach also creates the possibility to use a concise database which is easy to deploy.