Federated electronic data capture (fEDC): Architecture and prototype.

In clinical research as well as patient care, structured documentation of findings is an important task. In many cases, this is achieved by means of electronic case report forms (eCRF) using corresponding information technology systems. To avoid double data entry, eCRF systems can be integrated with electronic health records (EHR). However, when researchers from different institutions collaborate in collecting data, they often use a single joint eCRF system on the Internet. In this case, integration with EHR systems is not possible in most cases due to information security and data protection restrictions. To overcome this shortcoming, we propose a novel architecture for a federated electronic data capture system (fEDC). Four key requirements were identified for fEDC: Definitions of forms have to be available in a reliable and controlled fashion, integration with electronic health record systems must be possible, patient data should be under full local control until they are explicitly transferred for joint analysis, and the system must support data sharing principles accepted by the scientific community for both data model and data captured. With our approach, sites participating in a joint study can run their own instance of an fEDC system that complies with local standards (such as being behind a network firewall) while also being able to benefit from using identical form definitions by sharing metadata in the Operational Data Model (ODM) format published by the Clinical Data Interchange Standards Consortium (CDISC) throughout the collaboration. The fEDC architecture was validated with a working open-source prototype at five German university hospitals. The fEDC architecture provides a novel approach with the potential to significantly improve collaborative data capture: Efforts for data entry are reduced and at the same time, data quality is increased since barriers for integrating with local electronic health record systems are lowered. Further, metadata are shared and patient privacy is ensured at a high level.

ODM-DQA-Reporter: A Generic Approach to Assess and Monitor Basic Data Quality of Medical Research Data in Operational Data Model (ODM) Format.

A generic approach for assessment and continuous monitoring of data quality in ODM-based research data has been developed. The focus is on the two data quality indicators completeness and syntactic correctness. The main idea is to enable the generation of a data quality report without additional programming effort.

Linking EMR Data to REDCap: Implementation in the SOLKID Register.

Secondary use, the reuse of medical patient data stored during routine care in the hospital's electronic medical records (EMR) for research purpose is common, especially for registers and pragmatic trials. Often the medical data items are copied manually from the EMR into the used research database. This process is time consuming and error prone. In the "Safety of the Living Kidney Donor - The German National Register" (SOLKID-GNR), laboratory results gathered during control check-ups of the living donors before and after the transplantation are to be transferred from the EMR into the electronic data capture system REDCap of the register. In this work, we present our approach of realizing an automated transfer of time-dependent laboratory results from the EMR of the University Hospital of Münster to REDCap. A challenge lies in the multi-center structure of SOLKID-GNR. The participating transplant centers are using different EMR systems, which requires a flexible architecture design. In addition, we aimed to support reuse of the implementation for other research settings with other medical data items of interest.

An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC): Design and Evaluation Study.

BACKGROUND: Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. OBJECTIVE: The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. METHODS: We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. RESULTS: We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. CONCLUSIONS: Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.

A Seamless Pseudonymization and Randomization Workflow for REDCap.

The interaction of multiple computer systems during multi-center randomized controlled trials (RCTs) is a hurdle for IT-specialists as well as medical staff. A common workflow for the initial registration of a patient requires the generation of a pseudonym by a pseudonymization service, a manual transmission of the pseudonym to a randomization service, and a manual transfer of the pseudonym and assigned study arm into an electronic data capture (EDC) system. This interaction is often time consuming and error prone due to multiple system changes. Objective of this work is to enhance a commonly used EDC system, Research Electronic Data Capture (REDCap), as a single source of interaction for multi-center RCTs. This is achieved by providing two modules for a seamless integration of a pseudonymization service, i.e., Mainzelliste, and a randomization service, i.e., RandIMI. Thus, no site-specific system changes are required, which increases time efficiency and reduces errors. From a technical perspective, only authentication credentials and firewall exposure for a single system must be managed. To evaluate the usability of our implementation, the system usability scale was employed. The increase of time efficiency was measured in laboratory conditions by a comparison of the time for patient registrations with and without our modules. An "excellent" usability was shown and an average time reduction by nearly 64 %. Both open-source modules are available from the REDCap Repository of External Modules.

MainzelHandler: A Library for a Simple Integration and Usage of the Mainzelliste.

Pseudonymization plays a vital role in medical research. In Germany, the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) has developed guidelines on how to create pseudonyms and how to handle personally identifiable information (PII) during this process. An open-source implementation of a pseudonymization service following these guidelines and therefore recommended by the TMF is the so-called "Mainzelliste". This web application supports a REST-API for (de-) pseudonymization. For security reasons, a complex session and tokening mechanism for each (de-) pseudonymization is required and a careful interaction between front- and backend to ensure a correct handling of PII. The objective of this work is the development of a library to simplify the integration and usage of the Mainzelliste's API in a TMF conform way. The frontend library uses JavaScript while the backend component is based on Java with an optional Spring Boot extension. The library is available under MIT open-source license from https://github.com/DanielPreciado-Marquez/MainzelHandler.

Association of contact to small children with a mild course of COVID-19.

It is known that severe COVID-19 cases in small children are rare. If a childhood-related infection were protective against a severe course of COVID-19, it would be expected that adults with intensive and regular contact with small children also may have a mild course of COVID-19 more frequently. To test this hypothesis, a survey among 4010 recovered COVID-19 patients was conducted in Germany. 1186 complete answers were collected. 6.9% of these patients reported frequent and regular job-related contact with children below ten years of age, and 23.2% had their own small children, which was higher than expected. In the relatively small subgroup with intensive care treatment (n = 19), patients without contact with small children were overrepresented. These findings are not well explained by age, gender, or BMI distribution of those patients and should be validated in other settings.

Compatible Data Models at Design Stage of Medical Information Systems: Leveraging Related Data Elements from the MDM Portal.

Compatible data models are key for data integration. Data transformation after data collection has many limitations. Therefore compatible data structures should be addressed already during the design of information systems. The portal of Medical Data Models (MDM), which contains 20.000+ models and 495.000+ data items, was enhanced with a web service to identify data elements, which are frequently collected together in real information systems. Using Apache Solr, a fast search functionality to identify those elements with semantic annotations was implemented. This service was integrated into the metadata registry (MDR) component of MDM to make it available to the scientific community. It can be used to build intelligent data model editors, which suggest and import frequent data element definitions according to the current medical context.