Performance of Contradiction Rule Implementations in Health Data for Efficient Data Quality Assessments.

Introduction: Boolean rules are the building blocks for rule-based data quality assessment (DQA) in health research. While some DQA rules are generic, contradiction rules are guided by established facts supported by domain knowledge. A recent study reported performance degradation in infrastructure as DQA rules scale.

Randomized adaptive assessment of post COVID syndrome treatments (RAPID): a study protocol for a multicenter, randomized, controlled adaptive platform trial of treatment options for Post Covid Syndrome (PCS) on patients physical function including the first intervention specific appendix RAPID_REVIVE (reducing inflammatory activity in patients with PCS).

Background: The majority of patients recovers from severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) coronavirus disease 2019 (COVID-19) without obvious sequelae, but a significant proportion suffers long-term consequences which have been termed post COVID syndrome (PCS). Despite a wide range of considerations on treatment options in PCS and a significant number of trials initiated, only very few results from randomized controlled trials are currently available. In conclusion, there is an evident medical need to identify treatments for patients with PCS.

Correction: Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema.

[This corrects the article DOI: 10.2196/63906.].

Managing withdrawals and exclusions of study participants in COVID-19-research by NUKLEUS.

Background: This article describes how withdrawals and exclusions of study participants can be managed in COVID-19-cohort studies by NUKLEUS (German: NUM Klinische Epidemiologie- und Studienplattform), using NAPKON (German: Nationales Pandemie Kohorten Netz). The aim of this manuscript was to describe, how partial withdrawals can be performed so that most of the data and bio-samples can be kept for research purposes.

Methods: The study has taken all signed informed consents (ICs) of study participants into account in order to develop a method how partial withdrawals can be developed and installed.

Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema.

Background: Despite wide acceptance in medical research, implementation of the FAIR (findability, accessibility, interoperability, and reusability) principles in certain health domains and interoperability across data sources remain a challenge. While clinical trial registries collect metadata about clinical studies, numerous epidemiological and public health studies remain unregistered or lack detailed information about relevant study documents. Making valuable data from these studies available to the research community could improve our understanding of various diseases and their risk factors.

[Access to and use of Data for better Healthcare: A Plea for a cooperative data and Research Infrastructure of Statutory and Private Health Insurers and the Network University Medicine (NUM)].

With the Network of University Medicine (NUM) and the Medical Informatics Initiative (MII), the BMBF is funding two pioneering, structure-building research measures that are now being merged. The data integration centers (DIZ) of the MII are to be consolidated in the NUM. The aim is to establish a standardized research infrastructure within which the existing data from the clinical routine care of the 36 German university hospitals, from clinical cohorts and clinical-epidemiological studies can be used for various research questions upon request and via coordinated processes.

An Open Information Model-Based Repository for Sustainable Re-Use of Heterogeneous Pandemics Research Data.

The COVID-19 Research Network Lower Saxony (COFONI) is a German state network of experts in Coronavirus research and development of strategies for future pandemics. One of the pillars of the COFONI technology platform is its established research data repository (Available at https://forschungsdb.cofoni.

[Integration of Inventory Data from Cohort and Registry Studies into an Existing Research Network: National Pandemic Cohort Network (NAPKON)].

In the early phase of the COVID-19 pandemic, many local collections of clinical data on patients infected with SARS-CoV-2 were initiated in Germany. As part of the National Pandemic Cohort Network (NAPKON) of the University Medicine Network, the "Integration Core" was established to design the legal, technical and organisational requirements for the integration of inventory data into ongoing prospective data collections and to test the feasibility of the newly developed solutions using use cases (UCs). Detailed study documents of the data collections were obtained.

Depression and fatigue six months post-COVID-19 disease are associated with overlapping symptom constellations: A prospective, multi-center, population-based cohort study.

Background: Depression and fatigue are commonly observed sequelae following viral diseases such as COVID-19. Identifying symptom constellations that differentially classify post-COVID depression and fatigue may be helpful to individualize treatment strategies. Here, we investigated whether self-reported post-COVID depression and post-COVID fatigue are associated with the same or different symptom constellations.

Impact of Clinical Study Implementation on Data Quality Assessments - Using Contradictions within Interdependent Health Data Items as a Pilot Indicator.

Introduction: Contradiction is a relevant data quality indicator to evaluate the plausibility of interdependent health data items. However, while contradiction assessment is achieved using domain-established contradictory dependencies, recent studies have shown the necessity for additional requirements to reach conclusive contradiction findings. For example, the oral or rectal methods used in measuring the body temperature will influence the thresholds of fever definition.

Adaptability of Existing Feasibility Tools for Clinical Study Research Data Platforms.

Introduction: The increasing need for secondary use of clinical study data requires FAIR infrastructures, i.e. provide findable, accessible, interoperable and reusable data.

Reusing Biomedical Data as Agreed - Towards Structured Metadata for Data Use Agreements.

Introduction: With increasing availability of reusable biomedical data - from cohort studies to clinical routine data, data re-users face the problem to manage transferred data according to the heterogeneous data use agreements. While structured metadata is addressed in many contexts including informed consent, contracts are to date still unstructured text documents. In particular within collaborative and active working groups the actual usage agreement's regulations are highly relevant for the daily practice - can I share the data with colleagues from the same university or the same research network, can they be stored on a PHD student's laptop, can I store the data for further approved data usage requests?

Methods: In this article, we inspect and review seven different data usage agreements.

Towards a Consistent Representation of Contradictions Within Health Data for Efficient Implementation of Data Quality Assessments.

Contradictions as a data quality indicator are typically understood as impossible combinations of values in interdependent data items. While the handling of a single dependency between two data items is well established, for more complex interdependencies, there is not yet a common notation or structured evaluation method established to our knowledge. For the definition of such contradictions, specific biomedical domain knowledge is required, while informatics domain knowledge is responsible for the efficient implementation in assessment tools.

The Importance of Being FAIR and FAST - The Clinical Epidemiology and Study Platform of the German Network University Medicine (NUKLEUS).

The COVID-19 pandemic has urged the need to set up, conduct and analyze high-quality epidemiological studies within a very short time-scale to provide timely evidence on influential factors on the pandemic, e.g. COVID-19 severity and disease course.

The DZHK research platform: maximisation of scientific value by enabling access to health data and biological samples collected in cardiovascular clinical studies.

The German Centre for Cardiovascular Research (DZHK) is one of the German Centres for Health Research and aims to conduct early and guideline-relevant studies to develop new therapies and diagnostics that impact the lives of people with cardiovascular disease. Therefore, DZHK members designed a collaboratively organised and integrated research platform connecting all sites and partners. The overarching objectives of the research platform are the standardisation of prospective data and biological sample collections among all studies and the development of a sustainable centrally standardised storage in compliance with general legal regulations and the FAIR principles.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

Background: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO.

The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics.

The German government initiated the Network University Medicine (NUM) in early 2020 to improve national research activities on the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. To this end, 36 German Academic Medical Centers started to collaborate on 13 projects, with the largest being the National Pandemic Cohort Network (NAPKON). The NAPKON's goal is creating the most comprehensive Coronavirus Disease 2019 (COVID-19) cohort in Germany.

Integration of decentralized clinical data in a data warehouse: a service-oriented design and realization.

Objectives: In this paper we present a general concept and describe the difficulties for the integration of data from various clinical partners in one data warehouse using the Open European Nephrology Science Center (OpEN.SC) as an example. This includes a requirements analysis of the data integration process and also the design according to these requirements.