BMC Med Inform Decis Mak
August 2022
Background: With the growing impact of observational research studies, there is also a growing focus on data quality (DQ). As opposed to experimental study designs, observational research studies are performed using data mostly collected in a non-research context (secondary use). Depending on the number of data elements to be analyzed, DQ reports of data stored within research networks can grow very large.
View Article and Find Full Text PDFThe academic research environment is characterized by self-developed, innovative, customized solutions, which are often free to use for third parties with open-source code and open licenses. On the other hand, they are maintained only to a very limited extent after the end of project funding. The ToolPool Gesundheitsforschung addresses the problem of finding ready to use solutions by building a registry of proven and supported tools, services, concepts and consulting offers.
View Article and Find Full Text PDFBackground: Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium's partner sites.
View Article and Find Full Text PDFStud Health Technol Inform
May 2021
Introduction: The aim of this study is to evaluate the use of a natural language processing (NLP) software to extract medication statements from unstructured medical discharge letters.
Methods: Ten randomly selected discharge letters were extracted from the data warehouse of the University Hospital Erlangen (UHE) and manually annotated to create a gold standard. The AHD NLP tool, provided by MIRACUM's industry partner was used to annotate these discharge letters.
Semantic interoperability is a major challenge in multi-center data sharing projects, a challenge that the German Initiative for Medical Informatics is taking up. With respect to laboratory data, enriching site-specific tests and measurements with LOINC codes appears to be a crucial step in supporting cross-institutional research. However, this effort is very time-consuming, as it requires expert knowledge of local site specifics.
View Article and Find Full Text PDFObjectives: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions.
Design: Retrospective cohort study.
Setting: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe.
We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.
View Article and Find Full Text PDFStud Health Technol Inform
September 2019
Antimycotics are substances to treat fungal infections, a frequent cause of death on intensive care units. It is of great importance to administer such drugs only to patients who actually need them, since the unnecessary application leads to the selection of multiresistent fungi, making future therapy more difficult, and represents a significant financial burden for the health care system. Within the scope of a prospective study, which analyses the premature discontinuation of the administration of unnecessary antifungal drugs, two software platforms for recruitment support were implemented and compared at the University Hospital Erlangen.
View Article and Find Full Text PDFStud Health Technol Inform
September 2019
Introduction: Data quality (DQ) is an important prerequisite for secondary use of electronic health record (EHR) data in clinical research, particularly with regards to progressing towards a learning health system, one of the MIRACUM consortium's goals. Following the successful integration of the i2b2 research data repository in MIRACUM, we present a standardized and generic DQ framework.
State Of The Art: Already established DQ evaluation methods do not cover all of MIRACUM's requirements.
Stud Health Technol Inform
August 2019
Background: To make patient care data more accessible for research, German university hospitals join forces in the course of the Medical Informatics Initiative. In a first step, the administrative data of university hospitals is made available for federated utilization. Project-specific de-identification of this data is necessary to satisfy privacy laws.
View Article and Find Full Text PDFStud Health Technol Inform
September 1999
The University of Erlangen-Nuremberg contains 22 hospitals and 11 autonomous medical departments which are spread out over a large area in the city of Erlangen. The necessary connections of these units and their computer based subsystems to each other and to the medical computer centre via fibre optics cables is complete. The internal cabling of the individual units is largely completed.
View Article and Find Full Text PDFStud Health Technol Inform
February 1998
The Erlangen communication hub allows medical subsystems to exchange data by two completely different methods. Since 1995 a communication data base, which is implemented using the relational data base system ADABAS D, contains data from the most important hospital systems. This data can be accessed by other medical systems.
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