TY  - JOUR
A1  - Sommer, Kim K.
A1  - Amr, Ali
A1  - Bavendiek, Udo
A1  - Beierle, Felix
A1  - Brunecker, Peter
A1  - Dathe, Henning
A1  - Eils, Jürgen
A1  - Ertl, Maximilian
A1  - Fette, Georg
A1  - Gietzelt, Matthias
A1  - Heidecker, Bettina
A1  - Hellenkamp, Kristian
A1  - Heuschmann, Peter
A1  - Hoos, Jennifer D. E.
A1  - Kesztyüs, Tibor
A1  - Kerwagen, Fabian
A1  - Kindermann, Aljoscha
A1  - Krefting, Dagmar
A1  - Landmesser, Ulf
A1  - Marschollek, Michael
A1  - Meder, Benjamin
A1  - Merzweiler, Angela
A1  - Prasser, Fabian
A1  - Pryss, Rüdiger
A1  - Richter, Jendrik
A1  - Schneider, Philipp
A1  - Störk, Stefan
A1  - Dieterich, Christoph
T1  - Structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores
JF  - Life
N2  - Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.
KW  - medical informatics initiative
KW  - HiGHmed
KW  - medical data integration center
KW  - clinical routine data
KW  - heart failure
KW  - risk prediction scores
KW  - semantic interoperability
KW  - openEHR
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-275239
SN  - 2075-1729
VL  - 12
IS  - 5
ER  - 
TY  - JOUR
A1  - Beierle, Felix
A1  - Schobel, Johannes
A1  - Vogel, Carsten
A1  - Allgaier, Johannes
A1  - Mulansky, Lena
A1  - Haug, Fabian
A1  - Haug, Julian
A1  - Schlee, Winfried
A1  - Holfelder, Marc
A1  - Stach, Michael
A1  - Schickler, Marc
A1  - Baumeister, Harald
A1  - Cohrdes, Caroline
A1  - Deckert, Jürgen
A1  - Deserno, Lorenz
A1  - Edler, Johanna-Sophie
A1  - Eichner, Felizitas A.
A1  - Greger, Helmut
A1  - Hein, Grit
A1  - Heuschmann, Peter
A1  - John, Dennis
A1  - Kestler, Hans A.
A1  - Krefting, Dagmar
A1  - Langguth, Berthold
A1  - Meybohm, Patrick
A1  - Probst, Thomas
A1  - Reichert, Manfred
A1  - Romanos, Marcel
A1  - Störk, Stefan
A1  - Terhorst, Yannik
A1  - Weiß, Martin
A1  - Pryss, Rüdiger
T1  - Corona Health — A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic
JF  - International Journal of Environmental Research and Public Health
N2  - Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
KW  - mobile health
KW  - ecological momentary assessment
KW  - digital phenotyping
KW  - longitudinal studies
KW  - mobile crowdsensing
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242658
SN  - 1660-4601
VL  - 18
IS  - 14
ER  -