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Published year: 20224
Implementation of a clinical trial recruitment support system based on fast healthcare interoperability resources (FHIR) in a cardiology department
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Clinical Trial Recruitment Support Systems can booster patient inclusion of clinical trials by automatically analyzing eligibility criteria based on electronic health records. However, missing … interoperability has hindered introduction of those systems on a broader scale. Therefore, our aim was to develop a recruitment support system based on FHIR R4 and evaluate its usage and features in a cardiology department. Clinical conditions, anamnesis, examinations, allergies, medication, laboratory data and echocardiography results were imported as FHIR resources. Clinical trial information, eligibility criteria and recruitment status were recorded using the appropriate FHIR resources without extensions. Eligibility criteria linked by the logical operation “OR” were represented by using multiple FHIR Group resources for enrollment. The system was able to identify 52 of 55 patients included in four clinical trials. In conclusion, use of FHIR for defining eligibility criteria of clinical trials may facilitate interoperability and allow automatic screening for eligible patients at multiple sites of different healthcare providers in the future. Upcoming changes in FHIR should allow easier description of “OR”-linked eligibility criteria.

Authors: Clemens Scherer, Stephan Endres, Martin Orban, Stefan Kääb, Steffen Massberg, Alfred Winter, Matthias Löbe

Date Published: 1st May 2022

Publication Type: Journal article

Multi-institutional breast cancer detection using a secure on-boarding service for Distributed Analytics
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data … due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.

Authors: Sascha Welten, Lars Hempel, Masoud Abedi, Yongli Mou, Mehrshad Jaberansary, Laurenz Neumann, Sven Weber, Kais Tahar, Yeliz Ucer Yediel, Matthias Löbe, Stefan Decker, Oya Beyan, Toralf Kirsten

Date Published: 1st Apr 2022

Publication Type: Journal article

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm
FAIR4Health

Abstract (Expand)

The current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a …t also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.

Authors: Jonás Carmona-Pírez, Beatriz Poblador-Plou, Antonio Poncel-Falcó, Jessica Rochat, Celia Alvarez-Romero, Alicia Martínez-García, Carmen Angioletti, Marta Almada, Mert Gencturk, A. Anil Sinaci, Jara Eloisa Ternero-Vega, Christophe Gaudet-Blavignac, Christian Lovis, Rosa Liperoti, Elisio Costa, Carlos Luis Parra-Calderón, Aida Moreno-Juste, Antonio Gimeno-Miguel, Alexandra Prados-Torres

Date Published: 1st Feb 2022

Publication Type: Journal article

Towards harmonized data quality in the medical informatics initiative - current state and future directions
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Health data from hospital information systems are valuable sources for medical research but have known issues in terms of data quality. In a nationwide data integration project in Germany, health care … data from all participating university hospitals are being pooled and refined in local centers. As there is currently no overarching agreement on how to deal with errors and implausibilities, meetings were held to discuss the current status and the need to develop consensual measures at the organizational and technical levels. This paper analyzes the discovered similarities and differences. The result shows that although data quality checks are carried out at all sites, there is a lack of both centrally coordinated data quality indicators and a formalization of plausibility rules as well as a repository for automatic querying of the rules, for example in ETL processes.

Authors: Matthias Löbe, Gaetan Kamdje-Wabo, Adriana Carina Sinza, Helmut Spengler, Marcus Strobel, Erik Tute

Date Published: 2022

Publication Type: Journal article

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