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TAPP: Defining standard provenance information for clinical research data and workflows - Obstacles and opportunities

Authors:

Kerstin Gierend,

Judith A.H. Wodke,

Markus Mandalka,

Alexander Scheuerlein,

Atinkut Zeleke,

Dagmar WaltemathAuthors Info & Claims

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

Pages 1551 - 1554

https://doi.org/10.1145/3543873.3587562

Published: 30 April 2023 Publication History

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Abstract

Data provenance has raised much attention across disciplines lately, as it has been shown that enrichment of data with provenance information leads to better credibility, renders data more FAIR fostering data reuse. Also, the biomedical domain has recognised the potential of provenance capture. However, several obstacles prevent efficient, automated, and machine-interpretable enrichment of biomedical data with provenance information, such as data heterogeneity, complexity, and sensitivity. Here, we explain how in Germany clinical data are transferred from hospital information systems into a data integration centre to enable secondary use of patient data and how it can be reused as research data. Considering the complex data infrastructures in hospitals, we indicate obstacles and opportunities when collecting provenance information along heterogeneous data processing pipelines. To express provenance data, we indicate the usage of the Fast Healthcare Interoperability Resource (FHIR) provenance resource for healthcare data. In addition, we consider already existing approaches from other research fields and standard communities. As a solution towards high-quality standardised clinical research data, we propose to develop a ’MInimal Requirements for Automated Provenance Information Enrichment’ (MIRAPIE) guideline. As a community project, MIRAPIE should generalise provenance information concepts to allow its world-wide applicability, possibly beyond the health care sector.

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Cited By

Kohrs FAuer SBannach-Brown AFiedler SHaven THeise VHolman CAzevedo FBernard RBleier ABössel NCahill BCastro LEhrenhofer AEichel KFrank MFrick CFriese MGärtner AGierend KGrüning DHahn LHülsemann MIhle MIllius SKönig LKönig MKulke LKutlin ALammers FMehler DMiehl CMüller-Alcazar ANeuendorf CNiemeyer HPargent FPeikert APfeuffer CReinecke RRöer JRohmann JSánchez-Tójar AScherbaum SSixtus ESpitzer LStraßburger VWeber MWhitmire CZerna JZorbek DZumstein PWeissgerber T(2023)Eleven strategies for making reproducible research and open science training the norm at research institutionseLife10.7554/eLife.8973612Online publication date: 23-Nov-2023
https://doi.org/10.7554/eLife.89736
Gierend KWaltemath DGanslandt TSiegel F(2023)Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept StudyJMIR Formative Research10.2196/500277(e50027)Online publication date: 7-Dec-2023
https://doi.org/10.2196/50027
Gierend KFreiesleben SKadioglu DSiegel FGanslandt TWaltemath D(2023)The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods StudyJournal of Medical Internet Research10.2196/4880925(e48809)Online publication date: 8-Nov-2023
https://doi.org/10.2196/48809

Index Terms

TAPP: Defining standard provenance information for clinical research data and workflows - Obstacles and opportunities
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Information systems
  1. Data management systems
    1. Information integration
      1. Extraction, transformation and loading

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Published In

cover image ACM Conferences

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

April 2023

1567 pages

ISBN:9781450394192

DOI:10.1145/3543873

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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Association for Computing Machinery

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Publication History

Published: 30 April 2023

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WWW '23

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Cited By

Kohrs FAuer SBannach-Brown AFiedler SHaven THeise VHolman CAzevedo FBernard RBleier ABössel NCahill BCastro LEhrenhofer AEichel KFrank MFrick CFriese MGärtner AGierend KGrüning DHahn LHülsemann MIhle MIllius SKönig LKönig MKulke LKutlin ALammers FMehler DMiehl CMüller-Alcazar ANeuendorf CNiemeyer HPargent FPeikert APfeuffer CReinecke RRöer JRohmann JSánchez-Tójar AScherbaum SSixtus ESpitzer LStraßburger VWeber MWhitmire CZerna JZorbek DZumstein PWeissgerber T(2023)Eleven strategies for making reproducible research and open science training the norm at research institutionseLife10.7554/eLife.8973612Online publication date: 23-Nov-2023
https://doi.org/10.7554/eLife.89736
Gierend KWaltemath DGanslandt TSiegel F(2023)Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept StudyJMIR Formative Research10.2196/500277(e50027)Online publication date: 7-Dec-2023
https://doi.org/10.2196/50027
Gierend KFreiesleben SKadioglu DSiegel FGanslandt TWaltemath D(2023)The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods StudyJournal of Medical Internet Research10.2196/4880925(e48809)Online publication date: 8-Nov-2023
https://doi.org/10.2196/48809

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