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  1. AU="A. Anil Sinaci"
  2. AU="Najwan K. Jubair"
  3. AU="Ogden, Adam"
  4. AU="Claustre, Laurent"
  5. AU="Aithal, Advait R"
  6. AU="Hoppe, M"
  7. AU=Mohamed Islam N.
  8. AU="Ion Romulus Scorei"
  9. AU="Jarmusch, Scott A"
  10. AU="Rupesh Chikhale"
  11. AU=Mubarak Naeem
  12. AU="Lesbordes, Pierre"
  13. AU="Karine Bernardeau"
  14. AU="Post, Zachary D"
  15. AU="Mukherjee, Prattusha"
  16. AU="Yang, Wulin"
  17. AU=Jang Kyoung-Min
  18. AU="Shilpakar, Ramila"
  19. AU="Azad, Sajad"
  20. AU="Connolly, John A"
  21. AU="Šmit, Žiga"
  22. AU=Lee Geun Dong
  23. AU="Laireiter, Anton-Rupert"
  24. AU=Allen Larry A
  25. AU="Jamali, Myriam"
  26. AU="Lima-Filho, Moysés de Oliveira"
  27. AU="Bowen, Ceri"
  28. AU=Demidenko Eugene
  29. AU="Hashemi-Soteh, Mohammad Bagher"
  30. AU="Monfardini, Silvio"
  31. AU="Nasim, Aqeel"
  32. AU=Stennard Fiona A

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  1. Artikel ; Online: A Nationwide Chronic Disease Management Solution via Clinical Decision Support Services

    Mustafa Mahir Ulgu / Gokce Banu Laleci Erturkmen / Mustafa Yuksel / Tuncay Namli / Şenan Postacı / Mert Gencturk / Yildiray Kabak / A Anil Sinaci / Suat Gonul / Asuman Dogac / Zübeyde Özkan Altunay / Banu Ekinci / Sahin Aydin / Suayip Birinci

    JMIR Medical Informatics, Vol 12, p e

    Software Development and Real-Life Implementation Report

    2024  Band 49986

    Abstract: BackgroundThe increasing population of older adults has led to a rise in the demand for health care services, with chronic diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministry ...

    Abstract BackgroundThe increasing population of older adults has led to a rise in the demand for health care services, with chronic diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministry of Health has initiated strategies to implement an integrated health care model for chronic disease management. We aim to present the design, development, nationwide implementation, and initial performance results of the national Disease Management Platform (DMP). ObjectiveThis paper’s objective is to present the design decisions taken and technical solutions provided to ensure successful nationwide implementation by addressing several challenges, including interoperability with existing IT systems, integration with clinical workflow, enabling transition of care, ease of use by health care professionals, scalability, high performance, and adaptability. MethodsThe DMP is implemented as an integrated care solution that heavily uses clinical decision support services to coordinate effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines and, hence, to increase the quality of health care delivery. The DMP is designed and implemented to be easily integrated with the existing regional and national health IT systems via conformance to international health IT standards, such as Health Level Seven Fast Healthcare Interoperability Resources. A repeatable cocreation strategy has been used to design and develop new disease modules to ensure extensibility while ensuring ease of use and seamless integration into the regular clinical workflow during patient encounters. The DMP is horizontally scalable in case of high load to ensure high performance. ResultsAs of September 2023, the DMP has been used by 25,568 health professionals to perform 73,715,269 encounters for 16,058,904 unique citizens. It has been used to screen and monitor chronic diseases such as obesity, cardiovascular risk, diabetes, and hypertension, resulting in ...
    Schlagwörter Computer applications to medicine. Medical informatics ; R858-859.7
    Thema/Rubrik (Code) 360
    Sprache Englisch
    Erscheinungsdatum 2024-01-01T00:00:00Z
    Verlag JMIR Publications
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm

    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

    International Journal of Environmental Research and Public Health, Vol 19, Iss 2040, p

    2022  Band 2040

    Abstract: 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 methodological challenge due to the current lack of ... ...

    Abstract 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 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.
    Schlagwörter FAIR principles ; multimorbidity ; mortality ; research data management ; pathfinder case study ; privacy-preserving distributed data mining ; Medicine ; R
    Sprache Englisch
    Erscheinungsdatum 2022-02-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: Postmarketing Safety Study Tool

    A. Anil Sinaci / Gokce B. Laleci Erturkmen / Suat Gonul / Mustafa Yuksel / Paolo Invernizzi / Bharat Thakrar / Anil Pacaci / H. Alper Cinar / Nihan Kesim Cicekli

    BioMed Research International, Vol

    A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies

    2015  Band 2015

    Abstract: Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing ... ...

    Abstract Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.
    Schlagwörter Medicine ; R
    Thema/Rubrik (Code) 306
    Sprache Englisch
    Erscheinungsdatum 2015-01-01T00:00:00Z
    Verlag Hindawi Limited
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: FAIR4Health

    Christian Lovis / Christophe Gaudet-Blavignac / Miriam Quintero / Patrick Weber / Kevin Ashley / Manuel M. Perez-Perez / Carlos Luis Parra Calderón / Laurence Horton / Celia Alvarez-Romero / A. Anil Sinaci / Alicia Martínez-García / Eva Méndez / Mert Gencturk / Rosa Liperoti / Tony Hernández-Pérez / Matthias Löbe / Carmen Angioletti / Thomas M. Deserno / Nagarajan Ganapathy /
    Elisio Costa / Marta Almada / Giorgio Cangioli / Catherine Chronaki / Beatriz Poblador-Plou / Ronald Cornet / Antonio Gimeno-Miguel / Jonás Carmona-Pírez / Alexandra Prados-Torres / Antonio Poncel-Falcó / Bojan Zaric / Tomi Kovacevic / Sanja Hromis / Darijo Bokan / Carlos Rapallo Fernández / Jelena Djekic Malbasa / Jessica Rochat / Teresa Velázquez Fernández

    Open Research Europe, Vol

    Findable, Accessible, Interoperable and Reusable data to foster Health Research [version 2; peer review: 1 approved, 2 approved with reservations]

    2022  Band 2

    Abstract: Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the ... ...

    Abstract Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators’ performance.
    Schlagwörter FAIR principles ; health research data management ; HL7 FHIR ; health data ; data sharing ; data reuse ; eng ; Science ; Q ; Social Sciences ; H
    Thema/Rubrik (Code) 306
    Sprache Englisch
    Erscheinungsdatum 2022-05-01T00:00:00Z
    Verlag F1000 Research Ltd
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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