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  1. Article ; Online: Young man with chest pain and an abnormal echocardiogram.

    Peters, Matthew / Kalra, Dinesh

    Heart (British Cardiac Society)

    2024  Volume 110, Issue 9, Page(s) 656–684

    MeSH term(s) Male ; Humans ; Chest Pain/diagnosis ; Chest Pain/etiology ; Coronary Artery Disease ; Coronary Angiography ; Multidetector Computed Tomography
    Language English
    Publishing date 2024-04-15
    Publishing country England
    Document type Journal Article
    ZDB-ID 1303417-0
    ISSN 1468-201X ; 1355-6037
    ISSN (online) 1468-201X
    ISSN 1355-6037
    DOI 10.1136/heartjnl-2024-323886
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  2. Article: Editorial: Managing healthcare transformation towards P5 medicine.

    Blobel, Bernd / Kalra, Dipak

    Frontiers in medicine

    2023  Volume 10, Page(s) 1244100

    Language English
    Publishing date 2023-08-25
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2775999-4
    ISSN 2296-858X
    ISSN 2296-858X
    DOI 10.3389/fmed.2023.1244100
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  3. Article ; Online: Clinical Research Informatics: Contributions from 2022.

    Tannier, Xavier / Kalra, Dipak

    Yearbook of medical informatics

    2023  Volume 32, Issue 1, Page(s) 146–151

    Abstract: Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.: Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) ... ...

    Abstract Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.
    Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.
    Results: Among the 1,324 papers returned by the search, published in 2022, that were in the scope of the various areas of CRI, the full review process selected four best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define a process and to gain multi-decision-maker acceptance of broad consent for the reuse of health data for research whilst remaining compliant with the European General Data Protection Regulation. The authors of the second-best paper present a federated architecture for the conduct of clinical trial feasibility queries that utilizes HL7 Fast Healthcare Interoperability Resources and an HL7 standard query representation. The third best paper aligns with the overall theme of this Yearbook, the inclusivity of potential participants in clinical trials, with recommendations to ensure greater equity. The fourth proposes a multi-modal modelling approach for large scale phenotyping from electronic health record information. This year's survey paper has also examined equity, along with data bias, and found that the relevant publications in 2022 have focused almost exclusively on the issue of bias in Artificial Intelligence (AI).
    Conclusions: The literature relevant to CRI in 2022 has largely been dominated by publications that seek to maximise the reusability of wide scale and representative electronic health record information for research, either as big data for distributed analysis or as a source of information from which to identify suitable patients accurately and equitably for invitation to participate in clinical trials.
    MeSH term(s) Humans ; Artificial Intelligence ; Medical Informatics ; Electronic Health Records ; Big Data ; Peer Review
    Language English
    Publishing date 2023-12-26
    Publishing country Germany
    Document type Randomized Controlled Trial ; Journal Article
    ZDB-ID 2251229-9
    ISSN 2364-0502 ; 2364-0502
    ISSN (online) 2364-0502
    ISSN 2364-0502
    DOI 10.1055/s-0043-1768748
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Raising the Impact of Real World Evidence.

    Kalra, Dipak

    Studies in health technology and informatics

    2019  Volume 258, Page(s) 1

    Abstract: In an environment in which most regulatory and authoritative health strategy decisions are made on the basis of randomised control trials, real-world evidence (RWE), primarily derived from electronic health records, remains a second-class citizen. Real ... ...

    Abstract In an environment in which most regulatory and authoritative health strategy decisions are made on the basis of randomised control trials, real-world evidence (RWE), primarily derived from electronic health records, remains a second-class citizen. Real world evidence is widely taken to include Pragmatic clinical trials and insights derived from the distributed analysis of large volumes of routinely collected health data and registry data (so called big data). This presentation will look at the growing scale and reputation of big health data, the ways in which good governance principles and better quality data are creating reusable data at scale, how platforms and tools are enabling better quality evidence generation, and the perspectives of different stakeholders towards the positioning of RWE in decision making: by regulators, health technology assessment agencies, outcomes benchmarking and value based care. This talk will review how we are presently able to generate trustworthy real world evidence, what we mean by that, and the barriers that remain to trusting it. These remaining barriers will need to be tackled by future health informatics research.
    MeSH term(s) Benchmarking ; Decision Making ; Electronic Health Records ; Evidence-Based Medicine ; Medical Informatics ; Technology Assessment, Biomedical
    Language English
    Publishing date 2019-04-02
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
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  5. Article: The importance of real-world data to precision medicine.

    Kalra, Dipak

    Personalized medicine

    2019  Volume 16, Issue 2, Page(s) 79–82

    MeSH term(s) Big Data ; Data Analysis ; Humans ; Precision Medicine/methods
    Language English
    Publishing date 2019-02-06
    Publishing country England
    Document type Editorial
    ZDB-ID 2299146-3
    ISSN 1744-828X ; 1741-0541
    ISSN (online) 1744-828X
    ISSN 1741-0541
    DOI 10.2217/pme-2018-0120
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6549: Show issues Location:
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    Zs.MG 73: Show issues

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  6. Article: Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews.

    Declerck, Jens / Kalra, Dipak / Vander Stichele, Robert / Coorevits, Pascal

    JMIR medical informatics

    2024  Volume 12, Page(s) e51560

    Abstract: Background: Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of ... ...

    Abstract Background: Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data.
    Objective: This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework.
    Methods: A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English.
    Results: A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions.
    Conclusions: The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.
    Language English
    Publishing date 2024-03-06
    Publishing country Canada
    Document type Journal Article ; Review
    ZDB-ID 2798261-0
    ISSN 2291-9694
    ISSN 2291-9694
    DOI 10.2196/51560
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Digital Analysis of Clinical Screening Criteria for a Rare Disease - Behcet's Disease.

    Tapuria, Archana / Kalra, Dipak / Curcin, Vasa

    Studies in health technology and informatics

    2023  Volume 305, Page(s) 444–447

    Abstract: The objective is to identify clinical screening criteria for a rare disease,- Behcet's disease and to analyse the digitally structured and unstructured components of the Identified Clinical criteria, build a clinical archetype using OpenEHR editor to be ... ...

    Abstract The objective is to identify clinical screening criteria for a rare disease,- Behcet's disease and to analyse the digitally structured and unstructured components of the Identified Clinical criteria, build a clinical archetype using OpenEHR editor to be used by learning health support systems for clinical screening of the disease. Methods/Search Strategy: Literature search was conducted, 230 papers were screened, and finally 5 papers were retained, analysed and summarised. Digital Analysis of the clinical criteria was done and a sandardised clinical knowledge model of the same was built using OpenEHR editor, underpinned by OpenEHR international standards. Results The structured and unstructured components of the criteria analysed to be able to incorporate them in a learning health system to screen patients for Behcet's disease. SNOMED CT and Read codes were assigned to the structured componenets. Possible misdiagnosis were identified, along with their corresponding clinical terminology codes that can be incorporated in the Electronic Health Record systems. Conclusion: The identified clinical screening was digitally analysed which can be embedded into a clinical decision support system that can be plugged onto the primary care systems to give an alert to the clinicians if a patient needs to be screened for a rare disease, for e.g., Behcet's.
    MeSH term(s) Humans ; Behcet Syndrome/diagnosis ; Rare Diseases/diagnosis ; Decision Support Systems, Clinical ; Knowledge ; Learning Health System
    Language English
    Publishing date 2023-06-19
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI230527
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Clinical Research Informatics: Contributions from 2022

    Tannier, Xavier / Kalra, Dipak

    Yearbook of Medical Informatics

    2023  Volume 32, Issue 01, Page(s) 146–151

    Abstract: Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.: Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) ... ...

    Abstract Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.
    Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.
    Results: Among the 1,324 papers returned by the search, published in 2022, that were in the scope of the various areas of CRI, the full review process selected four best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define a process and to gain multi-decision-maker acceptance of broad consent for the reuse of health data for research whilst remaining compliant with the European General Data Protection Regulation. The authors of the second-best paper present a federated architecture for the conduct of clinical trial feasibility queries that utilizes HL7 Fast Healthcare Interoperability Resources and an HL7 standard query representation. The third best paper aligns with the overall theme of this Yearbook, the inclusivity of potential participants in clinical trials, with recommendations to ensure greater equity. The fourth proposes a multi-modal modelling approach for large scale phenotyping from electronic health record information. This year's survey paper has also examined equity, along with data bias, and found that the relevant publications in 2022 have focused almost exclusively on the issue of bias in Artificial Intelligence (AI).
    Conclusions: The literature relevant to CRI in 2022 has largely been dominated by publications that seek to maximise the reusability of wide scale and representative electronic health record information for research, either as big data for distributed analysis or as a source of information from which to identify suitable patients accurately and equitably for invitation to participate in clinical trials.
    Keywords Observational studies as Topic ; real-world data ; real-world evidence generation ; consent ; phenotyping
    Language English
    Publishing date 2023-08-01
    Publisher Georg Thieme Verlag KG
    Publishing place Stuttgart ; New York
    Document type Article
    ZDB-ID 2251229-9
    ISSN 2364-0502 ; 0943-4747 ; 2364-0502
    ISSN (online) 2364-0502
    ISSN 0943-4747 ; 2364-0502
    DOI 10.1055/s-0043-1768748
    Database Thieme publisher's database

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  9. Article ; Online: Atherosclerotic cardiovascular disease risk prediction: current state-of-the-art.

    Rout, Amit / Duhan, Sanchit / Umer, Muhammad / Li, Miranda / Kalra, Dinesh

    Heart (British Cardiac Society)

    2023  

    Language English
    Publishing date 2023-11-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 1303417-0
    ISSN 1468-201X ; 1355-6037
    ISSN (online) 1468-201X
    ISSN 1355-6037
    DOI 10.1136/heartjnl-2023-322928
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    Zs.MO 112: Show issues

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  10. Article ; Online: Clinical Research Informatics.

    Daniel, Christel / Tannier, Xavier / Kalra, Dipak

    Yearbook of medical informatics

    2022  Volume 31, Issue 1, Page(s) 161–164

    Abstract: Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021.: Method: Using PubMed, we did a bibliographic search using a combination of MeSH ... ...

    Abstract Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021.
    Method: Using PubMed, we did a bibliographic search using a combination of MeSH descriptors and free-text terms on CRI, followed by a double-blind review in order to select a list of candidate best papers to be peer-reviewed by external reviewers. After peer-review ranking, three section editors met for a consensus meeting and the editorial team was organized to finally conclude on the selected three best papers.
    Results: Among the 1,096 papers (published in 2021) returned by the search and in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes an operational and scalable framework for generating EHR datasets based on a detailed clinical model with an application in the domain of the COVID-19 pandemics. The authors of the second best paper present a secure and scalable platform for the preprocessing of biomedical data for deep data-driven health management applied for the detection of pre-symptomatic COVID-19 cases and for biological characterization of insulin-resistance heterogeneity. The third best paper provides a contribution to the integration of care and research activities with the REDCap Clinical Data and Interoperability sServices (CDIS) module improving the accuracy and efficiency of data collection.
    Conclusions: The COVID-19 pandemic is still significantly stimulating research efforts in the CRI field to improve the process deeply and widely for conducting real-world studies as well as for optimizing clinical trials, the duration and cost of which are constantly increasing. The current health crisis highlights the need for healthcare institutions to continue the development and deployment of Big Data spaces, to strengthen their expertise in data science and to implement efficient data quality evaluation and improvement programs.
    MeSH term(s) Humans ; Big Data ; COVID-19 ; Data Collection ; Medical Informatics ; Pandemics ; Biomedical Research
    Language English
    Publishing date 2022-12-04
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 2251229-9
    ISSN 2364-0502 ; 2364-0502
    ISSN (online) 2364-0502
    ISSN 2364-0502
    DOI 10.1055/s-0042-1742530
    Database MEDical Literature Analysis and Retrieval System OnLINE

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