The Experts below are selected from a list of 1701 Experts worldwide ranked by ideXlab platform

Katherine Holmes - One of the best experts on this subject based on the ideXlab platform.

Siaw-teng Liaw - One of the best experts on this subject based on the ideXlab platform.

  • Health Information Governance in a Digital Environment - Clinical decision support systems: data quality management and Governance.
    Studies in health technology and informatics, 2020
    Co-Authors: Siaw-teng Liaw
    Abstract:

    This chapter examines data quality management (DQM) and Information Governance (IG) of electronic decision support (EDS) systems so that they are safe and fit for use by clinicians and patients and their carers. This is consistent with the ISO definition of data quality as being fit for purpose. The scope of DQM & IG should range from data creation and collection in clinical settings, through cleaning and, where obtained from multiple sources, linkage, storage, use by the EDS logic engine and algorithms, knowledge base and guidance provided, to curation and presentation. It must also include protocols and mechanisms to monitor the safety of EDS, which will feedback into DQM & IG activities. Ultimately, DQM & IG must be integrated across the data cycle to ensure that the EDS systems provide guidance that leads to safe and effective clinical decisions and care.

  • an integrated organisation wide data quality management and Information Governance framework theoretical underpinnings
    Journal of innovation in health informatics, 2014
    Co-Authors: Siaw-teng Liaw, C Pearce, Hemachandra Liyanage, Gladys Ss Cheahliaw, Simon De Lusignan
    Abstract:

    Introduction  Increasing investment in eHealth aims to improve cost effectiveness and safety of care. Data extraction and aggregation can create new data products to improve professional practice and provide feedback to improve the quality of source data. A previous systematic review concluded that locally relevant clinical indicators and use of clinical record systems could support clinical Governance. We aimed to extend and update the review with a theoretical framework. Methods  We searched PubMed, Medline, Web of Science, ABI Inform (Proquest) and Business Source Premier (EBSCO) using the terms curation, Information ecosystem, data quality management (DQM), data Governance, Information Governance (IG) and data stewardship. We focused on and analysed the scope of DQM and IG processes, theoretical frameworks, and determinants of the processing, quality assurance, presentation and sharing of data across the enterprise. Findings  There are good theoretical reasons for integrated Governance, but there is variable alignment of DQM, IG and health system objectives across the health enterprise. Ethical constraints exist that require health Information ecosystems to process data in ways that are aligned with improving health and system efficiency and ensuring patient safety. Despite an increasingly ‘big-data’ environment, DQM and IG in health services are still fragmented across the data production cycle. We extend current work on DQM and IG with a theoretical framework for integrated IG across the data cycle. Conclusions  The dimensions of this theory-based framework would require testing with qualitative and quantitative studies to examine the applicability and utility, along with an evaluation of its impact on data quality across the health enterprise.

  • An integrated organisation-wide data quality management and Information Governance framework: Theoretical underpinnings
    Informatics in Primary Care, 2014
    Co-Authors: Siaw-teng Liaw, C Pearce, G S S Liaw, Hemachandra Liyanage, Simon De Lusignan
    Abstract:

    Introduction Increasing investment in eHealth aims to improve cost effectiveness and safety of care. Data extraction and aggregation can create new data products to improve professional practice and provide feedback to improve the quality of source data. A previous systematic review concluded that locally relevant clinical indicators and use of clinical record systems could support clinical Governance. We aimed to extend and update the review with a theoretical framework. Methods We searched PubMed, Medline, Web of Science, ABI Inform (Proquest) and Business Source Premier (EBSCO) using the terms curation, Information ecosystem, data quality management (DQM), data Governance, Information Governance (IG) and data stewardship. We focused on and analysed the scope of DQM and IG processes, theoretical frameworks, and determinants of the processing, quality assurance, presentation and sharing of data across the enterprise. Findings There are good theoretical reasons for integrated Governance, but there is variable alignment of DQM, IG and health system objectives across the health enterprise. Ethical constraints exist that require health Information ecosystems to process data in ways that are aligned with improving health and system efficiencyand ensuring patient safety. Despite an increasingly 'big-data' environment, DQM and IG in health services are still fragmented across the data production cycle. We extend current work on DQM and IG with a theoretical framework for integrated IG across the data cycle. Conclusions The dimensions of this theory-based framework would require testing with qualitative and quantitative studies to examine the applicability and utility, along with an evaluation of its impact on data quality across the health enterprise. Copyright © 2014 The Author(s).

Simon De Lusignan - One of the best experts on this subject based on the ideXlab platform.

  • an integrated organisation wide data quality management and Information Governance framework theoretical underpinnings
    Journal of innovation in health informatics, 2014
    Co-Authors: Siaw-teng Liaw, C Pearce, Hemachandra Liyanage, Gladys Ss Cheahliaw, Simon De Lusignan
    Abstract:

    Introduction  Increasing investment in eHealth aims to improve cost effectiveness and safety of care. Data extraction and aggregation can create new data products to improve professional practice and provide feedback to improve the quality of source data. A previous systematic review concluded that locally relevant clinical indicators and use of clinical record systems could support clinical Governance. We aimed to extend and update the review with a theoretical framework. Methods  We searched PubMed, Medline, Web of Science, ABI Inform (Proquest) and Business Source Premier (EBSCO) using the terms curation, Information ecosystem, data quality management (DQM), data Governance, Information Governance (IG) and data stewardship. We focused on and analysed the scope of DQM and IG processes, theoretical frameworks, and determinants of the processing, quality assurance, presentation and sharing of data across the enterprise. Findings  There are good theoretical reasons for integrated Governance, but there is variable alignment of DQM, IG and health system objectives across the health enterprise. Ethical constraints exist that require health Information ecosystems to process data in ways that are aligned with improving health and system efficiency and ensuring patient safety. Despite an increasingly ‘big-data’ environment, DQM and IG in health services are still fragmented across the data production cycle. We extend current work on DQM and IG with a theoretical framework for integrated IG across the data cycle. Conclusions  The dimensions of this theory-based framework would require testing with qualitative and quantitative studies to examine the applicability and utility, along with an evaluation of its impact on data quality across the health enterprise.

  • An integrated organisation-wide data quality management and Information Governance framework: Theoretical underpinnings
    Informatics in Primary Care, 2014
    Co-Authors: Siaw-teng Liaw, C Pearce, G S S Liaw, Hemachandra Liyanage, Simon De Lusignan
    Abstract:

    Introduction Increasing investment in eHealth aims to improve cost effectiveness and safety of care. Data extraction and aggregation can create new data products to improve professional practice and provide feedback to improve the quality of source data. A previous systematic review concluded that locally relevant clinical indicators and use of clinical record systems could support clinical Governance. We aimed to extend and update the review with a theoretical framework. Methods We searched PubMed, Medline, Web of Science, ABI Inform (Proquest) and Business Source Premier (EBSCO) using the terms curation, Information ecosystem, data quality management (DQM), data Governance, Information Governance (IG) and data stewardship. We focused on and analysed the scope of DQM and IG processes, theoretical frameworks, and determinants of the processing, quality assurance, presentation and sharing of data across the enterprise. Findings There are good theoretical reasons for integrated Governance, but there is variable alignment of DQM, IG and health system objectives across the health enterprise. Ethical constraints exist that require health Information ecosystems to process data in ways that are aligned with improving health and system efficiencyand ensuring patient safety. Despite an increasingly 'big-data' environment, DQM and IG in health services are still fragmented across the data production cycle. We extend current work on DQM and IG with a theoretical framework for integrated IG across the data cycle. Conclusions The dimensions of this theory-based framework would require testing with qualitative and quantitative studies to examine the applicability and utility, along with an evaluation of its impact on data quality across the health enterprise. Copyright © 2014 The Author(s).

Márcio Alexandre De Macedo Rodrigues - One of the best experts on this subject based on the ideXlab platform.

  • CSE - Information Governance, Big Data and Data Quality
    2013 IEEE 16th International Conference on Computational Science and Engineering, 2013
    Co-Authors: Patrícia Alves De Freitas, Everson Andrade Dos Reis, Wanderson Senra Michel, Mauro Edson Gronovicz, Márcio Alexandre De Macedo Rodrigues
    Abstract:

    The value of Information as a competitive differential has been taken into consideration in companies all over the world for some time already. In recent years, there has been heated debate about some terms originated from new concepts related to Information, such as big data, due to the promise that such topic might revolutionise world trade. Hence, data and Information Governance and quality have been increasingly discussed in the business world.

  • Information Governance, Big Data and Data Quality
    2013 IEEE 16th International Conference on Computational Science and Engineering, 2013
    Co-Authors: Patrícia Alves De Freitas, Everson Andrade Dos Reis, Wanderson Senra Michel, Mauro Edson Gronovicz, Márcio Alexandre De Macedo Rodrigues
    Abstract:

    The value of Information as a competitive differential has been taken into consideration in companies all over the world for some time already. In recent years, there has been heated debate about some terms originated from new concepts related to Information, such as big data, due to the promise that such topic might revolutionise world trade. Hence, data and Information Governance and quality have been increasingly discussed in the business world.

Janis L. Huston - One of the best experts on this subject based on the ideXlab platform.

  • Information Governance standards for managing e-health Information:
    Journal of Telemedicine and Telecare, 2020
    Co-Authors: Janis L. Huston
    Abstract:

    Integrity of patient Information, from both a quality and a security perspective, is critical to patient care. In the UK, the Information Governance initiative of the National Health Service (NHS) provides a framework to monitor and control the management of confidential patient data. Information Governance standards grew out of the Data Accreditation Programme, first proposed in the 1998 NHS document Information for Health. The Data Accreditation Programme was based on a three-stage assessment of data quality in acute hospitals. Stage one required internal review of policy and procedures for data input into computerized patient administration systems. Stage two involved an external audit to verify compliance with the standards. Stage three mandated audits of data outputs, focusing on clinical coding quality. Before stage three of the programme was fully implemented, the standards were incorporated into the Information Governance initiative, in which standards were expanded to include primary care and oth...

  • Information Governance standards for managing e-health Information.
    Journal of telemedicine and telecare, 2020
    Co-Authors: Janis L. Huston
    Abstract:

    Integrity of patient Information, from both a quality and a security perspective, is critical to patient care. In the UK, the Information Governance initiative of the National Health Service (NHS) provides a framework to monitor and control the management of confidential patient data. Information Governance standards grew out of the Data Accreditation Programme, first proposed in the 1998 NHS document Information for Health. The Data Accreditation Programme was based on a three-stage assessment of data quality in acute hospitals. Stage one required internal review of policy and procedures for data input into computerized patient administration systems. Stage two involved an external audit to verify compliance with the standards. Stage three mandated audits of data outputs, focusing on clinical coding quality. Before stage three of the programme was fully implemented, the standards were incorporated into the Information Governance initiative, in which standards were expanded to include primary care and other health-care settings. These standards address many Information management issues, including security and data quality, which are key concerns in telemedicine and e-health applications. Compliance is essential for the successful implementation of the NHS Care Records Service, which will allow sharing of electronically stored patient Information across the UK.