An integrated organisation-wide data quality management and information governance framework

Theoretical underpinnings

Siaw-Teng Liaw, Christopher Pearce, Harshana Liyanage, Gladys SS Liaw, Simon De Lusignan

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)199-206
Number of pages8
JournalInformatics in Primary Care
Volume21
Issue number4
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • Big data
  • Clinical
  • Data quality
  • Electronic health records
  • Governance
  • Information
  • Integration
  • Organisation

Cite this

Liaw, Siaw-Teng ; Pearce, Christopher ; Liyanage, Harshana ; Liaw, Gladys SS ; De Lusignan, Simon. / An integrated organisation-wide data quality management and information governance framework : Theoretical underpinnings. In: Informatics in Primary Care. 2014 ; Vol. 21, No. 4. pp. 199-206.
@article{c82ee996b69146bdb17803c259a7272e,
title = "An integrated organisation-wide data quality management and information governance framework: Theoretical underpinnings",
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.",
keywords = "Big data, Clinical, Data quality, Electronic health records, Governance, Information, Integration, Organisation",
author = "Siaw-Teng Liaw and Christopher Pearce and Harshana Liyanage and Liaw, {Gladys SS} and {De Lusignan}, Simon",
year = "2014",
doi = "10.14236/jhi.v21i4.87",
language = "English",
volume = "21",
pages = "199--206",
journal = "Informatics in Primary Care",
issn = "1476-0320",
publisher = "BCS, The Chartered Institute for IT",
number = "4",

}

An integrated organisation-wide data quality management and information governance framework : Theoretical underpinnings. / Liaw, Siaw-Teng; Pearce, Christopher; Liyanage, Harshana; Liaw, Gladys SS; De Lusignan, Simon.

In: Informatics in Primary Care, Vol. 21, No. 4, 2014, p. 199-206.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - An integrated organisation-wide data quality management and information governance framework

T2 - Theoretical underpinnings

AU - Liaw, Siaw-Teng

AU - Pearce, Christopher

AU - Liyanage, Harshana

AU - Liaw, Gladys SS

AU - De Lusignan, Simon

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Big data

KW - Clinical

KW - Data quality

KW - Electronic health records

KW - Governance

KW - Information

KW - Integration

KW - Organisation

UR - http://www.scopus.com/inward/record.url?scp=84910152395&partnerID=8YFLogxK

U2 - 10.14236/jhi.v21i4.87

DO - 10.14236/jhi.v21i4.87

M3 - Article

VL - 21

SP - 199

EP - 206

JO - Informatics in Primary Care

JF - Informatics in Primary Care

SN - 1476-0320

IS - 4

ER -