Siaw-Teng Liaw1, Christopher Pearce2, Harshana Liyanage3, Gladys S S Liaw4, Simon de Lusignan3. 1. School of Public Health & Community Medicine, UNSW Medicine and General Practice Unit, South Western Sydney Local Health District, Australia; Academic General Practice Unit, Fairfield Hospital, Cnr of Polding Street and Prairievale Road, Prairiewood, NSW 2176, Australia. siaw@unsw.edu.au. 2. Inner Eastern Medicare Local, Melbourne, Australia. 3. Department of Primary Care & Clinical Informatics, University of Surrey, Guildford, UK. 4. ZxCel Consulting, Melbourne, Australia.
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.
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.
Authors: Christopher M Pearce; Adam McLeod; Jon Patrick; Douglas Boyle; Marianne Shearer; Paula Eustace; Mary Catherine Pearce Journal: JMIR Res Protoc Date: 2016-12-20
Authors: William Hinton; Andrew McGovern; Rachel Coyle; Thang S Han; Pankaj Sharma; Ana Correa; Filipa Ferreira; Simon de Lusignan Journal: BMJ Open Date: 2018-08-20 Impact factor: 2.692
Authors: Siaw-Teng Liaw; Harshana Liyanage; Craig Kuziemsky; Amanda L Terry; Richard Schreiber; Jitendra Jonnagaddala; Simon de Lusignan Journal: Yearb Med Inform Date: 2020-04-17