The Electronic Practice Based Research Network

Project Status

Chief Investigators
Prof. Siaw-Teng Liaw, Ms Jane Taggart, Dr Hairong Yu, Dr Douglas Boyle, Prof Mark F Harris, Prof Bin Jalaludin, , Professor Geoff Delaney, Dr Andrew Knight, Dr Michael Tam, Dr Blanca Gallego Luxan, A/Prof Vincent Wong

Associate Investigators
Ms Jenny Wright, Dr Sarah Dennis, Dr Isuru Ratnayake, Dr Joel Rhee


In the wake of growing health care needs of the ageing population, scarcity of resources and increasing costs of health care delivery in the management of chronic disease, many health systems have focused on promoting and monitoring safety, quality and cost-effectiveness – with the increasing recognition that up-to-date information and information technology is essential to achieve these goals. Routinely collected electronic health care data, aggregated into large databases, are increasingly being mined, linked and used for audit, continuous quality improvement in clinical care, health service planning, epidemiological study and evaluation research. 


The UNSW electronic Practice Based Research Network (ePBRN) is a network of computerised general practices, District Health Services and other public and private health care providers.

Two network neighbourhoods have been established in south west Sydney with one in Fairfield and the other in Wollondilly.

The UNSW ePBRN has established the tools and processes to collect/extract data from participating practices, aggregate, link and analyse the data to provide timely feedback to improve the quality of the data as well as to facilitate reflection by grassroots clinicians to generate grounded and data-driven clinical and population health questions. It is developing statistical and informatics-based methods, including ontologies and semantic web technologies to automate and sustainably manage “big data” extracted from multiple Health Information Systems to optimize the integrated and biopsychosocial care of patients with multiple chronic diseases, including cancer and mental health.


To share data extracted from health information systems to facilitate professional collegiality and coordination of health services, quality monitoring and research and development to improve health documentation, patient care and health outcomes in an integrated health neighbourhood.


The UNSW ePBRN team maintains a secure process to extract, link and analyse pseudonymised clinical data from health information systems, including electronic health records, to support the care of individuals and populations; audit and quality improvement activities; predictive modelling (e.g. of attendances at emergency departments or admissions); and health policy and planning through research and evaluation. Feedback on the quality of information and clinical and managerial indicators of care is provided to ePBRN members to enable reflection on the quality of information, care and outcomes.


  • Taggart J, Liaw S, Yu H. Structured data quality reports to improve EHR data quality. Int J  Med Inform 2015; 84:1084-98. 
  • Liaw S-T, Taggart J, Yu H, Lusignan Sd, Kuziemsky C, Hayen A: Integrating electronic health record information to support integrated care: Practical application of ontologies to improve the accuracy of diabetes disease registers. Journal of Biomedical Informatics 2014, 52:364-372.
  • Rahimi A, Liaw ST, Ray P, Taggart J, Yu H. Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review. Decision Analytics 2014, 1-5. doi:10.1186/2193-8636-1-5.
  • Rahimi A, Parameswaran N, Ray P,  Taggart J, Yu H, Liaw ST. Development of a Methodological Approach for Data Quality Ontology in Diabetes Management. International Journal of E-Health and Medical Communications (IJEHMC) 5(3) p77, 2014 
  • Rahimi A, Liaw ST, Ray P, Taggart J, Yu H Validating an ontology-based algorithm to identifypatients with Type 2 Diabetes Mellitus in ElectronicHealth Records. International Journal of Medical Informatics. 8 3 ( 2 0 1 4 ) 768–778, 2014. 
  • Liaw S, Taggart J, Lusignan S, Yu H. Data extraction from electronic health records ­ existing tools may be unreliable and potentially unsafe. Australian Family Physician Family Volume 42, No.11, November 2013 Pages 820-823 
  • Liaw S, Taggart J, De Lusignan S. From small practice-based data to big data – data extraction errors. In: Tannenbaum J, editor. AMIA Translational Bioinformatics and Clinical Research Informatics Summit 2013; San Francisco2013.
  • Liaw S, Rahimi A, Ray P, Taggart J, Dennis S, de Lusignan S, Jalaludin B, Yeo AET, Talaei-Khoei A. Towards an ontology for data quality in integrated chronic disease: a realist review of the literature. Int J Med Inform 2013; . 2012;82(1):10–24.
  • Taggart J, Liaw ST, Dennis S, Yu H, Rahimi A, Jalaludin B, Harris M. The University of NSW electronic Practice Based Research Network: Disease registers, data quality and utility. Stud Health Technol Inform 2012, 178, 219-27 [P001347093] 
  • Liaw ST, Taggart J, Dennis S, Yeo AET. Data quality and fitness for purpose of routinely collected data – a case study from an electronic Practice-Based Research Network (ePBRN). Proceedings of the American Medical Informatics Association Annual Symposium 2011; 785-794, Washington DC. [P001055926]
  • Siaw-Teng Liaw, Huei-Yang Chen, Della Maneze, Jane Taggart, Sarah Dennis, Sanjyot Vagholkar and Jeremy Bunker. The Quality of Routinely Collected Data:Using the “Principal Diagnosis” in Emergency Department Databases as an Example; electronic Journal of Health Informatics; Jan 2012; Vol 7(1):e1 [P000914468] 
  • Liaw ST, Chen HY, Maneze D, Taggart J, Dennis S, Vagholkar S, Bunker J. Health reform: is current electronic information fit for purpose? Emergency Medicine Australasia 2011 (Sep): (doi: 10.1111/j.1742-6723.2011.01486.x) [P001055927]
  • Yu H, Liaw S, Taggart J, Rahimi A, editors. Using Ontologies to Identify Patients with Diabetes in Electronic Health Records. International Semantic Web Computing; 2011; Sydney. Berlin Heidelberg: Springer-Verlag; 2011.