ePBRN 2019 GP Dataset

The University of New South Wales (UNSW) electronic Practice Based Research Network (ePBRN) was established in 2010 in the Fairfield local government area of South Western Sydney (SWS) to conduct clinical and health services research by extracting and linking routinely collected data from 18 participating General Practices (GPs) and local health services, including the five hospitals in the Fairfield and Wollondilly integrated health neighborhoods.  The SWS region has a diverse cultural and linguistic (CALD) population with a low socioeconomic profile. The ePBRN is a representative sample oaf SWS patients who use health services. 

The ePBRN 2019 GP dataset is a comprehensive compilation of electronic health records (EHR) originating from general practice sites located within the Fairfield and Wollondilly regions of the southwest Sydney local health district. The data is extracted from two EHR systems, namely Best Practice (BP) and Medical Director (MD), utilizing the GRHANITE tool from the University of Melbourne. The dataset is divided into three distinct modules, each representing patient data from the year 2000 to 2019. The dataset offers a longitudinal perspective of health service utilisation, trends, and patient profiles. In total, it encompasses a sizable cohort of 249,345 patients across 11 general practitioner sites. This extensive and temporally diverse dataset serves as a valuable resource for researchers and healthcare professionals, enabling in-depth analyses and insights into healthcare practices, patient demographics, and clinical outcomes within the Fairfield and Wollondilly areas of New South Wales.  

Dataset access fees

• ePBRN 2019 data access fee - A$12,500.00 

• SREDH Platform (data storage and linkage) license for 1 year - A$1,900.00 

• ePBRN linkage and data preparation costs - A$5,000

• Payment via bank transfer to UNSW Sydney

Dataset access instructions

• Fill out this form to get access to the dataset.

• Once the request is approved, please sign and return the SREDH Consortium membership, data usage and project description forms that will be sent up on filling out the data request form above. 

• Pay data access and associated fees, if applicable 

• Download the dataset from the SREDH secure server. 

• Submit progress report every 6 months until the completion of the project

Access Criteria

• Available to researchers( academic and non-academic) for non-commercial purposes.

• Researchers need to have experience handling sensitive patient data and training in ethics.

• Researchers are required to report biannually to the SREDH Consortium on any research outputs that arise.

• Any output that arises from this dataset needs to be reviewed by the data custodian (SREDH Consortium) before submission.
  
  

Frequently Asked Questions

• Please refer to FAQs page.

Selected publications

• Liaw S-T, Taggart J, Yu H, de Lusignan S, 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. J BiomedInform. 2014;52:364–72. http://dx.doi.org/10.1016/j.jbi.2014.07.016

• Siaw-Teng Liaw, Jane Taggart, Sarah Dennis, Anthony Yeo. Data quality and fitness for purpose of routinely collected data – a general practice case study from an electronic Practice-Based Research Network (ePBRN).". AMIA AnnualSymposium Proc. 2011:785–94. https://pubmed.ncbi.nlm.nih.gov/22195136

• South WesternSydney Primary Health Network (PHN). South West Sydney: Our Health in 2019: An in-Depth Study of the Health of the Population now and into the Future; SouthWestern Sydney Primary Health Network (PHN): Sydney, Australia, 2019. 

• Dennis, S., Taggart, J., Yu, H., Jalaludin, B., Harris, M. F., &Liaw, S. T. (2019). Linking observational data from general practice, hospital admissions and diabetes clinic databases: can it be used to predict hospital admission?. BMC health services research, 19(1), 1-11. http://dx.doi.org/10.1186/s12913-019-4337-1

• Liyanage, H.,Liaw, S. T., Jonnagaddala, J., Hinton, W., & de LUSIGNAN, S. (2018). Common Data Models (CDMs) to Enhance International Big Data Analytics: A Diabetes UseCase to Compare Three CDMs. EFMI-STC, 60-64. https://doi.org/10.3233/978-1-61499-921-8-60

• Garg, P.,Eastwood, J., Liaw, S. T., Jalaludin, B., & Grace, R. (2018). A case study of well child care visits at general practices in a region of disadvantage inSydney. Plos one, 13(10), e0205235. http://dx.doi.org/10.1371/journal.pone.0205235

• Kadappu, P., Jonnagaddala, J., Liaw, S. T., Cochran, B. J., Rye, K. A., & Ong, K. L. (2022). Statin Prescription Patterns and Associations with Subclinical Inflammation. Medicina (Kaunas, Lithuania), 58(8), 1096. https://doi.org/10.3390/medicina58081096 

• Vo, K., Jonnagaddala, J., & Liaw, T. (2019). Statistical supervised meta-ensemble algorithm for medical record linkage. Journal of Biomedical Informatics, 95, 103220. https://doi.org/10.1016/j.jbi.2019.103220 

• Guo, G. N., Jonnagaddala, J., Farshid, S., Huser, V., Reich, C., & Liaw, S. T. (2019). Comparison of the cohort selection performance of Australian Medicines Terminology to Anatomical Therapeutic Chemical mappings. Journal of the American Medical Informatics Association : JAMIA, 26(11), 1237–1246. https://doi.org/10.1093/jamia/ocz143 

•  Lu, Y., Van Zandt, M., Liu, Y., Li, J., Wang, X., Chen, Y., Chen, Z., Cho, J., Dorajoo, S. R., Feng, M., Hsu, M. H., Hsu, J. C., Iqbal, U., Jonnagaddala, J., Li, Y. C., Liaw, S. T., Lim, H. S., Ngiam, K. Y., Nguyen, P. A., Park, R. W., … Xu, H. (2022). Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort. JAMA network open, 5(3), e223877. https://doi.org/10.1001/jamanetworkopen.2022.3877 

• Williams, R. D., Markus, A. F., Yang, C., Duarte-Salles, T., DuVall, S. L., Falconer, T., Jonnagaddala, J., Kim, C., Rho, Y., Williams, A. E., Machado, A. A., An, M. H., Aragón, M., Areia, C., Burn, E., Choi, Y. H., Drakos, I., Abrahão, M. T. F., Fernández-Bertolín, S., Hripcsak, G., … Rijnbeek, P. R. (2022). Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network. BMC medical research methodology, 22(1), 35. https://doi.org/10.1186/s12874-022-01505-z 

• Reps, J. M., Kim, C., Williams, R. D., Markus, A. F., Yang, C., Duarte-Salles, T., Falconer, T., Jonnagaddala, J., Williams, A., Fernández-Bertolín, S., DuVall, S. L., Kostka, K., Rao, G., Shoaibi, A., Ostropolets, A., Spotnitz, M. E., Zhang, L., Casajust, P., Steyerberg, E. W., Nyberg, F., … Rijnbeek, P. R. (2021). Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR medical informatics, 9(4), e21547. https://doi.org/10.2196/21547