Despite increasing benchmarking and outlier detection activities within clinical quality registries (CQRs), there is little guidance available to support methodological decision-making and the implementation of optimal methods. Informed by simulation studies, this presentation will cover the key statistical considerations and recommended methods for benchmarking to accurately identify underperforming sites in CQRs.
Presenters
Prof Arul Earnest
Professor Earnest is the deputy head of the Clinical Outcomes data Reporting and Research Program and senior biostatistician (Biostatistics Unit) at the School of Public Health & Preventive Medicine at Monash University. He has authored a textbook, 5 book chapters, and over 265 publications in peer-reviewed international journals, His research focus is in the intersection of Bayesian spatio-temporal models and machine learning techniques for large clinical registries (leading a team of 4 data scientists & 7 PhD students), as well as leading the reporting of key benchmarked reports for various clinical quality registries housed with Monash University.
Dr Jessy Hansen
Dr Hansen is a Senior Scientist at the Institute of Medical Informatics, Statistics and Documentation at the Medical University of Graz, Austria. She recently completed a PhD in biostatistics at Monash University, during which she evaluated methods of benchmarking in clinical registries. She has previously worked as a data analyst in the Clinical Outcome Data Reporting and Research Program at the School of Public Health and Preventive Medicine at Monash University, providing statistical support to registries such as the Australian Pelvic Floor Procedure Registry and the Australia New Zealand Trauma Registry.