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Professor Jonathan Karnon
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School of Public Health, University of Adelaide (September 2007-
Health Economics and Decision Analysis, School of Health and Related Research, University of Sheffield (2002-2007)
Health and Safety Laboratory, Sheffield (2001-2002)
Health Economics Research Group, Brunel University (1995-2001)
QualificationsBA(Hons) Economic and Social History (University of Leeds)
MSc Health Economics (University of York)
I co-ordinate the postgraduate Health Economics program: Graduate Certificate and Graduate Diploma in Health Economics, and Master of Health Economics and Health Policy.
I co-ordinate two postgraduate courses: Health Economic Evaluation and Decision Making (PUB_HLTH_7082), and Applied Health Economic Evaluation (PUB_HLTH_7085)
See the following links for details about the Health Economic Evaluation and Decision Making course as an online extended short course:
I also teach on two other postgraduate courses: Health Technology Assessment (PUB_HLTH_7147HO), and Health Economics (PUB_HLTH_7081).
I have two broad areas of research interest:
1. Service evaluation and quality improvement
Using linked, de-identified health service data to investigate variation in clinical practice, idenitfy benchmark performance, and inform frameworks for quality improvement in clinical practice.
I have access to large, linked observational datasets covering primary and secondary care settings, which have been used to compare service providers (e.g. hospitals). Prospective PhD students are invited to contact me regarding research involving these datasets. The following is a research topic of particular interest to me:
Comparing hospital performance with respect to costs, outcomes, and processes of care: administrative vs. clinical data
Administrative hospital data is collated by hospitals for reporting to a centralized body (e.g. the National Hospital Cost Data Collection in Australia). The advantages of such data for informing hospital performance include that they are routinely collected in a standardized manner by all hospitals. Disadvantages include the potentially limited nature of the data collected, and the lag between data collection and availability of the data for analysis.
Clinical data are recorded during the process of managing patients, and includes details of care provided, such as the timing and results of diagnostic tests and investigations, and interventions. The advantages of clinical data include the additional detail on the clinical pathway, and the potential for quicker access to the data. The main disadvantage is the non-routine collection and reporting of clinical data, which means that primary data extraction has to be undertaken. However, with increasing use of electronic data systems, the extraction of large amounts of clinical data is more feasible than previously, when such data were manual extracted from paper-based clinical records.
In South Australia, the Clinical Reporting Repository provides centralized and real-time access to clinical data from six metropolitan public hospitals, as well as being linked to population-based mortality data.
We have compiled a dataset of de-identified administrative and clinical data for over 15,000 patients who presented at the emergency department of four hospitals with chest pain. The data is linked to inform costs and outcomes over a 12-month follow-up period. The purpose of compiling this dataset was to identify important variation in costs, outcomes, and processes of care across hospitals to inform and incentivize quality improvement activity at non-benchmark hospitals.
The base case analyses of variation have been undertaken, which showed significant differences in performance across hospitals. Important additional analyses remain, in particular, the relative value of the administrative and clinical data. A key issue in the analysis of hospital performance is the timeliness of the analysis. Given the lag in accessing administrative data, it would be useful to demonstrate the validity, feasibility, and timeliness of analyzing hospital performance using clinical data alone.
Potential topics for this PhD thesis include:
The selected candidate will be supported in applying for a University PhD scholarship, and an additional financial top-up to the scholarship will also be provided.
Karnon et al, Applying risk adjusted cost-effectiveness (RAC-E) analysis to hospitals: estimating the costs and consequences of variation in clinical practice, Health Economics.http://onlinelibrary.wiley.com/doi/10.1002/hec.2828/abstract
Pham C et al, Evaluating the effects of variation in clinical practice: a risk adjusted cost-effectiveness (RAC-E) analysis of acute stroke services, BMC Health Services Research.http://www.biomedcentral.com/1472-6963/12/266/abstract
2. Complex decision analytic modelling and model calibration/validation
I am interested in the costs and benefits of using more complex model structures to represent disease pathways in the context of cost-effectiveness modelling. A potential PhD topic would be an investigation of the model development process, including a comparison of the implementation, population, and validation of two or more divergent model structures.
Karnon et al, When to use discrete event simulation (DES) for the economic evaluation of health technologies? A review and critique of the costs and benefits of DES. Pharmacoeconomics 2014; 32(6): 547-58
Afzali et al, Model Performance Evaluation (Validation and Calibration) in Model-based Studies of Therapeutic Interventions for Cardiovascular Diseases, Applied Health Economics and Policy 2013; 11(2):85-93
Karnon et al, A Hybrid Cohort Individual Sampling Natural History Model of Age-Related Macular Degeneration: Assessing the Cost-Effectiveness of Screening Using Probabilistic Calibration, Medical Decision Making 2009; 29(3):304-16
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