Bayesian logistic regression approaches to predict incorrect DRG assignment
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Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode’s probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.
KeywordsDRGs Bayesian Analysis Health Informatics Clinical Coding Statistical Modelling
This research is funded by the Capital Markets Cooperative Research Centre (CMCRC) Limited. We would like to express our gratitude to the anonymous reviewers whose suggestions improved the quality and clarity of the manuscript.
Compliance with ethical standards
Conflicts of interest
No potential conflicts of interest exist for all authors.
- 4.Duckett S (2015) The Australian health care system. Oxford University Press. In: fifth editionGoogle Scholar
- 7.Hanson T, Branscum A, Johnson W (2014) Informative g-Priors for Logistic Regression. Bayesian Analysis, 9(3):597âA˘ S¸:612Google Scholar
- 9.Independent Hospital Pricing Authority. AR-DRG classification system. https://www.ihpa.gov.au/what-we-do/ar-drg-classification-system, 2017. [Accessed 2-November-2017]
- 10.H (1946) Jeffreys. An Invariant Form for the Prior Probability in Estimation Problems. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 186(1007):453–461Google Scholar
- 11.Z. Kalaylioglu and H. Demirhan. A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements. Statistical Methods in Medical Research, 0(0):1–15, 2015Google Scholar
- 14.Lally N (2015) The Informative g-Prior vs. University of Connecticut, Common Reference Priors for Binomial Regression With an Application to Hurricane Electrical Utility Asset Damage Prediction. Master’s thesisGoogle Scholar
- 20.Zellner A (1983) Applications of Bayesian Analysis in Econometrics. Journal of the Royal Statistical Society. Series D (The Statistician) 32(1/2):23–34Google Scholar