Bayesian logistic regression approaches to predict incorrect DRG assignment

Suleiman, M, Demirhan, H, Boyd, L, Girosi, F and Aksakalli, V 2018, 'Bayesian logistic regression approaches to predict incorrect DRG assignment', Health Care Management Science, pp. 1-12.


Document type: Journal Article
Collection: Journal Articles

Title Bayesian logistic regression approaches to predict incorrect DRG assignment
Author(s) Suleiman, M
Demirhan, H
Boyd, L
Girosi, F
Aksakalli, V
Year 2018
Journal name Health Care Management Science
Start page 1
End page 12
Total pages 12
Publisher Springer
Abstract 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.
Subject Stochastic Analysis and Modelling
Applied Statistics
Statistics not elsewhere classified
Keyword(s) Bayesian Analysis
Clinical Coding
DRGs
Health Informatics
Statistical Modelling
DOI - identifier 10.1007/s10729-018-9444-8
Copyright notice © Springer Science+Business Media, LLC, part of Springer Nature 2018
ISSN 1386-9620
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