Performance differences between the episode-based DBC and diagnosis-related DRG case mix systems

Abstract

This paper explores the question how much detail a cost system needs to have in order to provide reliable cost information at a reasonable price. In general, fine-grained cost systems with a lot of detail (in product definition, in cost drivers and in cost pools) are expected to provide more reliable cost information than coarse- grained cost systems with less detail. This paper takes as an example the DBC cost system that has been developed for the Dutch hospital sector. The fine-grained DBC system with over 40,000 health care products appears to outperform lowergrained DRG systems with “only” 15,000 and 6,000 health care products on cost homogeneity and predictive validity. It does so however at the cost of a high number of products with measurement and specification errors, caused by a large number of outliers and by a low number of observations in product groups. The cost-effectiveness of the DBC system is not very high: only 3% of all DBC-codes explains 80% of total costs, whereas the lower-grained DRG system uses 14% of the codes to explain 80% of total costs. Combined with the high administration cost of the DBCsystem, it was from an economic perspective, a sensible idea to replace the finegrained DBC-system by the coarse-grained DOT system.

Retrospective analysis of hospitalization costs using two payment systems: the diagnosis related groups (DRG) and the Queralt system, a newly developed case-mix tool for hospitalized patients

“…In general, it is assumed that larger number of cost groups produce more accurate cost information and, statistically, increasingly larger number of groups are expected to yield better values of the reduction in variance. However, Krabbe-alkemade et al noticed that, in some cases, a more fine-grained cost accounting system may not produce more accurate cost information due to measurement or classification errors [ 8 ]. Furthermore, they observed that higher number of groups with high reduction in variance may reduce efficiency in the sense of including many groups that do not match actual cases and could be, therefore, considered unnecessary.…”

“…The DRG was created at Yale University in the 1960s to define the services provided by each hospital and manage costs and quality and has rapidly been implemented across the globe. In its more recent version, all-patient refined (APR-DRG), it has several advantages, including transparency, exhaustivity (e.g., accounts for severity and risk levels), and the possibility of measuring quality indicators across hospitals and even countries [ 8 ]. However, it is costly and inflexible: it does not use all the administrative data available in each healthcare system and it is usually trained with the USA population and not the local one, thus precluding healthcare systems from tailoring this tool to their needs and capabilities in terms of data collection.…”