Opening the black box of diagnosis-related groups (DRGs): unpacking the technical remuneration structure of the Dutch DRG system

Comparison of diagnosis-related groups (DRG)-based hospital payment system design and implementation strategies in different countries: The case of ischemic stroke

Diagnosis-related groups (DRG) based hospital payment systems are gradually becoming the main mechanism for reimbursement of acute inpatient care. We reviewed the existing literature to ascertain the global use of DRG-based hospital payment systems, compared the similarities and differences of original DRG versions in ten countries, and used ischemic stroke as an example to ascertain the design and implementation strategies for various DRG systems. The current challenges with and direction for the development of DRG-based hospital payment systems are also analyzed. We found that the DRG systems vary greatly in countries in terms of their purpose, grouping, coding, and payment mechanisms although based on the same classification concept and that they have tended to develop differently in countries with different income classifications. In high-income countries, DRG-based hospital payment systems have gradually begun to weaken as a mainstream payment method, while in middle-income countries DRG-based hospital payment systems have attracted increasing attention and increased use. The example of ischemic stroke provides suggestions for mutual promotion of DRG-based hospital payment systems and disease management. How to determine the level of DRG payment incentives and improve system flexibility, balance payment goals and disease management goals, and integrate development with other payment methods are areas for future research on DRG-based hospital payment systems.

Dealing with Time Estimates in Hospital Cost Accounting: Integrating Fuzzy Logic into Time-Driven Activity-Based Costing

BACKGROUND

Time-driven activity-based costing (TDABC) can support value-based healthcare (VBHC) programs by providing insights into the actual relationships between time spent by the medical staff and the costs associated with specific care cycles. However, the robustness of time estimates (time variation) as well as the effort required to obtain these estimates are major challenges of the TDABC methodology, given the heterogeneity in patients' needs and the presence of (multi)morbidity. To allow for the variation in time estimates in an efficient manner, this study uses fuzzy logic (FL) to estimate the TDABC model parameters (FL-TDABC).

METHODS

A standardized care path was used to calculate the annual costs (per patient) and cost drivers of the Rheumatoid arthritis (RA) care cycle following the FL-TDABC methodology. Cost information (2018) was derived from hospital reports concerning financial, human resource and business intelligence data from a Dutch top clinical research hospital, Maasstad Hospital. Time estimates of procedures were obtained by interviewing the medical staff and relevant care activities were extracted from electronic health records. For analytical and validation purposes, FL-TDABC estimates were compared with TDABC and ABC cost estimates.

RESULTS

The RA care cycle annual costs totaled €1497 per patient (2018 prices) based on the FL-TDABC methodology. Maximum RA cycle costs (€1684) were some 22% higher than minimum costs (€1317) observed from FL-TDABC. Cost drivers explaining the cost variation are predominantly the number of consultations with rheumatologists and pharmacy costs related to RA. Based on TDABC and ABC, annual costs per patient were €1609 and €1604, respectively.

CONCLUSIONS

The FL-TDABC methodology offers a more precise and efficient estimate of care cycle costs, allowing for the subjective (fuzzy) nature of healthcare time estimates made by the medical staff. As a result, the FL-TDABC provides insight into the practice variation, and hence it can promote the transition from a volume-based system to a VBHC system.

DRG grouping by machine learning: from expert-oriented to data-based method

Background

Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost.

Methods

This study proposes a method of data-based grouping, designed and updated by machine learning algorithms, which could be trained by real cases, or even simulated cases. It inherits the decision-making rules from the expert-oriented grouping and improves performance by incorporating continuous updates at low cost. Five typical classification algorithms were assessed and some suggestions were made for algorithm choice. The kappa coefficients were reported to evaluate the performance of grouping.

Results

Based on tenfold cross-validation, experiments showed that data-based grouping had a similar classification performance to the expert-oriented grouping when choosing suitable algorithms. The groupings trained by simulated cases had less accuracy when they were tested by the real cases rather than simulated cases, but the kappa coefficients of the best model were still higher than 0.6. When the grouping was tested in a new DRGs system, the average kappa coefficients were significantly improved from 0.1534 to 0.6435 by the update; and with enough computation resources, the update process could be completed in a very short time.

Conclusions

As a new potential option, the data-based grouping meets the requirements of the DRGs system and has the advantages of high transparency and low cost in the design and update process.