Assessing the performance of a method for case-mix adjustment in the Korean Diagnosis-Related Groups (KDRG) system and its policy implications
Bibliographic record
Abstract
Abstract Background To evaluate the performance of the patient clinical complexity level (PCCL) mechanism, which is the patient-level complexity adjustment factor within the Korean Diagnosis-Related Groups (KDRG) patient classification system, in explaining the variation in resource consumption within age adjacent diagnosis-related groups (AADRGs). Methods We used the inpatient claims data from a public hospital in Korea from 1 January 2017 to 30 June 2019, with 18 846 claims and 138 AADRGs. The differences in the total average payment between the four PCCL levels for each AADRG was tested using ANOVA and Duncan’s post hoc test. The three patterns of differences with R-squared were as follows: the PCCL reflected the complexity well (valid); the average payment for PCCL 2, 3, and 4 was greater than PCCL 0 (partially valid); the PCCL did not reflect the complexity (not valid). Results There were 9 (6.52%), 26 (18.84%), and 103 (74.64%) ADRGs included in the valid, partially valid, and not valid categories, respectively. The average R-squared values were 32.18, 40.81, and 35.41%, respectively, with an average R-squared for all patterns of 36.21%. Conclusions Adjustment using the PCCL in the KDRG classification system exhibited low performance in explaining the variation in resource consumption within AADRGs. As the KDRG classification system is used for reimbursement under the new DRG-based prospective payment system (PPS) pilot project, with plans for expansion, there should be an overall review of the validity of the complexity and rationality of using the KDRG classification system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.131 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".