Cost-Effectiveness of Clinical Decision Support to Improve CKD Outcomes Among First Nations Australians
Bibliographic record
Abstract
Introduction: The Northern Territory (NT) is a hotspot for chronic kidney disease (CKD) and has a high incidence of kidney replacement therapy (KRT). The Territory Kidney Care clinical decision support (CDS) tool aims to improve diagnosis and management of CKD in remote NT, particularly among First Nations Australians. We model the cost-effectiveness of the CDS versus usual care. Methods: Taking a health care funder perspective, we modeled a cohort of people from remote NT at risk of or with CKD, as of January 1, 2017. A Markov cohort model was developed using 6 years of observed patient-level data (2017-2023), extrapolated to a 15-year time horizon. The CDS tool was modeled to improve CKD diagnosis (scenario 1), improve management (scenario 2), or improve both diagnosis and management (scenario 3). Results: The remote NT cohort consisted of 23,195 people, predominantly (89%) First Nations, with a mean age of 42 years. Scenario 3 (improved diagnosis and management) was most cost-effective at an incremental cost-effectiveness ratio (ICER) of $96,684 per patient avoiding KRT, $30,086 per patient avoiding death. Scenario 1 (improved diagnosis) was less cost-effective, and scenario 2 (improved management) was the least cost-effective. The ICER per quality-adjusted life years (QALYs) gained ranged from $3427 (scenario 3) to $63,486 (scenario 2). Conclusion: Territory Kidney Care is highly cost-effective when it supports early diagnosis of CKD and increases optimal management in diagnosed patients. These results support investing in CDS tools, implemented in strong partnerships, to improve outcomes in settings with a high burden of CKD.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".