Appropriateness-based reimbursement of elective invasive coronary procedures in low- and middle-income countries: Preliminary assessment of feasibility in India.
Bibliographic record
Abstract
BACKGROUND: Elective coronary interventional procedures are often overused and sometimes inappropriately used. The incentives for overuse are greater in low- and middle-income countries, where much of healthcare is provided by poorly regulated, fee-for-service systems. Overuse and inappropriate use increase healthcare costs and are potentially harmful to patients. Linking appropriate use of elective procedures to their reimbursement might deter overuse. METHODS: We explored the feasibility of introducing appropriateness criteria as a precondition to settling reimbursement claims in a publicly funded health insurance scheme in Maharashtra, India. Clinical algorithms were developed from the current best-practice criteria and used to determine appropriateness at the time of obtaining pre-authorization for elective percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG) surgeries. The number of PCIs as a proportion of the total number of procedures reimbursed under the scheme was the primary outcome measure. This proportion was compared for 1-year periods before and after implementation of appropriateness-based reimbursement, using the chi-square test. Comparisons were also made separately for public and private hospitals. The change in the proportion of CABG surgeries over the same time periods was used as a comparator (as they are less subject to inappropriate use). RESULTS: The insurance scheme provided cover to a population of 20 424 585 (18.2% of the population of Maharashtra) in 8 districts, through 106 hospitals (73 private and 33 public). There was a 12.3% (95% CI 8.9%-15.5%, p=0.0001) reduction in the proportion of PCIs performed in the 1-year period after the introduction of appropriateness-based reimbursement. The reduction was similar for public and private hospitals. There was no significant change in the proportion of CABG surgeries (2.3% v. 2.2%, p=0.20). At current rates, use of appropriateness-based reimbursement would result in approximately 783 (95% CI 483-1099) less PCIs with potential annual savings of about ₹ 57 million (US$ 0.93 million; 95% CI 0.57-1.3) to the government scheme. CONCLUSIONS: It seems feasible to implement an appropriateness-based system for reimbursement of elective coronary interventional procedures in a government-funded health insurance scheme in a developing country. This potentially cost-saving approach may reduce inappropriate use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".