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 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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".