Fragmentation of payment systems: an in-depth qualitative study of stakeholders’ experiences with the neonatal intensive care payment system in Iran
Bibliographic record
Abstract
BACKGROUND: Iran's fee-for-service (FFS) payment model in neonatal intensive care units (NICUs) is contentious due to the involvement of multiple stakeholders with differing interests, leading to increased costs, fragmentation, and reduced quality of care. This study explores the experiences and challenges of stakeholders with the NICU payment system and considers alternative payment methods. METHOD: A qualitative research approach was used, involving key informant interviews with stakeholders at various levels of the health system. Data were collected between March 2022 to September 2023 using a purposive sampling method with a snowball strategy. The transcribed data were analyzed using an inductive thematic approach in MAXQDA, with themes and sub-themes emerged and assessed by two independent coders. Four trustworthiness criteria were applied to ensure the quality of the results. RESULTS: The study involved 23 participants with diverse NICU payment backgrounds, identifying issues related to service accessibility, rising costs, neonatologists' income, and service quality. Stakeholders held differing views on the best payment model: health insurance executives favored a prospective payment method, faculty members favored supported modified FFS or per diem, and neonatal specialists expressed concerns about low tariffs and delayed payments. CONCLUSION: Iran's NICU payment system is unsatisfactory and requires urgent reform. Although stakeholders disagree on the best approach, reforms must be evidence-based and collaborative, addressing structural and cultural issues within the health system. The identification of an optimal payment system is essential for supporting neonatal care, benefiting newborns, families, society, and the broader health 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".