National Health Insurance Deficit in Indonesia: Identification of Causes and Solutions for Resolution
Bibliographic record
Abstract
INTRODUCTION: Since it was implemented in 2014, National Health Insurance Program (JKN) in Indonesia experienced a financial deficit. JKN recorded a deficit of 9.7 trillion, 9.75 trillion and 10.98 trillion rupiah from 2016-2018, respectively. The deficit is estimated to still continue in the upcoming years. Systemic solutions are needed to bring JKN improvement in the future. METHODS: Data was collected from June to December 2019 by in-depth interviews with selected informants and literature review, which later was analyzed by content and with data triangulation. RESULT: The results of in-depth interviews and a review of some of the literature shows that there are four main factors that causes JKN deficit, which are capitation payment system to provider, the alleged fraud, lag of backed-referral system, and catastrophic disease. CONCLUSION: This study provides a solution to the handling of JKN deficits in the short and long term in accordance with problems in terms of funding and JKN expenditure. The solution can be an alternative policy that can be implemented by the Government of Indonesia to deal with the JKN deficit.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".