Law Enforcement Against Medical Personnel as Perpetrators of Fraud in the National Health Insurance Program
Bibliographic record
Abstract
JKN is program issued by the government aimed providing certainty in providing comprehensive health services for all Indonesian citizens. However, since JKN was first implemented until now, many challenges have been experienced, one of which is the large number of fraudulent acts in its implementation dominated by JKN participants themselves. Therefore, this research aims to find out. what prevention efforts have been carried out, the application of current sanctions, and how to update the application of sanctions for JKN participants who commit fraud in the implementation of JKN in Indonesia. This research is normative legal research referring to legal sources, especially statutory regulations relating to acts of fraud in healthservice programs. Legal sources in this writing consist of primary legal materials and secondary legal materials. The data collection technique used is library research. The data analysis techniques carried out in this research will go through a qualitative processing and analysis stage. The results of this research show that prevention efforts that have been carriedout by the government include reporting suspected fraud to the Fraud Prevention and Handling Team, maintaining the confidentiality of residence identity and JKN KIS cards so that they are not misused, and complying with all existing regulations, then implementing sanctions against Currently, acts of fraud only take the form of administrative sanctions,therefore the effort to reform the application of sanctions is to include the concept of criminalization in the form of imprisonment in order to catch fraudsters in the implementation of JKN in Indonesia.
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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 | 0.009 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".