Identification of Factors Causing The Return of BPJS Files (Return Claim) At Dharma Yadnya General Hospital, Denpasar City, Quarter IV of 2023
Bibliographic record
Abstract
Background: Social Insurance Administration Organization (BPJS) has a crucial role in organizing social security programs in Indonesia. BPJS aims to ensure social protection for the community. BPJS is a legal entity that has a regulatory basis to regulate its operations. Claims are bills or demands for payment or health services provided to BPJS participants. This study aims to determine the factors causing the return of BPJS claim files at Dharma Yadnya General Hospital, Denpasar City. Method: The sample of this study was BPJS claim files totaling 137 claim files. The research data used by researchers is secondary data. The results of this study are the percentage of BPJS returns due to coding 30.65%, due to completeness 29.20%, due to visit indications 32.12%, and due to treatment indications 8.1%. These results were obtained from a total of 137 claim files returned by BPJS. Results: Based on the results of the study that has been carried out on the factors for returning BPJS claims at Dharma Yadnya General Hospital, it can be concluded that the largest factor in returning BPJS files is due to visit indications of 44 BPJS claim files. Conclusion: Socialization regarding coding standard rules, visit indications, treatment indications and completeness of medical record contents should be improved.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".