TINJAUAN PROSEDUR PELEPASAN INFORMASI MEDIS DALAM MENJAGA ASPEK KERAHASIAN REKAM MEDIS DI RSUD dr. DARSONO KABUPATEN PACITAN
Bibliographic record
Abstract
ABSTRAK Pendahuluan: Berdasarkan pengamatan pelepasan informasi medis pada bulan Januari 2015 hingga awal bulan Desember 2015 diketahui jumlah permintaan pelepasan informasi medis sebanyak 214 formulir dengan ketentuan untuk keperluan resume medis sebanyak 80 formulir dengan persentase 37%, asuransi sebanyak 68 formulir dengan persentase 32%, jasa raharja sebanyak 47 formulir dengan persentase 22% dan permintaan visum et repertum sebanyak 19 formulir dengan persentase 9%. Penelitian bertujuan untuk mengetahui gambaran umum dan menganalisis prosedur pelepasan informasi medis dalam menjaga aspek kerahsiaan rekam medis di RSUD dr. Darsono Kabupaten Pacitan. Metode: Jenis penelitian ini menggunakan penelitian diskriptif. Subjek Penelitian ini adalah petugas yang melayanii permintaan data medis pasien di RSUD dr. Darsono Kabupaten Pacitan. Hasil: Penggunaan informasi medis pada bulan maret-mei 2016 sebanyak 41 permintaan dengan permintaan klaim asuransi yang paling banyak yaitu sebanyak 51,2%. Prosedur dalam pelepasan informasi medis di rumah sakit terdapat 2 SPO yaitu guna pegurusan visum et repertum dan permintaan data dan atau pemberian informasi rekam medis, unit terkait pelepasan informasi adalah dokter, sub bag TU, sub bag pengembangan, rekam medis, keuangan, kepolisian, asuransi. Faktor prnghambat pelepasan informasi medis adalah ketidakhadiran dokter dan tanda tangan dokter yang belum ada ketika permintaan data medis pasien. Kesimpulan: Pelepasan informasi medis, dokter sebagai pemberi hasil pemeriksaan diharapkan dalam memberikan pelayanan, maupun pemberi hasil resume medis
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.082 | 0.026 |
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".