EVALUASI AUDIT SISTEM INFORMASI MENGENAI LAYANAN KESEHATAN
Bibliographic record
Abstract
Penelitian ini bertujuan untuk memecahkan sistem informasi yang digunakan dalam layanan kesehatan dengan fokus pada tiga aspek utama: keamanan data, kepatuhan terhadap regulasi, dan efektivitas operasional. Melalui audit sistem informasi yang dilakukan pada lima fasilitas kesehatan acak, ditemukan bahwa 60% dari sistem yang diaudit memiliki perlindungan data yang memadai, namun 40% lainnya masih menggunakan metode pengamanan yang kurang efektif. Kepatuhan terhadap regulasi menunjukkan bahwa 80% rumah sakit tidak sepenuhnya mematuhi peraturan perlindungan data pribadi, dan 70% petugas kesehatan melaporkan adanya gangguan dalam operasional akibat sistem downtime. Hasil penelitian ini menunjukkan bahwa meskipun terdapat beberapa sistem yang berfungsi dengan baik, banyak rumah sakit yang perlu melakukan perbaikan pada infrastruktur TI, kebijakan pengelolaan data, dan pelatihan staf. Rekomendasi penelitian ini adalah untuk memperkuat perlindungan data pasien, meningkatkan keterjagaan terhadap regulasi, dan meningkatkan efektivitas operasional melalui perbaikan sistem dan pelatihan lebih lanjut.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".