Dampak Penumpukan Dokumen Rekam Medis Terhadap Waktu Pengambilan Dokumen Rekam Medis Di RSU Sinar Husni Medan
Bibliographic record
Abstract
The filling system is one of the administrators of medical records which is responsible for orderly administration in an effort to improve health services in hospitals. Accumulation of medical record documents will affect of work of officers in the filling section. The purpose of this study is to determine the impact of the bildup of medical records documents on the time of taking medical record documents at Sinar Husni Hospital. This research is a descriptive study with a qualitative approach. The population is all filling officers at the Sinar Husni Hospital and all patient medical records calculated on average in the third quarter of 2020, counted 719 documents. The samples in this study were 2 filling officers at the Sinar Husni Hospital and part of the medical record documents totaling 86 medical record documents that were taken incidentally. The instrument used was an interview guide. The measurement of time to take medical record documents uses a stopwatch. Data were analyzed descriptively. The results showed that the accumulation of medical record documents had an impact on the time to take medical record documents at the Sinar Husni Hospital, because the officers had difficulty carrying out filling activities because the access between shelves was narrower and the documents piled on the floor were not properly aligned, with an average of 10.05 minute. We recommend to add more storage space and shelves so that medical record documents that are stacked on the floor can be moved to the storage racks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".