Analisa Ketepatan Kode Diagnosis Berdasarkan ICD-10 dengan Penerapan Karakter Ke-4 pada 10 Besar Penyakit Tribulan IV
Bibliographic record
Abstract
The accuracy of the diagnosis code on the medical record document is used as a basis for making reports. If the diagnosis code is not properly coded then the resulting information will have low validation. The results of the initial survey at UPTD Puskesmas Genuk in the Top 10 Trimonth III Disease Data in 2022, there are still many diagnosis codes that have not been coded until the 4th character. To determine the accuracy analysis of disease diagnosis codes based on ICD-10 with the application of the 4th character of the fourth quarter at UPTD Genuk Health Center in 2022. This type of research is descriptive quantitative with a retrospective study approach. The primary data sources used are observation and interviews while the secondary data is from medical record data obtained from SIMPUS. The total population was 4,861 medical record data while the samples used were 98 with systematic random sampling techniques. Based on the results of research on 98 medical record data, the exact code is 21 (27.23%) while the incorrect code is 77 (72.77%). The inaccuracy of the diagnosis code is because the coding officer is not PMIK, the code is given only up to the 3rd character, there is no SPO for Giving Disease Diagnosis Codes, not using ICD-10 but a list of SIMPUS codes. accuracy of the diagnosis code of 27.23% is much lower than the inaccuracy, coding officers from PMIK should be conducted, Training on Giving Disease / Action Coding, Diagnosis Codes at SIMPUS are given keys so that coders can choose a specific code, SIMPUS plus facilities for PMIK officers to validate coding after service.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".