Unlocking Electronic Medical Record Success: Readiness Assessment with the DOQ-IT Method
Bibliographic record
Abstract
The Regional General Hospital Genteng Banyuwangi continued to use paper-based medical records, resulting in a 44.33% delay in the return of inpatient medical records during the fourth quarter of 2023. This study aims to assess the hospital's readiness to implement Electronic Medical Record (EMR) using the Doctor’s Office Quality Information Technology (DOQ-IT) method. A total of 40 respondents participated in this study at RSUD Genteng Banyuwangi, which used the DOQ-IT method to measure readiness for EMR implementation. The assessment found that the hospital’s information technology infrastructure, leadership and governance, and human resources were all ready for implementation. However, the organizational work culture was not yet ready. Overall, the readiness level for implementing electronic medical records is at Level II, indicating that the hospital is ready for EMR implementation. The lack of Standard Operating Procedures (SOPs), limited computer skills, insufficient computer availability, and inadequate vendor and hospital training were identified as obstacles to EMR adoption. It was therefore essential to create SOPs, expand computer availability, and conduct regular training sessions to overcome these issues.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".