Preliminary considerations for electronic medical record (EMR) in the public hospitals of Gandaki province in Nepal
Bibliographic record
Abstract
Electronic Medical Record (EMR) is the digital tool to keep records of valuable information on computer or computer-like equipment, regarding health systems, including patient’s records and stock management which can provide real time evidence for better patient record, clinical care and health policy making. As Nepal does not have a national EMR system, and it is a challenge to get real time data, information and evidence required for effective health policy making. With an effective national EMR system in Nepal, the health system can be improved with reliable information of patients for the continuity of clinical care. There are existing scientific evidence of EMR on the lessons of implementation in Nepal, opportunities for the use of EMR in Malaysia, perspectives of health care workers on EMR after its implementation in Canada, on the development of success measuring tools and the use of questionnaires for the EMR implementation in Canada, best practices, impact of EMR on physician practice, and barriers for EMR implementation. EMR can improve the quality, effectiveness, transparency and efficiency of healthcare services and its management. Gandaki province is one of seven provinces in Nepal and is exploring ways to implement EMR in all the hospitals operated under the province government. There can be step wise approach to have EMR in all of these hospitals. This can give more strategies for the national EMR system.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".