Effect of implementing a standardize process on the quality of discharge summaries
Bibliographic record
Abstract
Objective: To assess the compliance rate and overall quality of discharge summaries among KFSH & RC patients according JCIA standards, during three years. Methods: A multidisciplinary team formulated, including the Quality director in the department of pediatrics as a chair person, the chairman of the department of pediatrics, the chief resident, a representative from medical records, a representative from the quality management, a representative from the information technology department and a representative from the medical and clinical affairs. An analysis of the situation as well as an explanation of the project idea was discussed with all team members and the process was built up as following: (See appendix 1) Results: Between the first quarter of 2011 and the first quarter 2014, a significant drop in the number of delinquent records have been documented where it was 1131 at the end of the first quarter of 2011 and dropped down to 68 records at the end of the second quarter of 2014. as well as the compliance to the JCIA standards which represent the documentation quality that has been audited by the hospital quality management department using a random, retrospective audits for 20 charts selected from the ICIS and reported on quarterly basis It shows improvement from 70% in the first quarter of 2013 up to 79% in the first quarter of 2014, and 86% in the second quarter of 2014, making the department of pediatrics ranking the first position across the hospital in comparison to the other department in regard to the quality of the discharge summaries. Conclusions: It is been found that by implementing a systematic framework in building up a discharge summaries for the patients will lead to a better quality of documentation and will minimize unnecessary delay. Comprehensive and precise clinical documentation improves the quality of patient care as well as the health information that is critical for ideal management of the health care 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 | 0.099 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".