Investigating the impact of electronic health record on healthcare professionals
Bibliographic record
Abstract
Although there is a significant influence of implementing the electronic health records (EHR) system in Qatar, there are very limited studies reviewed and analyzed the influence of implementing the EHR system on healthcare professionals in Qatar. This research aims to assess, summarize, and analyze the influence of the EHR system in healthcare settings in Qatar. The outcome of assessing the implementation of the EHR system may have advantages and disadvantages, which can impact healthcare professionals in healthcare settings in Qatar. The main objective is to evaluate EHR on healthcare professionals in healthcare. A total number of 210 participants were selected randomly from three private hospitals in Qatar. A validated survey distributed to physicians, pharmacists, nurses, and dietitians who work in these healthcare hospitals in Qatar. The purpose is to identify whether the outcome of using the EHR system improved healthcare professionals’ work after it has shifted from using files and hand-writing paperwork to the EHR system. By applying online survey, results indicate that most healthcare professionals positively perceive the use of the EHR system as a valuable 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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".