Identification of Fundamental Issues Relevant to Implementing Electronic Health Record Medication Reconciliation: Case of Noor Hospital in Iran
Bibliographic record
Abstract
By reviewing in the extant literature, it is inferable that implementation of Electronic Health Record (EHR) is practical for providing salutary effects in the healthcare industries during the procedure of medication reconciliation (Med Rec). This research aims to identify issues or latent concepts relevant to implementing EHR system in the Noor Hospital located in Alborz province, Iran. According to the recent study by scholars, nine latent concepts are related to the implementation of EHR, which are: care coordination issues (CCI); patient education issues (PEI); ownership and accountability issues (OAI); process-of-care issues (PCI); IT-related issues (ITRI); workforce training issues (WTI); workflow issues (WI); resources issues (RI); and documentation issues (DI). The author takes a quantitative method that involves questionnaires distribution among one hundred and thirty-eight practitioners. One hundred and thirty-two valid questionnaires were returned, and collected data were analyzed through the Statistical Package for Social Sciences (SPSS). Findings supported the notion that all issues have a positive relationship with the implementation of EHR Med Rec in the Noor hospital. Among them, DI and PEI had the highest association. The current study implies arresting messages and ramifications for the managers in the healthcare industries in Iran, especially Noor Hospital, and it has its academic benefits in this research era.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".