Electronic Health Records Functionalities in Saudi Arabia: Obstacles and Major Challenges
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Despite the innovative technology availability, however, the functionalities of usefulness are limited and not been explored in Saudi Arabian hospitals. This study aims to determine the extent and diffusion of Electronic Health Records (EHR) in public hospitals in Saudi Arabia and to explore the main obstacles, and problems of adopting EHR in these hospitals.METHODS: A comprehensive survey was developed and sent to the medium and large size hospital stakeholders to collect their opinions on the current status of the adoption and usage of EHR. Cluster random sampling has been used. The study has been conducted in the eastern province.RESULTS: Based on the 15 hospitals surveyed in the Eastern Province (EP), Saudi Arabia, a total of seven hospitals (46.6%) had an EHR system and the implementation is running. EHR is mostly used for order entry (51.11%) and char review (41.11%) in the EP in Saudi Arabia with obstacles to be used for decision support, documentation functions, communication tools. Despite the “secured” EHR system, the results shows that security mechanism did not cover all threats.CONCLUSION: The results suggest that more public hospitals are required to adopting more and more EHR and EHR functionalities. A periodic assessment of EHR status should be performed in addition to or part of an encouraging/ enforcing policies that can significantly increase the rate of adoption of EHR systems. Managers and policymakers can benefit from the study by facing obstacles and general challenges of problems like resistance to change from the medical staff in using the information technology, low and weak financing, and train technical supporting staff for adopting EHR.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".