Applications of Cloud Computing in Health Systems
Bibliographic record
Abstract
INTRODUCTION: Equitable access to health services is one of the health justice criteria. E-health can sometimes be helpful in this regard. This study is aimed to find the use of cloud computing services across health system.METHOD: In the present review article, numerous research papers from different resources, such as MEDLINE, IEEE and Science direct, were studied. Based on the subject, 210 studies were found. After quality analysis of the papers, 78 studies were selected, from which 53 articles were directly related to the applications of cloud computing in health system.FINDINGS: Cloud computing services are widely used in various industries. Therefore, health system takes advantage of the services. Findings indicate that, the applications of cloud computing in health system, including telemedicine, medical imaging, public and personal health, clinical and hospital information systems, medical decision support system, care, secondary use of health data, serve as different types of specialized software used to analyze gene sequences and archive huge biological data. Generally cloud computing services are available in two sectors in any health system as follows: E-health services and Bioinformatics.CONCLUSION: Facilitated access to the E-health services and big data in health systems are the main features of exploiting cloud computing services in health systems. Using cloud computing in health systems not only makes health services more affordable, but also helps nations to achieve health equity.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".