The assessment of Picture Archiving and Communication System based on Canadian Infoway PACS Opinion Survey in Teaching Hospitals of Shiraz University of Medical Sciences
Bibliographic record
Abstract
Introduction: Today the use of information technology in accordance with the rapid environmental changes and flexibility acquisition is necessary and unavoidable. Picture Archiving and Communication System (PACS) is one of the medical information technology used in health facilities. PACS provides the images archive and transmission possibility electronically in different units of the teaching and treatment centers. This study aimed to assess the PACS system in teaching hospitals of Shiraz University of Medical Sciences based on a survey of Canadian Infoway. Method: This descriptive cross-sectional study was performed on 53 individuals selected through Two-Stage Stratified Random Sampling. The study population consisted of 156 PACS users in Shiraz University of Medical Sciences hospitals which were equipped with the PACS system in 2015. Data were collected by the valid and reliable customized questionnaire of Canadian Infoway. The reliability was measured by a pilot study on 25 PACS users; Cronbach’s Alpha was estimated 0.78. Data were analyzed using SPSS 18. Also, frequency, mean, standard deviation were used. Results: The results are presented in three sections: environment (Background Variables), benefits and challenges of PACS. As to the system availability, 20.8% of the users in the clinic, 75.5% in the diagnostic imaging department, only 3.8% in offices had access to the PACS. As to system accessibility, 49.1% of the users just had access to tests, 5.7% only to the reports, and 45.3% to both reports and tests. With respect to benefits of PACS, the mean was 4.16 (SD: 0.5) (five-point scale 1-5) estimated, and in challenges, the mean was 3.48(SD: 0.5) (five-point scale 1-5). Conclusion: The results showed that although PACS could eliminate many restrictions concerning the use of radiology images and films, there were challenges in this regard. Users are recommended to have access to PACS in all clinics, physicians’ offices, and diagnostic imaging department. The majority of users agreed with the PACS benefits. Adequate management measures must be taken to maximize the benefits derived from this system and the utilization of information in order to improve the quality of care. Adequate training and elimination of the deficiencies could affect the use of this system and improvement in the health care services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".