Evaluation of Hospital Information Systems in Iran: A Case Study in the Kerman Province
Bibliographic record
Abstract
The Hospital Information system (HIS) is a comprehensive solution that offers complete data integration for different administrative levels in hospitals. To the extent that this system is close to its aim, the efficiency and quality of health care would increase in hospitals. The performance of HIS systems in 13 hospitals in Kerman province that they were evaluated based on four major criteria of ownership, location, education and software design. Seven hospitals were located in the capital city of Kerman province. According to teaching status of hospitals, four were teaching and based on their ownership three were public. The checklist of Iranian ministry of health and medical education, containing 20 indexes were used to evaluate each hospital’s HIS system in three main supportive, diagnosis and clinical sectors. Spearman correlation coefficient was used to assess the association between major sectors. The highest score (mean±SD) was observed in laboratory information systems (88.19±13.69), resource management (84.47±8.94), and registration information systems (84.47±18.06); the lowest scores were for telemedicine (45.58±3.86), staff information and timing systems (40±16.64), and decision support systems (23.6±4.97). The total score of HIS software was positively correlated with all its three components. There were strong positive correlations between all three components. The three factors of decision support systems, staff information systems and telemedicine have an important role in providing solutions for non-structured management problems and for leading decision-makers to insights, improving human resource management and solving the problem of access to services. Thus, based on the survey findings, those three factors need to be improved in the Iranian hospital information system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".