Analisis Penerapan Sistem Informasi Manajemen Rumah Sakit (SIMRS) terhadap Kinerja Pelayanan Kesehatan di Rumah Sakit X
Bibliographic record
Abstract
The evaluation of information systems becomes important to ensure the effectiveness of the Hospital Information System (SIMRS) application and its positive impact in producing information that meets the standards of data quality with the HOT fit theory, which includes the core elements of the information system: human, organization, Technology and net benefits. The research was conducted using a cross-sectional design and involved 78 SIMRS users in X Hospital as the sample. Data were analysed using the partial least squares (PLS) structural equation modelling (SEM) method. The results of the study showed that user satisfaction, information quality, service quality, system quality, organizational environment, system utilization level, and organizational structure had significant relationships with SIMRS based on hypothesis testing. The significant findings included user satisfaction, net benefits, system utilization, service quality, system quality, organizational environment, and information quality. This study emphasizes the importance of routine and periodic maintenance, monitoring of SIMRS by relevant units, attention to factors influencing SIMRS adoption by users, and regular training related to the operation of the SIMRS application. Efforts to improve operational skills in the aspects of system quality, information quality, service quality, organizational structure, and organizational environment of SIMRS are crucial to optimize the net benefits generated by SIMRS.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".