Performance Evaluation of an Information Technology Intervention Regarding Charging for Inpatient Medical Materials at a Regional Teaching Hospital in Taiwan: Empirical Study
Bibliographic record
Abstract
BACKGROUND: The process of manually recording the consumption of medical materials can be time consuming and prone to omission owing to its detailed and complicated nature. Implementing an information system will better improve work performance. OBJECTIVE: The Information System Success Model was adopted as the theoretical foundation. The opinions of nursing staff were collected to verify the impact of the system intervention on their work performance. METHODS: This cross-sectional study was conducted at a regional teaching hospital. Nursing staff were invited to participate in the field survey. A total of 296 questionnaires were collected, and of these, 284 (95.9%) were valid and returned. RESULTS: =0.709). The path of "service quality" to "user satisfaction" showed marginal significance (P=.08) under the 92% CI. Finally, the explanatory power of the model reached 68.9%. CONCLUSIONS: Support from the top management, appointment of a nurse supervisor as the change agent, recruitment of seed members to establish a pioneer team, and promotion of the system through the influence of opinion leaders in small groups were critical success factors needed for implementing the system in the case hospital. The target system was proven to be able to improve work performance, and the time saved could be further used for patient care, thereby increasing the value of nursing work. The positive experiences gained from this study could lay the foundation for the further promotion of the new system, and this is for future studies to replicate. The example of the successful experience of the case hospital could also serve as a reference for other hospitals in developing countries like Taiwan with regard to the promotion of nursing informatization.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".