Hospital Information Systems Implementation: An Evaluation of Critical Success Factors in Northeast of Iran
Bibliographic record
Abstract
INTRODUCTION: Implementation of hospital information systems (HIS) is considered as a difficult and sensitive task in terms of its scope and its mission to collect identity-related, demographic, clinical and managerial data of patients in an integrative manner as well as due to the changes it makes in users’ working practices. The purpose of the present study was to investigate users’ views and attitudes towards the key elements of successful implementation of HIS. METHODOLOGY: This applied study was conducted in a descriptive cross-sectional form. To this end, 248 users of HIS at teaching hospitals in the city of Mashhad (Northeast of Iran) were selected through stratified random sampling, and then a questionnaire was distributed to collect the required data. After collecting the questionnaires, data was entered into the SPSS software and the findings were examined by using descriptive statistics (frequency) and then illustrated in tables and diagrams. RESULTS: Functional factors, meeting users’ needs and ease of use had the highest prominence in successful implementation of a HIS. This mean that HIS considering demands of users is the first critical success factors in HIS implementation. CONCLUSION: The analysis of the research findings demonstrated that three groups including system users, technical operators (professionals) and managers have important role in implementation of HIS. Furthermore, successful implementation of HIS was required to be performed through a formulated program with specified time, costs, and manpower in which the employment and participation of various users of the system had been precisely defined. In this respect, financial supports and presence of hospital management team in meetings and decisions was also of utmost importance.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".