Discussing the Impact of Management Information System (MIS) on Improvement of Efficiency and Quality of Services of Hospitals—Case Study: Tehran’s Madayen Hospital
Bibliographic record
Abstract
In every industry, improvement of efficiency is one of the main priorities of managers. Increased efficiency has more importance in service based industries, because in these industries improvement of efficiency turns into a software category and in fact, in these industries increased assets can not necessarily lead to increased efficiency. In such conditions, the focus should be shifted towards software development. One of the most important service providing sections is the section of healthcare. Both at the macro level of nations, especially Iran and the micro levels including families, healthcare services are crucially important. This section is in contact with the vast majority of people and as a result it can affect the views and satisfaction of people and therefore, is highly emphasized. In spite of that, there is several information flows embedded in the section of healthcare and especially in hospitals. Hospitals include a large flow of information and management of this information requires advanced systems. Management information systems can be crucially useful in terms of management of this information. Every year, hospitals spend huge costs for implementation of management information systems. For organization managers it is important to know that to what extent these systems can impact the quality of services. In this research it has been tried to fulfill this question and for this purpose, the existing information in Madayen hospital is utilized. For the purpose of analysis of results, the approach of analysis of structural equations was applied through the LISREL and SPSS software. Results indicated that management information systems have impacts on financial dimensions, customer orientation, quality of services and quality of internal processes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".