Journal of

Background: Achieved results from this research shows that despite in health system in country efficiencies such as work force efficiency(doctors and nurses) is not incomputable  and percentage of bed occupation and regulating shift work program prepare manually and with paper, this issue results in time consuming of managers and wasting costs and errors in carried out computations. Therefore with technology improvement and automation system there is possibility of making such automatic data is provided. Using of intelligent system consider essentially for preparing optimal program. Studying of Data showed that the present situation of planning and determination of efficiency in Iran Hospitals is not desirable. In this research, Markov methods and Petri net and genetic algorithm as modeling method and developments of model and intelligent optimization for solving problems, reducing costs and planning for work force was used successfully and there is the possibility to generalize it with changes and reforms in programming. In Achieved results of studies show that, the present research is a suitable base for expanding researches in future in field of system designs in a way that can be used in different wards and can provide more facilities to approach the real world

Material & Methods: the present study is applied and descriptive-analysis one which had been as the software design and data collection tool was the checklist of Booali Hospital's patients' files which its admissibility confirmed by the related experts and studied after observation, chronoscope and also time of manpower servicing to several patients and study of the nurse and doctors working scheduling with use of intelligent system's designing. Data analysis and planning is carried out with the method of Petri net and Markov and genetic algorithm with use of Matlab and Hpsim software

Results:42 % improvement in expense decreasing and 87% time saving in servicing time to patients can be seen from comparing prepared program and designed system.

Conclusion: Research various models can be used as a suitable tool for scheduling and determination staffs optimized number needed in several parts of a hospital which has a vital role. In case that the designed system in the current project is limited to the achieved and collected data from educational center of Booali attached to Tehran Islamic Azad university, it is need to make changes in programming based on data for generalizing it and optimized utilization in other hospitals. So it is recommended to make the prepared program close to the real world for make these systems utilizable in other hospitals and increasing restrictions.

Background: Myocardial Infarction (MI) is the most common cause of death in developed and developing countries. Based on World Health Organization prediction, MI will be the leading cause of death in the world up to 2020. The main goal of acute myocardial infarction therapy is reperfusion which is performed by two treatment approaches, although these two methods use for unique complication treatment but they are different in treatment cost and quality; Since the costs of health care is growing, this study aimed at identifying affected differentiated costs among patients with acute MI with the purpose of providing a framework to compare two treatment methods.

Materials and Methods: In presented model in current study, with the purpose of select affecting cost attributes utilized hybrid of Genetic algorithm and decision tree approach as a new method and compare its result using random method.

Results: Study result presented classifies error reduction up to 0.2 compare with random method error. Then, based on selected attributes, presented a framework comparing two treatment methods. Also, in the majority of direct cost and total cost, Primary Angioplasty was cost effective more than Thrombolytic Therapy, but in indirect cost, we achieved some results dissimilar to physicians' expected results such as kidney failure.

Conclusion: Suggested approach in this research can be a guidance to physicians and insurances institutes in acute myocardial infarction treatment policies.