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Showing 4 results for Decision Making

M Zahavi, N Sadre Momtaz, Sh Arpanahi Istadegi,
Volume 3, Issue 3 (3-2010)
Abstract

Background and Aim: The main goal of each hospital - like all health care sectors - is protecting and increasing the health level in orsig country. The important role of human resources in hospitals is appropriate strategic decisions making for fast environmental changes and shows the need of staff participation. Nurses as a one of the biggest parts of hospital staff need to participate much more in strategic decision making in hospitals. This article is aimed to measure the level of nurses' participation in strategic decisions making.  

Materials and Methods: This research is descriptive - analytic. Total nurses of Shariati hospital were 577 nurses of which 175 nurses were selected and studied.The gathering information instrument was questionnaire adapted from Dustdar thesis. This questionnaire contained 35,5 - choice questions set by Likert basis. Project's data was analyzed with SPSS software in % 95 confidence levels and statistical specifications like mean, standard error,correlation and non parametric tests were used.

Results: According to the results obtained, there are meaningful relation between staff participation in strategic decision making and: marriage situation (PV=0.01), Decision Directing (PV=0.0), culture of participation (PV=0.00), organization maturity (PV=0.00) and risk tolerance (PV=0.01)  

Conclusions: It has been observed that nurse participation in strategic decision making in Shariati hospital is in "low" position (participation mean score: 28.52 from 100). So an administrator effort for spreading culture of participation to owner's process is something that should be paid attention more than before.


Abdollah Reihani Yasavoli , Seyed Saeed Tabatabaee , Mehdi Moghadasian, Shamsodin Nazemi , Hamid Shahbahrami, Rohollah Kalhor,
Volume 8, Issue 6 (3-2015)
Abstract

Background and Aim: Purchasing hospital equipment is one of the most important decisions made by managers and health professionals. Gray systems theory is an approach to deal with issues under uncertainty conditions where decision makers are facing a small number of unspecified data. The purpose of this paper is to apply the gray theory for the purchase of anesthesia machine .

Materials and Methods: This study was a survey in which mathematical analysis was used. Data collection devices included documents, interviews and questionnaires. In order to solve multiple criteria decision making in uncertain conditions, a model based on gray system theory was used. Data analysis was performed using Excel software.

Results: The findings show that among anesthetic equipment bought by public and private hospitals, brand A is superior to other brands. However, the gray possibility degree for the other brands is significantly different from brand A.

Conclusion: In addition to helping to purchase anesthetic equipment for hospitals, the results of this study are also useful for companies manufacturing medical equipment to evaluate their situation in terms of the studied criteria and take appropriate measures to develop their strengths and improve their weaknesses.


Javad Ahmadi, Jamshid Bahmei, Mohamad Ranjbar, Hamed Rahimi , Hamze Shahbazi ,
Volume 9, Issue 2 (7-2015)
Abstract

Background and Aim: To perform plans and achieve their goals, organizations are in need of different factors. One of the most important of which is the existence of an ideal organizational climate and participation. The main goal of this study is to determine the relationship between organizational climate and faculty members' participation in decision makings at Shahid Sadoughi University of Medical Sciences (SSUMS) in Yazd. Materials and Methods: This was a cross-sectional, descriptive and analytical study induced in 2011-2012. The research population comprised all 292 SSUMS faculty members. 72 members were selected through random-category sampling method. For data collection, two questionnaires included organizational climate and participation were used. The reliability of two questionnaires were 0.86 and 0.85, respectively, using Cronbach's alpha coefficient. The validity of both questionnaires were confirmed by expert panel. The data were analyzed using SPSS software version 18. For descriptive results, and used Pearson test for Analytical results. Results: The average of total participation rate for faculty members was computed to be 3.43. The total figure for organizational climate with a mean of 82.6 was obtained from the total average of micro-scale scores. A Pearson correlation coefficient of 0.418 showed that there was a direct relationship between organizational climate and faculty members' participation. Conclusion: In spite of weakness of counselling system and team work system, and approach diversity between the faculty members, they are enthusiastic to cooperate to solve the university problems. Therefore we suggest more financial and spiritual mechanisms for autonomy of the faculty members.
Saman Mohammadpour, Reza Rabiei, Elham Shabahrami, Kamyar Fathisalari, Maryam Khakzad, Mostafa Langarizadeh,
Volume 16, Issue 2 (5-2022)
Abstract

Background and Aim: Cancer is the second leading cause of death in the world, which leads to the death of more than 10 million people in the world every year. Its early diagnosis, management and proper treatment play an important role in reducing complications and mortality. One of the support tools in early diagnosis, treatment and management of this disease are Clinical Decision Support System (CDSS), which are divided into two groups, rule-based and non-rule-based. Rule-based decision support systems are created based on clinical guidelines, while non-rule-based decision support systems use machine learning. In this research, the effects of decision support systems, rule-based and non-rule-based, on cancer diagnosis, treatment and management were measured.
Materials and Methods: The present study was conducted using a systematic review method, which was conducted by searching the Web of Science, Scopus, IEEE and PubMED databases until 12/31/2021. After removing duplicates and evaluating the characteristics of the inclusion and exclusion criteria, studies related to the goal were selected. The selection of articles was based on the title, abstract and full text The data collection tool was the data extraction form, which included year of study, type of study, system of body, organ of body, the service provided by the decision support system, type of decision support system, effect, effect index and the score of effect index. Narrative synthesis were used for data analysis.
Results: Out of 768 articles, 16 articles related to the objectives of the study were identified. Studies were presented in two categories of clinical decision-support systems: Rule-based and non-Rule based. The effects evaluated in the clinical decision support systems were Rule-based, dose adjustment, symptoms, adherence to treatment guidelines, care time, smoking, need for chemotherapy and pain management, all of which except pain management were significant and positive. The effects evaluated were in the category of non-Rule based clinical decision support systems, diagnostic and therapeutic decisions, controlling neutropenia, all of which were significant and positive except controlling neutropenia.
Conclusion: The results obtained for the effectiveness of both Rule-based and non-Rule-based decision support systems indicated different benefits of these two categories. Therefore, using their combination in the field of cancer can bring very useful results.


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