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H Yaghfoori , A Sahrai , S F Khalifelo ,
Volume 13, Issue 2 (8-2014)
Abstract
Abstract
Background: Zone 3 of Zahedan City includes the most deprived areas in province and country. The purpose of this research was assessing the distribution of the health care center (hospitals) and determining the underserved areas based on hospital locating concidering standards and criteria.
Materials and Methods: In order to achieve accurate results based on GIS software, spatial and descriptive data using statistics and field observation gathered and connected to database. Besides, AHP Model used to set priorities in locating the optimal urban spaces for building hospitals in the city.
Results: Suitable zone with special symbol severance divide from another zones was determine to develop landuse based on dependency to health care centers aimed at providing services in local level . Similarly, two Places recommended constructing health care centers in complete suitable zone. Unsuitable and compeletly Unsouitable zones was determined in zone three of zahedan city in finaly map.
Conclusion: study analysis based on urben percapita, population and needed availibilty to health care centers suggests that Zahedan city need two new hospitals setting which has been indexed on final map.
Habib Ebrahimpour, Nourmoohammad Yaghubi, Seyd Saied Zahedi,
Volume 15, Issue 2 (6-2016)
Abstract
Background: The organizational learning has been influenced in different theories and model based on theoretical and practical dimensions in organizations development and provides a favorable context for changing and development. Organizational learning capacity can play a main role in clinical governance implemention.
Materials and Methods: This study was a descriptive- analitical and cross-sectional one which performed during the first six months of 2014. Study population included staff of Ardabil Social Security hospital. One hundred and seventy participants selected using simple random sampling. A four dimensional standard questionnaire of Gumejeet et al and a seven dimensional self administrated questionnaire were conducted to examine organizational learning capacity and clinical governance assessment, respectively. Data analysis was carried out using Pierson Correlation Coefficient and Mulivariate regression analysis. Data was analyzed by SPSS18 software.
Results: Study results revealed that there was a positive and significant relation between organizational learning capacity and clinical governance implementation (R= 0.507). This correlation coefficient was 0.644 in management commitment, 0.498 in systematic approach, 0.446 in open climate and 0.261 in knowledge transfer.
Conclusion: According to the main role of organizational learning on implementing clinical governance, providing an essential background to enforce organizational learning capacity in four components especially management commitment and systematic approach to implement efficient clinical governance is recommended.
Dr Farzad Firouzi Jahantigh, Mojtaba Ghaderi,
Volume 17, Issue 2 (9-2018)
Abstract
Background: Among various emergency services, the air emergency due to access to the extreme areas, possibility to move more patients, providing higher quality treatment to the patient being carried and also access to the hospital without problems such as traffic and sudden crashes, is one of the most important types of emergencies in the health sector. So, the right location according to the scientific principles, enhances the efficiency of the aerial emergency.
Materials and Methods: This descriptive- analytical present study was conducted as an applied research in Sistan and Baluchestan province in 2016. At first, indicators affecting the location of ambulances were identified. Then, the location using deploying a Fuzzy network analysis process model next to the Fuzzy Dematel technique and the integration with geographic information system was performed.
Results: Criteria for selecting the best places for deploying air ambulances in Sistan and Baluchestan province are considered as proximity to the roads, appropriate tilt area, proximity to crowded areas, proximity to high risk passages, and the convenient distance from the medical emergencies. Output weights of the technique used for affecting on GIS software were calculated 0.244, 0.083, 0.435, 0.182 and 0.057, respectively.
Conclusion: Study results revealed that number and coverage of aerial ambulances in cities and roads of Sistan and Baluchestan province are not suitable. Therefore, the map derived from the Fuzzy integration of the information layers identified by the effective factors, illustrated that the districts of Zabol and Iranshahr cities have the best status of selected criteria to establish the air emergencies bases in the province of Sistan and Baluchestan.
Motahareh Payam, Dr Farzad Firouzi Jahantigh,
Volume 18, Issue 3 (10-2019)
Abstract
Background: According to the importance of health and treatment, it is necessary to use suitable models for planning and setting surgery time. In this study, a mathematical model is offered for operational scheduling of surgeries at surgery rooms of hospitals.
Material and Methods: This is an applied study and its data is related to the surgery rooms of Zahedan Al-Zahra eye hospital. Study population was the surgeries performed in March 2016. The mathematical model of scheduling surgeries at the surgery room was optimized in MATLAB2014.
Results: Due to limitations on patient admission capacity in hospital surgery ward, 79 surgeries were called off. In the proposed model, the total waiting time index for performing surgeries was 1547.29, and this index was found to be 1842 without the use of the model. Therefore, the waiting time index was improved by 16%. In accordance with the third purpose (objective) function, the tally of delays for predicted surgery ending time in one month was estimated to be 69.15 hours. The process of each surgery includes four defined activities. The end time of the activities related to each surgery has been examined and it has been optimized according to the existing limitations.
Conclusion: The proposed model can improve the waiting time by 16% and makes it possible to choose the surgical procedures that should be canceled and delayed according to medical priorities.
Neda Vahedi Nezhad, Farzad Firouzi Jahantigh,
Volume 20, Issue 1 (5-2021)
Abstract
Introduction and purpose: Risk assessment is a necessity in high-risk work environments like hospitals. During epidemics, the need to maintain the health of healthcare staff increases as they are effective people in controlling the spread of the disease. The purpose of this study was to assess the occupational safety of healthcare staff against coronavirus using FMEA in
infectious diseases ward of Bu
-Ali Hospital in Zahedan.
Methodology: Failure modes were identified using brainstorming technique. After scoring them with S, O and D, they were prioritized by calculated RPN. To improve the traditional FMEA, failure modes were prioritized with weighted FMEA and MCDM techniques. After identifying the critical failure modes, the root causes of them were identified and categorized. Finally, corrective solutions were provided to handle them.
Results: Three processes including emergency admission, patient visit, and sampling were identified as priority processes. 58 failure modes and their effects were identified in 6 categories. 13 critical failures modes (RPN above 100) equivalent to 22% were identified. Then 42 root causes of them were identified by brainstorming technique and their classifications were done by Eindhoven. Finally, 49 corrective strategies were presented to handle critical risks.
Conclusion: Identifying 58 risks and their effects, identifying and classifying root causes and providing corrective solutions indicate the capability of the FMEA to assess the risk of critical departments such as hospitals. As a result, the FMEA is able to detect risks, reduce their consequences and improve quality. Risk assessment techniques along with the commitment of managers and the renewal of organizational policies can ensure the effectiveness of these activities
.
Rahele Panjekoobi, Farzad Firouzi Jahantigh,
Volume 20, Issue 4 (12-2021)
Abstract
Background and Aim: As difficulties increase, the level of uncertainty and risk in the supply chain increases. Medicine is a strategic product and is directly related to community health. The aim of this study is to evaluate the risk factors of pharmaceutical supply chain with artificial intelligence methods.
Materials and Methods: By reviewing the texts and interviewed 6 adept experts who had a Master’s degree and Ph.D. and had experience between 7 and 15 years in the field of risk and pharmaceutical supply chain, risk factors were identified. Finally, using multilayered perceptron neural networks and support vector machines with polynomial linear kernel functions and radial base in two low-risk and high-risk classes were classified in Python software.
Results: 22 factors were identified and classified using neural networks in 5 categories: assets, network and transportation, government and market, strategy and supplier. Shift in interest and inflation, Changes in exchange rates, Inflexibility in production and disruption of customer service are the most important risks in the pharmaceutical supply chain, respectively. The results of evaluation criteria showed that the multilayer perceptron model had better performance than the support vector machines with linear, polynomial and radial basis functions.
Conclusion: The results showed that artificial neural networks are able to classify pharmaceutical supply chain risk factors with acceptable accuracy. As a result, classification of risk factors with an accuracy of 97/07% indicates the high ability of multilayer perceptron network in risk assessment of pharmaceutical supply chain.
Mohaddese Arefi, Farzad Firouzijahantigh,
Volume 22, Issue 3 (12-2023)
Abstract
Background and purpose: The organ transplant network is among the most complex and challenging systems in the healthcare sector. This study presents a three-objective hierarchical location model for kidney transplants, aiming to simultaneously minimize total time and costs while maximizing geographic equity in the supply and demand network for donated kidneys. Various transportation modes within the network are also analyzed.
Materials and Methods: This applied research was conducted over a one-year period in 2022 (1401 in the Iranian calendar) in the province of Sistan and Baluchistan. The proposed mathematical model was implemented in GAMS software and solved using the Torabi-Hosseini method and epsilon constraint technique.
Results: The model recommended establishing candidate locations for organ collection units and transplant centers without the need for air transport equipment. It suggested that only the candidate location number 2 at Zabol Hospital Transplant Center should be equipped with air transport facilities, while the other proposed locations do not require the establishment or use of air emergency services.
Conclusion: The results indicate that the designed kidney transplant network is practical and feasible. Efficient network management ensures that all organ recipients, even those far from the provincial center and in remote areas, have timely access to the necessary facilities and equipment for transplant operations.
Mohammadreza Shahraki, Hamidreza Esmaeili,
Volume 22, Issue 4 (1-2024)
Abstract
Background and Purpose: Artificial intelligence (AI) plays a crucial role in the optimal management of hospital waste, particularly in predicting the volume and type of waste generated. This study aims to identify and rank the risks associated with the use of AI systems in hospital waste management by employing a multi-criteria decision-making approach.
Methods: This descriptive, cross-sectional study was conducted in 2023 (1402 in the Iranian calendar) at two hospitals, Ali Ibn Abitaleb and Khatam al-Anbiah, in Zahedan. Ten hospital staff members were selected as expert participants for the Delphi panel. The Shannon entropy method was utilized for risk weighting, and the TOPSIS method was applied to rank the identified risks.
Results: Kendall's coordination coefficient was used to assess the level of consensus among the Delphi panel members, with the coefficient values for the first, second, and third Delphi rounds being 6.3, 7.1, and 7.3, respectively. The indicators were weighted using the Shannon entropy method, based on three criteria: impact intensity (0.3), probability of occurrence (0.4), and detection probability (0.32). The TOPSIS method was then employed to rank the identified risks, with the most significant risks being the need for necessary infrastructure (0.847), the requirement for accurate and complete data (0.751), and budget constraints (0.749).
Conclusion: By applying multi-criteria decision-making methods, healthcare managers can effectively identify and prioritize the risks associated with using AI systems in hospital waste management, enabling them to focus on strengthening waste management practices based on these priorities.
