Showing 5 results for Firouzi Jahantigh
R Baradaran Kazemzadeh , M Sepehri , F Firouzi Jahantigh ,
Volume 12, Issue 4 (3-2014)
Abstract
Background and purpose: Hospital is the largest and most important executive unit of healthcare system therefore, full consideration of how to assess its quality is of particular importance. A question is always raised as how to evaluate the quality of the services. The current study seeks to provide a fuzzy model for assessing the service quality in this healthcare sector.
Material and method: The present cross-sectional study was conducted in two hospitals in Zahedan 2012.Via reviewing the related literature, the dimensions and components of service quality assessment were identified. The SERVQUAL questionnaire for hierarchical analyses was designed and the fuzzy AHP (Analytical Hierarchy Process) model was presented. SPSS v 10.0 and Fuzzy TOPSIS Solver 2013 software were used to analyze data.
Results: The findings indicated that the most important dimension for estimating the quality of healthcare services was empathy. Responsiveness, assurance, and tangible assets were the last important factors. The hospitals were compared using fuzzy AHP. According to the calculations, the ranking of the hospitals based on their performance was as follows: Imam Ali hospital with 31% compared to Social Security hospital with 29% had a better performance in service quality.
Conclusion: The results revealed that hospitals needed to focus more on empathy, expertise and reliability than providing high quality and satisfactory services. By considering their weaknesses, each of these hospitals can enhance service quality and consequently, provide a better service for patients.
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.
