An intelligent algorithm for optimizing emergency department job and patient satisfaction.

Purpose Resilience engineering, job satisfaction and patient satisfaction were evaluated and analyzed in one Tehran emergency department (ED) to determine ED strengths, weaknesses and opportunities to improve safety, performance, staff and patient satisfaction. The paper aims to discuss these issues. Design/methodology/approach The algorithm included data envelopment analysis (DEA), two artificial neural networks: multilayer perceptron and radial basis function. Data were based on integrated resilience engineering (IRE) and satisfaction indicators. IRE indicators are considered inputs and job and patient satisfaction indicators are considered output variables. Methods were based on mean absolute percentage error analysis. Subsequently, the algorithm was employed for measuring staff and patient satisfaction separately. Each indicator is also identified through sensitivity analysis. Findings The results showed that salary, wage, patient admission and discharge are the crucial factors influencing job and patient satisfaction. The results obtained by the algorithm were validated by comparing them with DEA. Practical implications The approach is a decision-making tool that helps health managers to assess and improve performance and take corrective action. Originality/value This study presents an IRE and intelligent algorithm for analyzing ED job and patient satisfaction - the first study to present an integrated IRE, neural network and mathematical programming approach for optimizing job and patient satisfaction, which simultaneously optimizes job and patient satisfaction, and IRE. The results are validated by DEA through statistical methods.

Dynamic safety assessment of natural gas stations using Bayesian network.

Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.

The Impact of Redundancy and Teamwork on Resilience Engineering Factors by Fuzzy Mathematical Programming and Analysis of Variance in a Large Petrochemical Plant.

BACKGROUND: Resilience engineering (RE) is a new paradigm that can control incidents and reduce their consequences. Integrated RE includes four new factors-self-organization, teamwork, redundancy, and fault-tolerance-in addition to conventional RE factors. This study aimed to evaluate the impacts of these four factors on RE and determine the most efficient factor in an uncertain environment. METHODS: The required data were collected through a questionnaire in a petrochemical plant in June 2013. The questionnaire was completed by 115 respondents including 37 managers and 78 operators. Fuzzy data envelopment analysis was used in different α-cuts in order to calculate the impact of each factor. Analysis of variance was employed to compare the efficiency score means of the four above-mentioned factors. RESULTS: The results showed that as α approached 0 and the system became fuzzier (α = 0.3 and α = 0.1), teamwork played a significant role and had the highest impact on the resilient system. In contrast, as α approached 1 and the fuzzy system went toward a certain mode (α = 0.9 and α = 1), redundancy had a vital role in the selected resilient system. Therefore, redundancy and teamwork were the most efficient factors. CONCLUSION: The approach developed in this study could be used for identifying the most important factors in such environments. The results of this study may help managers to have better understanding of weak and strong points in such industries.

A novel framework for improvement of road accidents considering decision-making styles of drivers in a large metropolitan area.

Road accidents can be caused by different factors such as human factors. Quality of the decision-making process of drivers could have a considerable impact on preventing disasters. The main objective of this study is the analysis of factors affecting road accidents by considering the severity of accidents and decision-making styles of drivers. To this end, a novel framework is proposed based on data envelopment analysis (DEA) and statistical methods (SMs) to assess the factors affecting road accidents. In this study, for the first time, dominant decision-making styles of drivers with respect to severity of injuries are identified. To show the applicability of the proposed framework, this research employs actual data of more than 500 samples in Tehran, Iran. The empirical results indicate that the flexible decision style is the dominant style for both minor and severe levels of accident injuries.

An Efficient Taguchi Approach for the Performance Optimization of Health, Safety, Environment and Ergonomics in Generation Companies.

BACKGROUND: A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented. METHODS: To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data. RESULTS: The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs. CONCLUSION: The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.

Semi-online patient scheduling in pathology laboratories.

OBJECTIVE: Nowadays, effective scheduling of patients in clinics, laboratories, and emergency rooms is becoming increasingly important. Hospitals are required to maximize the level of patient satisfaction, while they are faced with lack of space and facilities. An effective scheduling of patients in existing conditions is vital for improving healthcare delivery. The shorter waiting time of patients improves healthcare service quality and efficiency. Focusing on real settings, this paper addresses a semi-online patient scheduling problem in a pathology laboratory located in Tehran, Iran, as a case study. METHODS AND MATERIAL: Due to partial precedence constraints of laboratory tests, the problem is formulated as a semi-online hybrid shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem and response surface methodology is used for setting GA parameters. A lower bound is also calculated for the problem, and several experiments are conducted to estimate the validity of the proposed algorithm. RESULTS: Based on the empirical data collected from the pathology laboratory, comparison between the current condition of the laboratory and the results obtained by the proposed approach is performed through simulation experiments. The results indicate that the proposed approach can significantly reduce waiting time of the patients and improve operations efficiency. CONCLUSION: The proposed approach has been successfully applied to scheduling patients in a pathology laboratory considering the real-world settings including precedence constraints of tests, constraint on the number of sites or operators for taking tests (i.e. multi-machine problem), and semi-online nature of the problem.