Abstract:COVID-19 has been impacting the Med-Tech industry dramatically since the beginning of 2020. Along with the pandemic continuously growing, the demand for major global medical products such as masks and protective clothing has surged. The Med-Tech industry is facing the huge challenge of a lack of production capacity, including raw material, production equipment, production line, professional human resources, and more. It would require not only the operators in the Med-Tech industry to enlarge their productivity… Show more
“…Several current studies have adopted the DEMATEL technique to evaluate complicated issues, such as user interface analysis [ 55 ], intertwined evaluation in e-learning programs through a hybrid multiple criteria decision making (MCDM) model [ 56 ], building airline safety management system [ 57 ], evaluating value-created systems for science (technology) parks [ 58 ], selecting vehicle telematics system [ 59 ], improving the performance in a matrix organization [ 60 ], evaluation of design delay factors by importance-satisfaction analysis and NRM [ 61 ], selecting the model for digital music service platforms [ 62 ], the analysis of the environmental sustainability challenges in the Indian automobile industry [ 63 ], identifying the sustainable development strategies for industrial tourism via innovation opportunity analysis-NRM approach [ 64 ], determining critical performance criteria for hospital management by the double hierarchy hesitant fuzzy linguistic term sets (DHHFL)—DEMATEL method [ 65 ], the analysis of Med-tech industry entry strategy during pandemic [ 66 ], building the digital transformation strategies for the Med-Tech Enterprises by acquisition-importance analysis-NRM approach [ 67 ], identifying the critical success factors of SDM [ 8 ], investigation of factors impeding the dissemination of medical information standards [ 68 ], assessment of urban sustainable adoption strategies and common suited paths [ 69 ], planning urban revitalization and regional development strategies [ 70 ], analyzing the driving factors of urban music festival tourism and the strategies for service development [ 71 ], exploring the cause of gas explosion accidents by the DEMATEL-ISM method [ 72 ], evaluating the risk analysis of maritime accidents using the DEMATEL and ANP technique [ 73 ], and addressing the classifier selection problem in assistive technology adoption for patients with dementia by integrating the IF-DEMATEL and TOPSIS methods [ 74 ].…”
Shared decision making (SDM) is an interactive process that involves patients and their healthcare professionals reaching joint decisions about medical care through negotiation. As the initiators of medical decision-making in daily routine, physicians should be aware of and concerned about the SDM process. Thus, professional competency development for SDM has become increasingly critical for physicians’ training. Therefore, this study investigates the professional competency and the important competency development aspects/criteria of SDM tasks through expert interviews and literature research. The study adopts the SAA (satisfaction-attention analysis) method to assess the status of competency development aspects/criteria and determine the NRM (network relation map) based on the DEMATEL (decision-making trial and evaluation laboratory) technique. The results demonstrate that the CE (concept and evaluation) aspect is the dominant aspect, and the CR (communication and relationship) aspect is the aspect being dominated. The CE aspect influences the aspects of SP (skill and practice), JM (joint information and decision making) and CR, and the SP aspect affects the aspects of JM and CR. Then, the JM aspect affects the CR aspect. The study also suggests suitable adoption paths of competency development for SDM tasks using the NRM approach. It provides recommendations and strategic directions for SDM competency development and sustainable training programs.
“…Several current studies have adopted the DEMATEL technique to evaluate complicated issues, such as user interface analysis [ 55 ], intertwined evaluation in e-learning programs through a hybrid multiple criteria decision making (MCDM) model [ 56 ], building airline safety management system [ 57 ], evaluating value-created systems for science (technology) parks [ 58 ], selecting vehicle telematics system [ 59 ], improving the performance in a matrix organization [ 60 ], evaluation of design delay factors by importance-satisfaction analysis and NRM [ 61 ], selecting the model for digital music service platforms [ 62 ], the analysis of the environmental sustainability challenges in the Indian automobile industry [ 63 ], identifying the sustainable development strategies for industrial tourism via innovation opportunity analysis-NRM approach [ 64 ], determining critical performance criteria for hospital management by the double hierarchy hesitant fuzzy linguistic term sets (DHHFL)—DEMATEL method [ 65 ], the analysis of Med-tech industry entry strategy during pandemic [ 66 ], building the digital transformation strategies for the Med-Tech Enterprises by acquisition-importance analysis-NRM approach [ 67 ], identifying the critical success factors of SDM [ 8 ], investigation of factors impeding the dissemination of medical information standards [ 68 ], assessment of urban sustainable adoption strategies and common suited paths [ 69 ], planning urban revitalization and regional development strategies [ 70 ], analyzing the driving factors of urban music festival tourism and the strategies for service development [ 71 ], exploring the cause of gas explosion accidents by the DEMATEL-ISM method [ 72 ], evaluating the risk analysis of maritime accidents using the DEMATEL and ANP technique [ 73 ], and addressing the classifier selection problem in assistive technology adoption for patients with dementia by integrating the IF-DEMATEL and TOPSIS methods [ 74 ].…”
Shared decision making (SDM) is an interactive process that involves patients and their healthcare professionals reaching joint decisions about medical care through negotiation. As the initiators of medical decision-making in daily routine, physicians should be aware of and concerned about the SDM process. Thus, professional competency development for SDM has become increasingly critical for physicians’ training. Therefore, this study investigates the professional competency and the important competency development aspects/criteria of SDM tasks through expert interviews and literature research. The study adopts the SAA (satisfaction-attention analysis) method to assess the status of competency development aspects/criteria and determine the NRM (network relation map) based on the DEMATEL (decision-making trial and evaluation laboratory) technique. The results demonstrate that the CE (concept and evaluation) aspect is the dominant aspect, and the CR (communication and relationship) aspect is the aspect being dominated. The CE aspect influences the aspects of SP (skill and practice), JM (joint information and decision making) and CR, and the SP aspect affects the aspects of JM and CR. Then, the JM aspect affects the CR aspect. The study also suggests suitable adoption paths of competency development for SDM tasks using the NRM approach. It provides recommendations and strategic directions for SDM competency development and sustainable training programs.
“…These four periods are general descriptions of trends, but they are not applicable to all industries. Under the influence of many other factors, changes in the industry life cycle are more complicated [4].…”
Section: ) Industry Life Cycle Classificationmentioning
In recent years, China's economy has continued to grow at a high speed, which has led to a substantial increase in the performance of listed companies, laying a solid foundation for my country's stock market. Especially in 2007, my country's stock market surpassed most people's imagination and completed a historic leap in the development of my country's stock market. From the perspective of mature international markets, my country's securities market is an emerging market. However, with the continuous growth and development of my country's securities market, my country's securities market will continue to be full of vitality in the future. The function of optimizing the allocation of resources in the securities market by excellent domestic enterprises is more reflected. With the maturity of my country's stock market, how investors can invest under such stock market conditions has become a problem. Generally speaking, investors need to solve two problems when investing: one is how to analyze the industry and choose an excellent investment plan; the other is how to determine the investment ratio when investors choose multiple excellent stocks. The purpose of this article is to study the logical framework of industry analysis and stock investment, and using actual data, first use the simulated annealing algorithm to experiment, observe the rate of return and risk when the preference coefficient increases, and then base the algorithm on the basis of this algorithm. A particle swarm algorithm simulation experiment was carried out on the data. The experiment proved that the particle swarm simulation algorithm has a higher return on stock investment and lower risk than normal stock investment, with a return rate of 0.41 and a risk of 0.8. The expected results of the experiment are achieved, and the stock investment based on the algorithm is effectively proved.
“…x k = phase 2 (3, 4, 5) if x k = phase 3 (4,5,5) if x k = approved for limited or full use where x k is the stage of vaccines developed in the region [74]. 5,5) if x k = very insignificant or demand increased where x k is the shrinkage of demand.…”
Section: Factor Rulementioning
confidence: 99%
“…The COVID-19 pandemic has severely affected many industries, such as tourism, aviation, telemedicine, and catering [ 1 , 2 , 3 , 4 , 5 ], and manufacturing was no exception. Factories all over the world were, to varying degrees, affected by the COVID-19 pandemic.…”
The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.
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