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Peng-Ting Chen joined the Department of Biomedical Engineering (BME) and the International Institute of Medical Device Innovation Centre (MDIC) at the National Cheng Kung University (NCKU) as an associate professor. Chen's research scope mainly focuses on strategic analysis and planning. The filed includes the latest technology management issues, such as resource allocation analysis for medical big data and artificial intelligence-related applications, biomedical technology startups barriers, medical device R&D projects, biomedical startup incubation projects, and organizational innovation resistance construct study.
Healthcare industries are facing an enormous flow of medical records due to the progression of information technology and the trend of digital transformation. Thus, medical information digitalization is a huge digital dataset that can be utilized to benefit healthcare systems and patients. While many studies focus on the application of the digitalized medical information in the healthcare field, only a few mentioned its resistance. The theoretical background depicts a comprehensive overview of medical information digitalization and the barriers in previous literature. This study emphasized the interaction of medical information digitalization barriers and applies the importance-resistance analysis model (IRA) to identify the resistant factors overcoming strategy. It also clarifies the pathway to eliminating the innovation resistance and reveals the interaction of medical information digitalization barriers. The acquisition, management, and application of medical information digitalization are the key foundation of medical technology innovation, digital transformation, and the application of artificial intelligence. This work can reduce the limitation of a narrow healthcare context. This study helps healthcare industries to clarify and solve barriers and realizes the innovation and application of medical information digitalization. In the long term, the results provide a basis for the future development direction of medical information digitalization and affect the medical industry.
Wei-Chih Lu; I-Ching Tsai; Kuan-Chung Wang; Te-Ai Tang; Kuan-Chen Li; Ya-Ci Ke; Peng-Ting Chen. Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization. Sustainability 2021, 13, 7888 .
AMA StyleWei-Chih Lu, I-Ching Tsai, Kuan-Chung Wang, Te-Ai Tang, Kuan-Chen Li, Ya-Ci Ke, Peng-Ting Chen. Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization. Sustainability. 2021; 13 (14):7888.
Chicago/Turabian StyleWei-Chih Lu; I-Ching Tsai; Kuan-Chung Wang; Te-Ai Tang; Kuan-Chen Li; Ya-Ci Ke; Peng-Ting Chen. 2021. "Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization." Sustainability 13, no. 14: 7888.
The medical technology (Med-Tech) industry turnover has reached a record high, attracting a great deal of capital investment, while mergers and acquisitions continually increase. In order to move to a higher value-added segment, traditional Med-Tech manufacturers have to transform into digital smart manufacturers. This development trend promotes industrial operators of Med-Tech to consider how to strengthen professional competence, expand their market, and determine the future direction. This study proposed the value-driving forces of Med-Tech enterprise, based on five aspects: Professional competence (PC), operation management (OM), critical resources (CR), regulatory system (RS), and market expansion (ME). Then, the acquisition and importance analysis (AIA) and the network relation map (NRM) approaches were proposed and implemented to find an optimal pathway for a Med-Tech enterprise to implement digital transformation. Our findings suggest that Med-Tech enterprises should treat RS as the priority in transformation. Finally, based on small- and medium-sized Med-Tech enterprise scenarios, we propose four development strategies (direct acquisition, strategic alliance, maintenance status, and in-house development) should be decided in the digital transformation process.
I-Ching Fang; Peng-Ting Chen; Hsin-Hui Chiu; Chia-Li Lin; Fong-Chin Su. Establishing the Digital Transformation Strategies for the Med-Tech Enterprises Based on the AIA-NRM Approach. Applied Sciences 2020, 10, 7574 .
AMA StyleI-Ching Fang, Peng-Ting Chen, Hsin-Hui Chiu, Chia-Li Lin, Fong-Chin Su. Establishing the Digital Transformation Strategies for the Med-Tech Enterprises Based on the AIA-NRM Approach. Applied Sciences. 2020; 10 (21):7574.
Chicago/Turabian StyleI-Ching Fang; Peng-Ting Chen; Hsin-Hui Chiu; Chia-Li Lin; Fong-Chin Su. 2020. "Establishing the Digital Transformation Strategies for the Med-Tech Enterprises Based on the AIA-NRM Approach." Applied Sciences 10, no. 21: 7574.
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, but also new investors from outside. This study focused on the entry strategy analysis of the Med-Tech industry, developing five driving factors, and conducting an opinion survey from three different aspects, including vendors, channels, and end-users, under COVID-19 impact. A total of 99 valid questionnaires were collected. After that, the Importance Accessibility Analysis-Network Relation Map (IAA-NRM) approach was used to verify the importance and implementation priority of the entry strategies. Then, the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique is used to construct the NRM method. The research results showed that there is a common strategic path, from the regulatory system to operation resources and then marketing promotion. In addition, in these three viewpoints, vendors and end-users have similar priorities in terms of industry attributes and barriers to entry.
I-Ching Fang; Peng-Ting Chen; Hsin-Hui Chiu; Chia-Li Lin; Fong-Chin Su. Med-Tech Industry Entry Strategy Analysis under COVID-19 Impact. Healthcare 2020, 8, 431 .
AMA StyleI-Ching Fang, Peng-Ting Chen, Hsin-Hui Chiu, Chia-Li Lin, Fong-Chin Su. Med-Tech Industry Entry Strategy Analysis under COVID-19 Impact. Healthcare. 2020; 8 (4):431.
Chicago/Turabian StyleI-Ching Fang; Peng-Ting Chen; Hsin-Hui Chiu; Chia-Li Lin; Fong-Chin Su. 2020. "Med-Tech Industry Entry Strategy Analysis under COVID-19 Impact." Healthcare 8, no. 4: 431.
This work presents a fall detection system that is worn on the head, where the acceleration and posture are stable such that everyday movement can be identified without disturbing the wearer. Falling movements are recognized by comparing the acceleration and orientation of a wearer’s head using prespecified thresholds. The proposed system consists of a triaxial accelerometer, gyroscope, and magnetometer; as such, a Madgwick’s filter is adopted to improve the accuracy of the estimation of orientation. Moreover, with its integrated Wi-Fi module, the proposed system can notify an emergency contact in a timely manner to provide help for the falling person. Based on experimental results concerning falling movements and activities of daily living, the proposed system achieved a sensitivity of 96.67% in fall detection, with a specificity of 98.27%, and, therefore, is suitable for detecting falling movements in daily life.
Chih-Lung Lin; Wen-Ching Chiu; Ting-Ching Chu; Yuan-Hao Ho; Fu-Hsing Chen; Chih-Cheng Hsu; Ping-Hsiao Hsieh; Chien-Hsu Chen; Chou-Ching K. Lin; Pi-Shan Sung; Peng-Ting Chen. Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements. Sensors 2020, 20, 5774 .
AMA StyleChih-Lung Lin, Wen-Ching Chiu, Ting-Ching Chu, Yuan-Hao Ho, Fu-Hsing Chen, Chih-Cheng Hsu, Ping-Hsiao Hsieh, Chien-Hsu Chen, Chou-Ching K. Lin, Pi-Shan Sung, Peng-Ting Chen. Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements. Sensors. 2020; 20 (20):5774.
Chicago/Turabian StyleChih-Lung Lin; Wen-Ching Chiu; Ting-Ching Chu; Yuan-Hao Ho; Fu-Hsing Chen; Chih-Cheng Hsu; Ping-Hsiao Hsieh; Chien-Hsu Chen; Chou-Ching K. Lin; Pi-Shan Sung; Peng-Ting Chen. 2020. "Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements." Sensors 20, no. 20: 5774.
The computerized healthcare information system has undergone tremendous advancements in the previous two decades. Medical institutions are paying further attention to the replacement of traditional approaches that can no longer handle the increasing amount of patient data. In recent years, the healthcare information system based on big data has been growing rapidly and is being adapted to medical information to derive important health trends and support timely preventive care. This research aims to evaluate organization-driven barriers in implementing a healthcare information system based on big data. It adopts the analytic network process approach to determine the aspect weight and applies VlseKriterijumska Optimizacija I Kzompromisno Resenje (VIKOR) to conclude a highly appropriate strategy for overcoming such barriers. The proposed model can provide hospital managers with forecasts and implications that facilitate the withdrawal of organizational barriers when adopting the healthcare information system based on big data into their healthcare service system. Results can provide benefits for increasing the effectiveness and quality of the healthcare information system based on big data in the healthcare industry. Therefore, by understanding the sequence of the importance of resistance factors, managers can formulate efficient strategies to solve problems with appropriate priorities.
Peng-Ting Chen; Chia-Li Lin; Wan-Ning Wu. Big data management in healthcare: Adoption challenges and implications. International Journal of Information Management 2020, 53, 102078 .
AMA StylePeng-Ting Chen, Chia-Li Lin, Wan-Ning Wu. Big data management in healthcare: Adoption challenges and implications. International Journal of Information Management. 2020; 53 ():102078.
Chicago/Turabian StylePeng-Ting Chen; Chia-Li Lin; Wan-Ning Wu. 2020. "Big data management in healthcare: Adoption challenges and implications." International Journal of Information Management 53, no. : 102078.
The development of information and communication technology has led to the rapid growth of medical data encountered by various players in healthcare industry. This evolution from a paper-based database to electronic records demonstrates the continuous advancement of medical information systems. Medical institutions are paying more attention to this issue and attempting to figure out the applications of big data. However, most of them have struggled to find pathways to apply big data adequately. Using hybrid methodologies and examining Taiwan's healthcare industry, this research aims to assess, forecast and summarize the major applications of medical big data, and establish strategic pathways for medical institutions to follow regarding different dimensions of applications. First, a review of literature related to the utility of medical big data and interviews with relevant stakeholders were conducted. Content analysis was subsequently done to extract the key applications, and DEMATEL was used to find out their Net Relation Map (NRM). With the Innovation Importance-Resistance Analysis (IRA), this study carried out IRA-NRM analysis to cultivate the strategy of medical big data development. This research concluded a IRA-NRM framework of 4 application categories and 16 factors. Suggestions for medical institutions regarding the use of medical big data are also provided.
Peng-Ting Chen. Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis. Technological Forecasting and Social Change 2018, 130, 150 -164.
AMA StylePeng-Ting Chen. Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis. Technological Forecasting and Social Change. 2018; 130 ():150-164.
Chicago/Turabian StylePeng-Ting Chen. 2018. "Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis." Technological Forecasting and Social Change 130, no. : 150-164.