TY - JOUR
T1 - Big data management in healthcare
T2 - Adoption challenges and implications
AU - Chen, Peng Ting
AU - Lin, Chia Li
AU - Wu, Wan Ning
N1 - Funding Information:
This research was made possible by the support and assistance of a number of people whom I would like to thank. I am very grateful to all the respondents for their valuable opinions. I would like to thank my research assistant Ms. Ya-Ci Ke, Mr. Kuan-Chen Li, Mr. Nguyuen Quoc Duy, Mr. Wei-Zhi Lu, Mr. Kuan-Chung Wang and Miss I-Ching Tsai’s help in literatures and questionnaires collection and organization. This research was supported by the Ministry of Technology and Science under grant number MOST 105-2221-E-006 -260-MY3 and MOST 108-2221-E-006-063 and the Medical Device Innovation Center (MDIC) , National Cheng Kung University(NCKU) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MoE) in Taiwan .
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
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U2 - 10.1016/j.ijinfomgt.2020.102078
DO - 10.1016/j.ijinfomgt.2020.102078
M3 - Article
AN - SCOPUS:85078793271
SN - 0268-4012
VL - 53
JO - International Journal of Information Management
JF - International Journal of Information Management
M1 - 102078
ER -