Study of E-business applications based on big data analysis in modern hospital health management

Li, Xiaohong; Zhang, Yanling; Li, Yujuan; Yu, Ke; Du, Yihua

doi:10.1007/s10257-021-00520-9

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…T [25] represents the set of tags used by u i , and Freqð u i , t j Þ represents the number of times the user u i has used the tag t j . (6) Weights between resource and tag nodes. If t j labels resource r i , a directed edge exists from r i to t j with the following weights:…”

Section: Advances In Multimediamentioning

confidence: 99%

“…In addition, to deal with massive commercial data information, extracting useful data is very important for the production management of enterprise managers [4,5]. However, traditional data management and data mining technologies are unable to store and analyze large amounts of data efficiently [6,7]. Therefore, although companies have valuable data resources, they cannot be effectively utilized them [8,9].…”

Section: Introductionmentioning

confidence: 99%

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The Role of Cognitive Science and Big Data Technology in the Design of Business Information Management Systems

2022

Advances in Multimedia

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With the surge in the amount of data in the internal and external environment, the collection, analysis, processing, and storage of the increasing data sources and data volume, as well as the problems of big data management, are the current situation and dilemmas of data management faced by enterprises today. Cognitive science and big data technology can provide good auxiliary support for enterprise management decision-making. This study takes the business information management of cold chain logistics enterprises as an example, aiming at the characteristics of business intelligence data in real applications, based on cognitive science and big data technology, from low-cost and high-performance storage, security management, and big data analysis. This paper is mainly through the research of big data processing theory and key technologies. Based on analyzing the logistics industry’s data access rules and characteristics, this study proposes a hot data prediction model for multilayer hybrid storage systems. It is verified that the prediction model has good accuracy, robustness, and universality. For the application scenario of multitenant distributed data access, a data transparent security management model is proposed. Simulation experiments show that this method can realize data security management when the performance loss is controlled within an acceptable range. Based on the real-time computing technology of massive data, the label optimization scheme of collaborative filtering and reinforcement learning is used to realize the logistics distribution recommendation model and to solve the accuracy and real-time problems of logistics service distribution analysis.

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Section: Advances In Multimediamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role of Cognitive Science and Big Data Technology in the Design of Business Information Management Systems

2022

Advances in Multimedia

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“…The current hospital information data statistics are mainly completed by using the hospital's own office automation system and manual statistics [3]. Hospital information data is the basis for the hospital's human resources and financial management [4].…”

Section: Hospital Information Data Statisticsmentioning

confidence: 99%

Research on hospital information data statistics under the background of big data and artificial intelligence

Chen,

Chai

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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With the continuous increase of online social media data, data has become a powerful assistant for the competition and development of various industries, and various data statistics and processing analysis tools in big data technology have also developed into necessary tools for enterprises. Artificial intelligence technology enables electronic devices to have intelligent thinking ability after being trained by data sets, and is widely used in various industries that require complex computing processes. The hospital is an inseparable place for every citizen, and the amount of data it generates is extremely huge. Therefore, it will be extremely difficult for the staff to manually count the data in the hospital system. Applying big data and artificial intelligence technology to hospital information data statistics will reduce the complicated manual statistical process and reduce labor costs. This paper firstly conducts a basic analysis of hospital information data statistics requirements, big data and artificial intelligence technology, and then in order to investigate the current hospital information data statistics, through the questionnaire star online platform, the information data statistics staff of 30 hospitals have been surveyed. After the interview, through the analysis of the questionnaire data, it was found that there are problems in the current hospital information data statistics that the quality of data statistics is poor, the data statistics take a long time and the process is complicated. Finally, based on big data and artificial intelligence technology, the pertinence of the hospital information data statistics problem is designed. Solution: After a second visit to the research object, it was found that the statistical efficiency, cost and accuracy of hospital information using related strategies were greatly improved.

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“…Extant literature has studied ‘Data Science Strategy’ in the contexts of dynamic market places, organizational and dynamic capabilities (Knabke & Olbrich, 2018 ), innovations (Mikalef et al 2018 , 2019a ), and new product development processes (Johnson et al 2017 ). Studies have been conducted in the context of but not limited to healthcare (Chen and Banerjee 2020 ; Kamble et al 2019 ; Kemppainen et al 2019 ; Li et al 2021 ; Newlands et al 2020 ; Ramnath et al 2020 ; Yang et al 2015 ; Wang and Hajli 2017 ), B2B (Hallikainen et al 2020 ), construction (Ahmed et al 2018 ; Ram et al 2019 ; Sang et al 2020 ), supply chain management (Ali et al 2020 ; Arunachalam et al 2018 ; Brinch et al 2018 ; Dubey et al 2019a ; Khan 2019 ; Lai et al 2018 ; Lamba and Singh 2018 ; Mandal 2019 ; Singh and Singh 2019 ; Wang et al 2018c ), manufacturing (Popovič et al 2018 ; Verma 2017 ), consumer goods (Rialti et al 2018 ); e-commerce (Behl et al 2019 ; Wamba et al 2017 ), telecommunications (Saldžiūnas and Skyrius 2017 ; Walker and Brown 2019 ), banking and financial services (Lee et al 2017 ; Lautenbach et al 2017 ; Gregory 2011 ), automotive (Dremel et al 2017 ), and airlines (Holland et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

Reddy

Bhattacharjee

Mishra

et al. 2022

Inf Syst E-Bus Manage

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While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects. Supplementary Information The online version contains supplementary material available at 10.1007/s10257-022-00550-x.

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Study of E-business applications based on big data analysis in modern hospital health management

Cited by 4 publications

References 15 publications

The Role of Cognitive Science and Big Data Technology in the Design of Business Information Management Systems

The Role of Cognitive Science and Big Data Technology in the Design of Business Information Management Systems

Research on hospital information data statistics under the background of big data and artificial intelligence

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

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