healthcare industry
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2022 ◽  
Author(s):  
Sanjay Patil

Many healthcare organizations and facilities are currently attempting to improve either managerial or systematic operations. As a result, those businesses' performance has improved, as has their financial growth and reputation in local and global marketplaces. Deep learning and AI are utilized to control healthcare systems in this case. It aids in the provision of better service, the diagnosis of different diseases, and a variety of other tasks. Based on this, this paper will expound on the definitions of deep learning and AI, as well as the importance and change management applications of these tools.


2022 ◽  
Vol 40 (S1) ◽  
Author(s):  
SIJI OLIVER ◽  
C. L. JEBA MELVIN

COVID-19, one of the worst pandemics in recent years, have changed the face of our world. Every sector has been experiencing a tug in unexpected directions than anticipated. Often it is said that the healthcare sector is facing a boom in this COVID-19 episode, nevertheless there has been a decline of out-patient segment in hospitals. An out-patient is one who visits a hospital for treatment without staying overnight. Through this time of uncertainty where a new normal is being  burgeoned, the people’s attitude towards healthcare has shifted a great deal. Predominantly out-patients are hesitant to continue with their regular physician visits by delaying or avoiding unneeded visits. People with underlying diseases, both which are at low risk or at high risk, find themselves at higher caution due to the COVID-19. This study focuses to understand the attitude of out-patients and of out-patient’s with risk during this COVID-19 pandemic towards hospitals in India. Online or Tele medical consultation has picked up momentum among out-patients during the COVID-19 Unlock phase which shines a possibility as a new normal in the healthcare industry.


Author(s):  
Joseph R. Keebler ◽  
Michael A. Rosen ◽  
Dean F. Sittig ◽  
Eric Thomas ◽  
Eduardo Salas

This article reviews three industry demands that will impact the future of Human Factors and Ergonomics in Healthcare settings. These demands include the growing population of older adults, the increasing use of telemedicine, and a focus on patient-centered care. Following, we discuss a path forward through improved medical teams, error management, and safety testing of medical devices and tools. Future challenges are discussed.


2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Baobao Dong ◽  
Xiangming Wang ◽  
Qi Cao

With the development of wireless network, communication technology, cloud platform, and Internet of Things (IOT), new technologies are gradually applied to the smart healthcare industry. The COVID-19 outbreak has brought more attention to the development of the emerging industry of smart healthcare. However, the development of this industry is restricted by factors such as long construction cycle, large investment in the early stage, and lagging return, and the listed companies also face the problem of financing difficulties. In this study, machine learning algorithm is used to predict performance, which can not only deal with a large amount of data and characteristic variables but also analyse different types of variables and predict their classification, increasing the stability and accuracy of the model and helping to solve the problem of poor performance prediction in the past. After analysing the sample data from 53 listed companies in smart healthcare industry, we argued that the conclusion of this study can not only provide reference for listed companies in smart healthcare industry to formulate their own strategies but also provide shareholders with strategies to avoid risks and help the development of this emerging industry.


2022 ◽  
pp. 83-110
Author(s):  
Chaudhery Mustansar Hussain ◽  
Mosae Selvakumar Paulraj ◽  
Samiha Nuzhat

2022 ◽  
pp. 431-454
Author(s):  
Pinar Kirci

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.


2022 ◽  
pp. 80-124
Author(s):  
Kamalendu Pal

The supply chain forms the backbone of healthcare industry operations. The design and development of healthcare information systems (HIS) help different types of decision-making at various levels of business operations. Business process management decision-making is a complex task requiring real-time data collection from different operational sources. Hence, information technology (IT) infrastructure for data acquisition and sharing affects the operational effectiveness of the healthcare industry. The internet of things (IoT) applications have drawn significant research interest in the service of the healthcare industry. IoT technology aims to simplify the distributed data collection in healthcare practice, sharing, and processing of information and knowledge across many collaborating partners using suitable enterprise information systems. However, implementing blockchain technology in IoT-based data communication networks demands extra research initiatives. This chapter presents a review of security-related issues in the context of a HIS consisting of IoT-based blockchain technology.


2022 ◽  
pp. 197-213
Author(s):  
Bijoylaxmi Sarmah ◽  
Shampy Kamboj ◽  
Neeraj Kumar Phookan

Radio frequency identification (RFID) technology holds tremendous potential in improving the patient management system in hospitals attaining global importance in the healthcare industry due to the spread of the COVID-19 pandemic at present. RFID assists in wireless data storage and automatic retrieval, making systems efficient, improving patient safety, and decreasing costs. Although RFID is an emerging technology in the healthcare industry, its adoption is yet to gather momentum. This chapter will provide a background for healthcare practitioners and researchers about RFID technologies in the healthcare sector. Moreover, an integrated conceptual framework will be proposed consisting of factors that influence RFID technology adoption intention in the healthcare industry. This study will be the first of its kind to identify and classify various factors of RFID adoption intention and provide a comprehensive model using an exploratory method laying the foundation for academicians and industry practitioners for the future scope of its research.


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