Big Data Analytics for Intelligent Healthcare Management

2019 ◽  
2021 ◽  
Vol 23 (06) ◽  
pp. 1167-1182
Author(s):  
Shreyas Nopany ◽  
◽  
Prof. Manonmani S ◽  

The healthcare industry has become increasingly demanding in recent years. The growing number of patients makes it difficult for doctors and staff to manage their work effectively. In order to achieve their objectives, data analysts collect a large amount of data, analyze it, and use it to derive valuable insights. Data analytics may become a promising solution as healthcare industry demands increase. The paper discusses the challenges of data analytics in the healthcare sector and the benefits of using big data for healthcare analytics. Aside from focusing on the opportunities that big data analytics has in the healthcare sector, the paper will also discuss data governance, strategy formulation, and improvements to IT infrastructure. Implementation techniques include Hadoop, HDFS, MapReduce, and Apache in Big Data Analytics. A Healthcare Management System can be categorized into five divisions, namely, Drug discovery, Disease prevention, diagnosis and treatment, Hospital operations, post-care, requiring comprehensive data management. Big Data analysis support transformation is identified as a required component in future research for the application of Big Data in HealthCare.


2018 ◽  
Vol 12 (3) ◽  
pp. 226-240 ◽  
Author(s):  
Sachin S. Kamble ◽  
Angappa Gunasekaran ◽  
Milind Goswami ◽  
Jaswant Manda

2020 ◽  
Vol 1 ◽  
pp. 66-70
Author(s):  
Nikoleta Leventi ◽  
Alexandrina Vodenitcharova ◽  
Kristina Popova

A clinical trial, according to the WHO, “is any research study that prospectively assigns human participants or groups of humans to one or more health-related interventions to evaluate the effects on health outcomes. Interventions include but are not restricted to drugs, cells and other biological products, surgical procedures, radiological procedures, devices, behavioural treatments, process-of-care changes, preventive care, etc”. The application of innovative information technologies like artificial intelligence and big data analytics in clinical trial processes is a new challenge. Such systems are useful tools, and promise to enhance the healthcare management, and to optimize clinical outcomes and economic effectiveness. However, their use raises ethical and social issues. In this direction, the European Commission in June 2018 set up the High Level Expert Group on AI, which offers guidance on a comprehensive framework for trustworthy AI. Trustworthy AI consists of three components, which should be met during the entire life cycle of the system: (1) it should be lawful, (2) it should be ethical, and (3) it should be robust. In this article we used the focus group methodology to obtain information from experts about the ethical aspects raised when innovative information technologies like artificial intelligence and big data analytics are used in clinical trials. Feedback from the experts was also gathered regarding the usage of the proposed guidelines for trustworthy AI, as an evaluation tool for the particular case of clinical trials.


2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.


2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma

2017 ◽  
Vol 49 (004) ◽  
pp. 825--830
Author(s):  
A. AHMED ◽  
R.U. AMIN ◽  
M. R. ANJUM ◽  
I. ULLAH ◽  
I. S. BAJWA

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