An analytical hierarchical process evaluation on parameters Apps-based Data Analytics for healthcare services

2021 ◽  
pp. 215-239
Author(s):  
Monika Arora ◽  
Radhika Adholeya ◽  
Swati Sharan
Keyword(s):  
Process Evaluation ◽  
Data Analytics ◽  
Healthcare Services ◽  
Analytical Hierarchical Process

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

Insight Into Big Data Analytics

2018 ◽  
pp. 67-79
Author(s):  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Imran
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Huge Amount ◽  
Business Decision ◽  
Digital Era ◽  
Data Formats ◽  
Recent Trends ◽  
Analytical Approaches

In the present digital era, more data are generated and collected than ever before. But, this huge amount of data is of no use until it is converted into some useful information. This huge amount of data, coming from a number of sources in various data formats and having more complexity, is called big data. To convert the big data into meaningful information, the authors use different analytical approaches. Information extracted, after applying big data analytics methods over big data, can be used in business decision making, fraud detection, healthcare services, education sector, machine learning, extreme personalization, etc. This chapter presents the basics of big data and big data analytics. Big data analysts face many challenges in storing, managing, and analyzing big data. This chapter provides details of challenges in all mentioned dimensions. Furthermore, recent trends of big data analytics and future directions for big data researchers are also described.

scholarly journals Proposed Usage of ICT and Big Data in Healthcare of Madhya Pradesh

2021 ◽  
Vol 9 (12) ◽  
pp. 2242-2248
Author(s):  
Mimoh Ojha
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Madhya Pradesh ◽  
Information And Communications Technology ◽  
Area Network ◽  
Wide Area Network ◽  
Healthcare Data ◽  
Health Application

Abstract: This paper gives an insight of how information and communications technology (ICT) in combination with big data analytics can help to improve healthcare services in Madhya Pradesh, which is a state in India having approximately 75 million populations. With ongoing projects like ‘Digital India’ which will allow computerization of hospitals and digitization of healthcare data. Digital India coupled with ICT, can play an indispensable role in providing effective healthcare services through e-health application like electronic health record, e-prescription, computerized physician order entry, telemedicine, mhealth along with the network like State wide area network (SWAN) and National health information network which will allow sharing of healthcare records across the network. Data stored through e-health application is of huge size having different formats which makes it difficult to perform analytics on it. But with big data analytics we can perform analytics on large voluminous healthcare data and useful result obtained from data analytics, patients can be given better and specific treatments. It will also help doctors to exchange their knowledge and treatment practices. This paper also illustrates a case study on M.Y. hospital located in Indore, Madhya Pradesh. Keywords: ICT, E-health, Digital India, SWAN, CUG, Big Data Analytics.

PP164 Improving Medical Diagnosis Through Advanced Data Analytics Tools

2019 ◽  
Vol 35 (S1) ◽  
pp. 67-68
Author(s):  
Imanol González-Barcina ◽  
Aitor García de Vicuña-Meléndez ◽  
Ana Santorcuato ◽  
Ivan Revuelta-Antizar ◽  
Santiago Rodríguez-Tejedor ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Language Processing ◽  
Clinical Data ◽  
Clinical Management ◽  
Data Analytics ◽  
Clinical Decision Making ◽  
Medical Information ◽  
Emergency Services ◽  
Healthcare Services ◽  
Added Value

IntroductionCurrent clinical practice is based on guidelines and local protocols that are informed by clinical evidence. This means that clinical variability is reduced, but can lead to inefficient clinical decision-making, and can increase medical errors, decreasing patient's safety. The aim of the EXCON project is to investigate the innovative concept of Intelligent Clinical History (ICH), and to develop functional prototypes of high added-value in healthcare services.MethodsThe innovative EXCON project will take advantage of recent advances in technologies for coding, structuring and semantizing medical information. Thanks to this new structuring, the EXCON platform will be developed. Final users will be health professionals and other decision-makers. Doctors, nurses, epidemiologists and information specialists will be involved in the development and subsequent validation of the platforms.ResultsTo develop the ICH platform clinical data on a highly prevalent symptom with high variability in clinical practice, such as non-traumatic chest pain in emergency services, has been collected from different electronic medical record databases. The extraction of clinical data to implement new techniques of artificial intelligence requires tasks that must be automated, which today is difficult and tedious (data is often not computerized). Through techniques applied in EXCON, such as natural language processing, relevant clinical data have been extracted and a Decision Support System has been developed and validated. This tool optimizes resources and improves clinical management, reducing errors and increasing patient's safety.ConclusionsIn coming decades, patient management will be impacted by the application of new advanced data analytics tools. This will allow for safer and more efficient clinical management, decrease variability in clinical practice, and improve equity. That is why the development and assessment of these technologies is necessary.

scholarly journals Industry 4.0 Applications for Medical/Healthcare Services

2021 ◽  
Vol 10 (3) ◽  
pp. 43
Author(s):  
Shuva Paul ◽  
Muhtasim Riffat ◽  
Abrar Yasir ◽  
Mir Nusrat Mahim ◽  
Bushra Yasmin Sharnali ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Data Analytics ◽  
Industrial Revolution ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Start Ups ◽  
Medical Healthcare

At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm.

scholarly journals BIG DATA ANALYTICS IN HEALTHCARE SERVICES

2019 ◽  
Vol 1 (2) ◽  
pp. 22-24
Author(s):  
GUNASEKAR THANGARASU ◽  
KAYALVIZHI SUBRAMANIAN
Keyword(s):  
Big Data ◽  
Medical Research ◽  
Patient Care ◽  
Clinical Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Effective Practices ◽  
Novel Approach

This study addresses the healthcare services problems which focus on the upcoming and promising areas of medical research and proposed a novel approach integrating in big data analytics and Apache. The proposed approach will improve the healthcare services fastly and efficiently. The big data analytics can continually evaluate clinical data in order to improve the effective practices of physicians and improved patient care

Big Data, Data Mining, and Data Analytics in IoT-Based Healthcare Applications

2022 ◽  
pp. 1035-1053
Author(s):  
Isakki Alias Devi P
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Health Management ◽  
Health Resources ◽  
Healthcare Services ◽  
Data Systems ◽  
Healthcare Applications ◽  
Big Data Systems ◽  
Iot Devices

IoT seriously impacts every industry. The healthcare industry has experienced progression in digitizing medical records. Healthcare services are costlier than ever. Data mining is one of the largest challenges to face IoT. Big Data is an accumulation of data. IoT devices receive lots of data. Big data systems can do a lot of data analytics. The tools can also be used to perform these operations. The big health application system can be built by integrating medical health resources using intelligent terminals, internet of things (IoT), big data, and cloud computing. People suffer from many diseases. A big health system can be applied to scientific health management by detecting risk factors for the occurrence of diseases. Patients can have special attention to their health requirements and their devices can be tuned to remind them of their appointments, calorie count, exercise check, blood pressure variations, symptoms of any diseases, and so much more.

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