scholarly journals Analysis technique of a wearable IoT health information on the MAPHIS

2018 ◽  
Vol 7 (3.3) ◽  
pp. 224
Author(s):  
Kwang Man Ko ◽  
Soon Gohn Kim
Keyword(s):  
Health Care ◽  
Big Data ◽  
Real Time ◽  
Health Management ◽  
Digital Health ◽  
Third Party ◽  
Smart Device ◽  
Calorie Intake ◽  
Management Service ◽  
Ubiquitous Environment

Background/Objectives: As u-health becomes common that monitors body condition real time in the ubiquitous environment, people are increasingly interested in promoting their health using biometric information identified by various health equipment.Methods/Statistical analysis: the concept of digital health has emerged that encompasses the followings: u-health that is expected to improve efficiency in medical service and monitor patients’ condition with wireless communication through convergence of ICT and health care industry; smart health(s-health) that manages their own workout, calorie intake and sporting activities with their smart device; and, mobile health (m-health) that uses wearable and mobile devices as a means of healthcare.Findings: In this study, we aim to develop a health care platform that receives diabetes information generated from various IoT based on remote inputs, stores, analyzes, processes and provides visualized information. The purpose of this study is to develop and test IoT-based diabetes health big-data platform for diabetes mellitus patients. To achieve this goal, we suggest the development result of service and contents oriented “An IoT-based diabetes health big-data Offloading platform” that comprehensively manages healthcare products created in many IoT-based diabetes information to build a personal health management system. We also developed android 4.x-based application so that the health management service and contents provided by a third party can be checked with the client PC as well as health management service and contents offered by web-based client application and third party can be operated in the mobile environment such as smartphone or tablet.Improvements/Applications: The results of this study are verified by applying it to patients with diabetes or suspected cases. In order to increase the efficiency of real-time processing, we used off-loading technology to utilize big data related to diabetes generated from wearable IoT device. The results of this study will be used for telemedicine in two hospitals in Malaysia after various laboratory verification procedures.  

Internet of Things in Healthcare as an Innovative Form of Personalized Medicine

2021 ◽  
pp. 1933-1943
Author(s):  
Ljubica Dikovic
Keyword(s):  
Health Care ◽  
Personalized Medicine ◽  
Internet Of Things ◽  
Real Time ◽  
Health Management ◽  
Digital Health ◽  
Future Application ◽  
The Future ◽  
Monitoring Treatment ◽  
Internet Revolution

Internet of things is a significant advancement in the big data era, which supports many real-time engineering applications through enhanced services. Generally, the next Internet revolution will be the interconnection between everyday existing objects in order to create a smart grid and intelligent environment. The future application of technology in health care will lead to the creation of an entirely new level of personalized, digital health care, where everyone is responsible for monitoring their own health and the quality of their own life. Further research is aimed at improving the existing sensors through increasing their capabilities and enhancing their efficiency. In the coming period, IoT is expected to play the key roles in all the aspects of modern medical treatment and health management - prevention, diagnosis, disease monitoring, treatment monitoring. This paper emphasizes the growing needs for better functioning of healthcare systems in real-time as well as the future development of personalized medicine.

scholarly journals Student Physical Health Information Management Model under Big Data Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Wang ◽  
Ning Wang ◽  
MeiJie Li ◽  
Simeng Mi ◽  
YaYa Shi
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Physical Health ◽  
Health Information ◽  
Information Management ◽  
Health Management ◽  
Health Information Management ◽  
Management Model ◽  
Student Health ◽  
Management Service

Health is considered an important foundation for students’ success. However, with the accelerated pace of life, rising pressure from various parties, weak health awareness, lack of exercise time, and other reasons, students’ physical quality is generally declining, the incidence of health diseases is increasing, and the onset age tends to be younger. With the development of the concept of “health first,” health management continues to expand and extend and students’ health management has attracted more attention from many aspects. Due to the late and low starting point of health management research and the lack of professional theoretical support, a complete, mature, and effective health management service system has not been established to deal with the students’ health. In order to make student health management more scientific, normative, and effective, this article has proposed big data technology to build the student health information management model. The first step of the approach is to store and analyze the data of students’ physical health. It is necessary to combine the data collection, supervision, data analysis, and data application of students’ physical health and gradually improve the national monitoring and evaluation system of students’ physical health. Student health check-up management platform is mainly used in realizing the school student information management and student health information relationship between system, science, standardization, and automation, and its main task is to use a computer to perform daily management of all previous medical information of students, such as query, modify, add, delete, and enhance the physical health of students information management ability given the large data analysis of useful information. In addition, we have built a doctor recommendation model based on online questions and answers to give specific health recommendations for students of different physiques.

Big Data Paradigm for Healthcare Sector

2016 ◽  
pp. 169-186
Author(s):  
Jyotsna Talreja Wassan
Keyword(s):  
Health Care ◽  
Big Data ◽  
Electronic Health Records ◽  
Real Time ◽  
Real Data ◽  
Relevant Information ◽  
Healthcare Sector ◽  
Clinical Environment ◽  
Time Dynamic ◽  
Health Care Records

The digitization of world in various areas including health care domain has brought up remarkable changes. Electronic Health Records (EHRs) have emerged for maintaining and analyzing health care real data online unlike traditional paper based system to accelerate clinical environment for providing better healthcare. These digitized health care records are form of Big Data, not because of the fact they are voluminous but also they are real time, dynamic, sporadic and heterogeneous in nature. It is desirable to extract relevant information from EHRs to facilitate various stakeholders of the clinical environment. The role, scope and impact of Big Data paradigm on health care is discussed in this chapter.

scholarly journals Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 506 ◽  
Author(s):  
Faisal Mehmood ◽  
Shabir Ahmad ◽  
DoHyeun Kim
Keyword(s):  
Big Data ◽  
Real Time ◽  
Recommendation System ◽  
Tourism Industry ◽  
Jeju Island ◽  
Third Party ◽  
User Preference ◽  
Optimal Route ◽  
Route Recommendation ◽  
Travel Route Recommendation

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

scholarly journals The future of health tourism in the industrial revolution 4.0 era

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Brian Kee Mun Wong ◽  
Sarah Alia Sa’aid Hazley
Keyword(s):  
Health Care ◽  
Real Time ◽  
Industrial Revolution ◽  
Digital Health ◽  
Current Trend ◽  
Tourism Industry ◽  
Healthcare Industry ◽  
Health Tourism ◽  
Content Type ◽  
The Future

Purpose The technological advances in the Industrial Revolution (IR) 4.0 era escalate the advancement of the healthcare industry, including the health tourism phenomenon. Based on the current trend in connected health care (e.g. mobile healthcare technology; digital health, etc.), this paper aims to propose that the distance between healthcare providers around the globe and its potential patients can be vastly reduced to almost on a real time basis. Design/methodology/approach A secondary literature review is conducted to identify the key development of IR 4.0 technologies in the healthcare industry and its possible trend leading the health tourism sector. Findings The adoption of IR 4.0 technologies is expected to make seeking treatments overseas more affordable, accessible and health records readily available on a real-time and secured basis. However, it is worth to note that the growth of health tourism raises the eyebrows of society from the security, social and economic perspectives. Originality/value This paper contributes to our understanding that the emergence of IR 4.0 technologies changes the landscape of the health care and health tourism industry. Continuous technology advancement is expected to further shape the future trend and escalate the commercialization aspect of the health tourism industry.

scholarly journals Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform (Preprint)

2018 ◽  
Author(s):  
Jacob McPadden ◽  
Thomas JS Durant ◽  
Dustin R Bunch ◽  
Andreas Coppi ◽  
Nathaniel Price ◽  
...  
Keyword(s):  
Health Care ◽  
Big Data ◽  
Precision Medicine ◽  
Open Source ◽  
Real Time ◽  
Data Science ◽  
Patient Monitoring ◽  
Medicine Research ◽  
Health Care Data ◽  
Precision Medicine Research

BACKGROUND Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. OBJECTIVE The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform. METHODS We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics. RESULTS Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases. CONCLUSIONS The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research.

scholarly journals Smart-devices in Human Behavior Manipula-tion: Process diagram with exploratory assessment

2021 ◽  
Vol 1 (3) ◽  
pp. 54-58
Author(s):  
Md. Mojibur Rahman Redoy Akanda ◽  
Md. Alamgir Hossain
Keyword(s):  
Human Behavior ◽  
Health Management ◽  
Human Life ◽  
Personal Data ◽  
Third Party ◽  
Smart Devices ◽  
Smart Device ◽  
Sensitive Data ◽  
Care Health ◽  
Using Data

Smart devices have become an essential part of human life with a bunch of modern features and facilities. Even in health care, health management, education, and the science sector use intelligent devices for their convenience. With the assertion of its wellness, people forget its downside and treating smart devices as their primary need. Whereas smart devices are tracking and collecting all user movements, including interest, boredom, and daily activity. As the data remain store in vendors' servers, and lightweight smart devices follow weak security, so data leakage also makes the data available to unauthorized parties. This sensitive data uses by vendors and  third-party for business and various purposes to influence and manipulate human behavior by showing content mapping to the collected data. Because of the huge involvement of the user in smart-device, marketing strategy also changed a lot. Digital marketing has been  introduced and become a key to success for many businesses where a particular content/advertisement can be mapped to particular leads. The next move of a user on the internet is shaping by applying numerous strategies based on previously collected data. In the era of smart devices, our personal life and personal data are not remaining personal anymore. This paper illustrates the systematic process of collecting and using data for manipulating human behavior. The raise of human behavior manipulation has been explained and an exploratory survey is imputed to strongly support the research statement.

Factors Influencing Procurement of Digital Health Care: A Case Study in Dutch District Nursing

2021 ◽  
Author(s):  
Sander Holterman ◽  
Marike Hettinga ◽  
Erik Buskens ◽  
Maarten Lahr
Keyword(s):  
Health Care ◽  
Health System ◽  
Task Force ◽  
Public Procurement ◽  
Digital Health ◽  
Third Party ◽  
Framework Analysis ◽  
Factors Influencing ◽  
Dutch Health

Background: Digital health is considered a promising solution in keeping health care accessible and affordable. However, implementation is often complex and sustainable funding schemes are lacking. Despite supporting policy, scaling up innovative forms of health care progresses much slower than intended in Dutch national framework agreements. The aim of this study is to identify factors that influence the procurement of digital health particular in district nursing. Methods: A case study approach was used, in which multiple stakeholder perspectives are compared using thematic framework analysis. The case studied was the procurement of digital health in Dutch district nursing. Literature on implementation of digital health, public procurement and payment models was used to build the analytic framework. We analysed fourteen interviews (secondary data), two focus groups organised by the national task force procurement and eight governmental and third-party reports. Results: Five themes emerged from the analysis: 1) rationale 2) provider-payer relationship, 3) resources, 4) evidence, and 5) the payment model. Per theme a number of factors were identified, mostly related to the design and functioning of the Dutch health system and to the implementation process at providers' side. Conclusions: This study identified factors influencing the procurement of digital health in Dutch district nursing. The findings, however, are not unique for digital health, district nursing or the Dutch health system. The results presented will support policy makers, and decision makers to improve procurement of digital health. Investing in better relationships between payer and care provider organisations and professionals is an important next step towards scaling digital health.

scholarly journals ICT-Based Health Care Services for Individuals with Spinal Cord Injuries: A Feasibility Study

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2491
Author(s):  
Wan-ho Jang ◽  
Seung-bok Lee ◽  
Dong-wan Kim ◽  
Yun-hwan Lee ◽  
Yun-jeong Uhm ◽  
...  
Keyword(s):  
Health Care ◽  
Spinal Cord ◽  
Medical Care ◽  
Health Care Services ◽  
Spinal Cord Injuries ◽  
Smart Device ◽  
Management Service ◽  
Care Services ◽  
Cord Injury ◽  
Tract Infections

In the Republic of Korea, 90.5% of those living with spinal cord injury (SCI) are faced with medical complications that require chronic care. Some of the more common ones include urinary tract infections, pressure sores, and pain symptomatology. These and other morbidities have been recognized to deteriorate the individual’s health, eventually restricting their community participation. Telerehabilitation, using information and communication technology, has propelled a modern-day movement in providing comprehensive medical services to patients who have difficulty in mobilizing themselves to medical care facilities. This study aims to verify the effectiveness of health care and management in the SCI population by providing ICT-based health care services. We visited eight individuals living with chronic SCI in the community, and provided ICT-based health management services. After using respiratory and urinary care devices with the provision of home visit occupational therapy, data acquisition was achieved and subsequently entered into a smart device. The entered information was readily accessible to the necessary clinicians and researchers. The clients were notified if there were any concerning results from the acquired data. Subsequently, they were advised to follow up with their providers for any immediate medical care requirements. Digital hand-bike ergometers and specialized seating system cushions are currently in development. The ICT-based health care management service for individuals with SCI resulted in a favorable expected level of outcome. Based on the results of this study, we have proposed and are now in preparation for a randomized clinical trial.

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