scholarly journals BIMBINGAN TEKNIS PERAMALAN JUMLAH KUNJUNGAN PASIEN DENGAN TABLEAU

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Mieke Nurmalasari ◽  
Witri Zuama Qomarania ◽  
Nauri Anggita Temesvari ◽  
Tria Saras Pertiwi
Keyword(s):  
Big Data ◽  
Information Management ◽  
Data Analytics ◽  
Digital Health ◽  
Rapid Development ◽  
Health Data ◽  
Patient Visit ◽  
Professional Health ◽  
Number Of Visits ◽  
Technical Guidance

ABSTRAK.  Peramalan jumlah kunjungan pasien berguna untuk membantu manajemen dalam membuat kebijakan dan perencanaan yang efektif dan efisien. Pesatnya perkembangan teknologi menjadikan data kesehatan digital sebagai salah satu sumber big data. Perlu dilakukan peningkatan pengetahuan pada mahasiswa dan tenaga Perekan Medis dan Manajemen Informasi Kesehatan dalam menganalisis data kunjungan pasien. Metode yang digunakan dalam kegiatan ini adalah pelatihan atau bimbingan teknis yang bersifat teoritis dan praktis. Hasil dari pelatihan ini adalah peningkatan pengetahuan peserta dalam menganalisis data peramalan kunjungan pasien menggunakan software statistik A Tableau. Kata kunci: kunjungan pasien; peramalan; analisis data; public tableau ABSTRACT. Forecasting number of visits is useful to help management to make effective and efficient policies and plans. The rapid development of technology makes digital health data as a one of big data sources. It is necessary to increase the knowledge of student and Professional Health Information Management in analyzing the patient visit data. The method used in this activity is a training or technical guidance which is namely theoretical and practical. The result of this training is an increase in participants' knowledge in analyzing the forecasting of patient visit data using a statistical software Tableau. Keywords: patient visit; forecasting; data analytics; public tableau

scholarly journals Patient information ownership in the age of digital health and big data

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
M Mirchev
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Analytics ◽  
Personal Information ◽  
Digital Health ◽  
Clinical Care ◽  
Personal Data ◽  
Health Data ◽  
Patient Data ◽  
Long Run

Abstract Background In the context of digital health and the increasing capabilities to derive, store and use information, Big data, and data analytics provide an exceptional perspective towards the evolution of medicine and public health. We collect patient data at unimaginable scale thanks to technological improvements such as wearables, sensors, smart and mobile devices. We are digitizing health on our way to improve cares. The other side of the coin reveals specific issues: it is all about personal information. The risks we face in regard to privacy, autonomy and ultimately justice are worth debating. Aim To consider whether ownership of patient data in the context of digital health and Big data is a good way to guarantee both privacy and the social interest in the field of public health. Methods Historical, documental, ethical research. Results The abilities to collect and store zettabytes of health-related information is spectacular, but learning how to structure and optimize the use of this information is pivotal for the future of public health. People are sensitive in terms of “ownership”, rights and privacy, although the idea for actual ownership of health information is not quite popular. Given the fact, that it is personal data, a lot of concerns are related to ensuring privacy. One way to do it is by recognizing patient ownership over their data. The major issue with this, is that it might limit, or even prevent public interest, and so the public benefits. Having in mind the huge commercial interest in health data, that concern looks relevant. When applied in healthcare Big data has the potential to provide important data analytics, which means that we can move to next step in healthcare development - improving disease prevention and health promotion, which are vastly ignored in favor of clinical care. In this specific environment, it is highly questionable whether patient`s ownership would bring more benefit, than harms in the shared goal of improving healthcare. Key messages What people might do if their health data is their property, might reflect in a bad way the common goal to structure and use it for health improving. Patient data ownership might not be reasonable in the long run, even though from an ethical standpoint and with regard to patient`s autonomy looks fair.

scholarly journals Light-Weight, Self-Powered Sensor Based on Triboelectric Nanogenerator for Big Data Analytics in Sports

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2322
Author(s):  
Xiaofei Ma ◽  
Xuan Liu ◽  
Xinxing Li ◽  
Yunfei Ma
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Mechanical Energy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Triboelectric Nanogenerator ◽  
Light Weight ◽  
Table Tennis ◽  
Self Powered

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

Intelligent health data analytics: A convergence of artificial intelligence and big data

2019 ◽  
Vol 32 (4) ◽  
pp. 178-182 ◽  
Author(s):  
Syed Sibte Raza Abidi ◽  
Samina Raza Abidi
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Health Outcomes ◽  
Health System ◽  
Data Analytics ◽  
Heterogeneous Data ◽  
Health Data ◽  
Care Quality ◽  
System Management ◽  
Health Data Analytics

Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.

scholarly journals Round table: AI and Big Data: Ethical Challenges and Health Opportunities

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Keyword(s):  
Big Data ◽  
Digital Health ◽  
Rapid Development ◽  
Open Data ◽  
Wearable Devices ◽  
Good Evidence ◽  
Electronic Patient Records ◽  
Ethical Challenges ◽  
Data Movement ◽  
Recent Emergence

Abstract The recent emergence of Big Data in healthcare (including large linked data from electronic patient records (EPR) as well as streams of real-time geolocated health data collected by personal wearable devices, etc.) and the open data movement enabling sharing datasets are creating new challenges around ownership of personal data whilst at the same time opening new research opportunities and drives for commercial exploitation. A balance must be struck between an individual’s desire for privacy and their desire for good evidence to drive healthcare, which may sometimes be in conflict. With the increasing use of mobile and wearable devices, new opportunities have been created for personalized health (tailored care to the needs of an individual), crowdsourcing, participatory surveillance, and movement of individuals pledging to become “data donors” and the “quantified self” initiative (where citizens share data through mobile device-connected technologies). These initiatives created large volumes of data with considerable potential for research through open data initiatives. In this workshop we will hear from a panel of international speakers working across the digital health, Big Data ethics, computer science, public health divide on how they have addressed the challenges presented by increased use of Big Data and AI systems in healthcare with insights drawn from their own experience to illustrate the new opportunities that development of these movements has opened up. Key messages The potential of open access to healthcare data, sharing Big Data sets and rapid development of AI technology, is enormous - so as are the challenges and barriers to achieve this goal. Policymakers, scientific and business communities should work together to find novel approaches for underlying challenges of a political and legal nature associated with use of big data for health.

scholarly journals Big Data on Machine Learning – A Review

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Balasree K ◽  
Dharmarajan K
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Big Data Technology

In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.

Leveraging Applications of Data Mining in Healthcare Using Big Data Analytics

2017 ◽  
pp. 345-359
Author(s):  
Mohammad Hossein Tekieh ◽  
Bijan Raahemi ◽  
Eric I. Benchimol
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Distributed Algorithms ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Data ◽  
Tree Structure ◽  
Massive Data ◽  
Potential Applications

Big data analytics has been introduced as a set of scalable, distributed algorithms optimized for analysis of massive data in parallel. There are many prospective applications of data mining in healthcare. In this chapter, the authors investigate whether health data exhibits characteristics of big data, and accordingly, whether big data analytics can leverage the data mining applications in healthcare. To answer this interesting question, potential applications are divided into four categories, and each category into sub-categories in a tree structure. The available types of health data are specified, with a discussion of the applicable dimensions of big data for each sub-category. The authors conclude that big data analytics can provide more advantages for the quality of analysis in particular categories of applications of data mining in healthcare, while having less efficacy for other categories.

scholarly journals The Policy Effect of the General Data Protection Regulation (GDPR) on the Digital Public Health Sector in the European Union: An Empirical Investigation

2019 ◽  
Vol 16 (6) ◽  
pp. 1070 ◽  
Author(s):  
Bocong Yuan ◽  
Jiannan Li
Keyword(s):  
European Union ◽  
Financial Performance ◽  
Data Protection ◽  
Digital Health ◽  
Rapid Development ◽  
Health Data ◽  
Personal Health ◽  
The European Union ◽  
General Data Protection Regulation ◽  
General Data

The rapid development of digital health poses a critical challenge to the personal health data protection of patients. The European Union General Data Protection Regulation (EU GDPR) works in this context; it was passed in April 2016 and came into force in May 2018 across the European Union. This study is the first attempt to test the effectiveness of this legal reform for personal health data protection. Using the difference-in-difference (DID) approach, this study empirically examines the policy influence of the GDPR on the financial performance of hospitals across the European Union. Results show that hospitals with the digital health service suffered from financial distress after the GDPR was published in 2016. This reveals that during the transition period (2016–2018), hospitals across the European Union indeed made costly adjustments to meet the requirements of personal health data protection introduced by this new regulation, and thus inevitably suffered a policy shock to their financial performance in the short term. The implementation of GDPR may have achieved preliminary success.

scholarly journals Data Analytics in Banking and Financial Services

2019 ◽  
Vol 8 (S1) ◽  
pp. 67-69
Author(s):  
S. Palaniammal ◽  
V. S. Thangamani
Keyword(s):  
Decision Making ◽  
Developing Countries ◽  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Data Science ◽  
Banking Industry ◽  
Rapid Development ◽  
Banking Services ◽  
The Impact

In Journal of Banking and Finance [1] we are living in the era of the big data. The rapid development of scientific and data technology over the past decade has brought not only new and sophisticated analytical tools into Financial and Banking services, but also introduced the power of data science application in everyday strategic and operational management. Data analytics and science developments have been particularly valuable to financial organizations that heavily depend on financial information in their decision making processes. The article presents the research that focuses on the impact of the data and technology trends on decision making, particularly in Finance and Banking services. It covers an overview of the benefits associated with the decision analytics and the use of big data by financial organizations. The aim of the research is to highlight the areas of impact where the big data trends are creating disruptive changes to the way the Finance and banking industry traditionally operates. For example, we can see rapid changes to organisation structures, approach to competition and customer as well as the recognition of the importance of data analytics in strategic and tactical decision making. Investment in data analytics is no longer considered a luxury, but necessity, especially for the financial organizations in developing countries. Technology and data science are both forcing and enabling the financial and banking industry to respond to transformative demands and adapt to rapidly changing market conditions in order to survive and thrive in highly competitive global environment. Financial companies operating in developing countries must develop strong understanding of data-related trends and impacts as well as opportunities. This knowledge should not only be utilized for survival efforts, but also seen as the opportunity to engage at global level through innovation, flexibility, and early adoption of data science benefits. The paper also recommends further studies in related areas, which would provide additional value and awareness to the organizations that are considering their participation in the global data and analytical trends.

scholarly journals Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review (Preprint)

2019 ◽  
Author(s):  
Leila Ismail ◽  
Huned Materwala ◽  
Achim P Karduck ◽  
Abdu Adem
Keyword(s):  
Big Data ◽  
Data Management ◽  
Real Time ◽  
Management System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems

BACKGROUND Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system. OBJECTIVE This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed. METHODS To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases. RESULTS Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care. CONCLUSIONS There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.

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