Health Care Analytics and Big Data Management in Influenza Vaccination Programs: Use of Information–Entropy Approach

2017 ◽  
pp. 211-238
Author(s):  
Sharon Hovav ◽  
Hanan Tell ◽  
Eugene Levner ◽  
Alexander Ptuskin ◽  
Avi Herbon
Keyword(s):  
Health Care ◽  
Big Data ◽  
Influenza Vaccination ◽  
Data Management ◽  
Information Entropy ◽  
Vaccination Programs ◽  
Health Care Analytics

A privacy protection method for health care big data management based on risk access control

2019 ◽  
Vol 23 (3) ◽  
pp. 427-442 ◽  
Author(s):  
Mingyue Shi ◽  
Rong Jiang ◽  
Xiaohan Hu ◽  
Jingwei Shang
Keyword(s):  
Health Care ◽  
Big Data ◽  
Access Control ◽  
Data Management ◽  
Privacy Protection ◽  
Protection Method

scholarly journals Big data management with machine learning inscribed by domain knowledge for health care

2017 ◽  
Vol 6 (4) ◽  
pp. 98 ◽  
Author(s):  
EPhzibah E.P. ◽  
Sujatha R
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Data Management ◽  
Domain Knowledge ◽  
Disease Diagnosis ◽  
Computing Methods ◽  
Support Vector ◽  
Vector Method ◽  
Medical Practitioners

In this work, a framework that helps in the disease diagnosis process with big-data management and machine learning using rule based, instance based, statistical, neural network and support vector method is given. Concerning this, big-data that contains the details of various diseases are collected, preprocessed and managed for classification. Diagnosis is a day-to-day activity for the medical practitioners and is also a decision-making task that requires domain knowledge and expertise in the specific field. This framework suggests different machine learning methods to aid the practitioner to diagnose disease based on the best classifier that is identified in the health care system. The framework has three main segments like big-data management, machine learning and input/output details of the patient. It has been already proved in the literature that the computing methods do help in disease diagnosis, provided the data about that particular disease is available in the data center. Thus this framework will provide a source of confidence and satisfaction to the doctors, as the model generated is based on the accuracy of the classifier compared to other classifiers.

Application of Big Data in Healthcare

2016 ◽  
pp. 149-168
Author(s):  
Md Rakibul Hoque ◽  
Yukun Bao
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Management ◽  
Healthcare Spending ◽  
Well Being ◽  
Healthcare Industry ◽  
The World ◽  
Hospital Systems ◽  
New Challenges

This chapter investigates the application, opportunities, challenges and techniques of Big Data in healthcare. The healthcare industry is one of the most important, largest, and fastest growing industries in the world. It has historically generated large amounts of data, “Big Data”, related to patient healthcare and well-being. Big Data can transform the healthcare industry by improving operational efficiencies, improve the quality of clinical trials, and optimize healthcare spending from patients to hospital systems. However, the health care sector lags far behind compared to other industries in leveraging their data assets to improve efficiencies and make more informed decisions. Big Data entails many new challenges regarding security, privacy, legal concerns, authenticity, complexity, accuracy, and consistency. While these challenges are complex, they are also addressable. The predominant ‘Big Data' Management technologies such as MapReduce, Hadoop, STORM, and others with similar combinations or extensions should be used for effective data management in healthcare industry.

scholarly journals Toward a Learning Health-care System – Knowledge Delivery at the Point of Care Empowered by Big Data and NLP

2016 ◽  
Vol 8s1 ◽  
pp. BII.S37977 ◽  
Author(s):  
Vinod C. Kaggal ◽  
Ravikumar Komandur Elayavilli ◽  
Saeed Mehrabi ◽  
Joshua J. Pankratz ◽  
Sunghwan Sohn ◽  
...  
Keyword(s):  
Health Care ◽  
Big Data ◽  
Real Time ◽  
Health Care System ◽  
Free Text ◽  
Individualized Care ◽  
Knowledge Delivery ◽  
Health Care Analytics ◽  
Learning Health Care System ◽  
Care System

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

Application of Big Data in Healthcare

Big Data ◽  
2016 ◽  
pp. 1189-1208 ◽  
Author(s):  
Md Rakibul Hoque ◽  
Yukun Bao
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Management ◽  
Healthcare Spending ◽  
Well Being ◽  
Healthcare Industry ◽  
The World ◽  
Hospital Systems ◽  
New Challenges

This chapter investigates the application, opportunities, challenges and techniques of Big Data in healthcare. The healthcare industry is one of the most important, largest, and fastest growing industries in the world. It has historically generated large amounts of data, “Big Data”, related to patient healthcare and well-being. Big Data can transform the healthcare industry by improving operational efficiencies, improve the quality of clinical trials, and optimize healthcare spending from patients to hospital systems. However, the health care sector lags far behind compared to other industries in leveraging their data assets to improve efficiencies and make more informed decisions. Big Data entails many new challenges regarding security, privacy, legal concerns, authenticity, complexity, accuracy, and consistency. While these challenges are complex, they are also addressable. The predominant ‘Big Data' Management technologies such as MapReduce, Hadoop, STORM, and others with similar combinations or extensions should be used for effective data management in healthcare industry.

scholarly journals Quality of Big Data in health care

2015 ◽  
Vol 28 (6) ◽  
pp. 621-634 ◽  
Author(s):  
Sreenivas R. Sukumar ◽  
Ramachandran Natarajan ◽  
Regina K. Ferrell
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Quality ◽  
Health Data ◽  
Data Sets ◽  
Processing Technologies ◽  
Content Type ◽  
Road Map ◽  
Quality Issues ◽  
Health Care Analytics

Purpose – The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics. Design/methodology/approach – The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines. Findings – Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance. Practical implications – The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines. Originality/value – Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

Health Informatics: Handle with Caution

1997 ◽  
Vol 36 (02) ◽  
pp. 79-81
Author(s):  
V. Leroy ◽  
S. Maurice-Tison ◽  
B. Le Blanc ◽  
R. Salamon
Keyword(s):  
Health Care ◽  
Data Management ◽  
Medical Informatics ◽  
Health Care Professionals ◽  
Data Security ◽  
Health Informatics ◽  
The Other ◽  
Inappropriate Use

Abstract:The increased use of computers is a response to the considerable growth in information in all fields of activities. Related to this, in the field of medicine a new component appeared about 40 years ago: Medical Informatics. Its goals are to assist health care professionals in the choice of data to manage and in the choice of applications of such data. These possibilities for data management must be well understood and, related to this, two major dangers must be emphasized. One concerns data security, and the other concerns the processing of these data. This paper discusses these items and warns of the inappropriate use of medical informatics.

Factors affecting influenza vaccine uptake among health care workers in the Medical and Health Sciences Center of Debrecen University, Hungary

2012 ◽  
Vol 153 (13) ◽  
pp. 505-513 ◽  
Author(s):  
Piroska Orosi ◽  
Ágnes Borbély ◽  
Judit Szidor ◽  
János Sándor
Keyword(s):  
Health Care ◽  
Influenza Vaccination ◽  
Health Care Workers ◽  
Vaccination Rate ◽  
Internal Communication ◽  
Health Science ◽  
Vaccine Uptake ◽  
Science Center ◽  
Family Doctors ◽  
Care Workers

Influenza vaccination is the most effective way of influenza prevention. The vaccination rate is low worldwide. In Hungary, the vaccine is free of charge to health care workers and, therefore, the low vaccination rate is unaccountable. Aims: In this study, the authors wanted to explore those factors which influence the refusal of vaccination. Methods: The Health Science Center of Debrecen University has about 4000 employees. The authors adjusted a questionnaire with 45 questions and sent it to 525 randomly selected health care workers, 294 of whom responded (response rate, 56%). The Epiinfo software was used for statistical evaluation. Results: The respondents strongly agreed that the vaccine is free and easy to obtain at the workplace. Official recommendations of the occupational health, the Medical Association of Hungary and advice of the family doctors failed to influence the decision. However, a significant impact of communication with family members, friends and colleagues on the decision was documented. Conclusions: The results indicate that the most important tool in decision making of influenza vaccination is the internal communication, but this effect is not a permanent one. International data show highly variable vaccination rates (between 2.1% and 82%). A better vaccination rate (98% or above) may be achieved with a mandatory influenza vaccination program among health care workers. Orv. Hetil., 2012, 153, 505–513.

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