Healthcare industry has been a significant area for innovative application of various technologies over decades. Being an area of social relevance governmental spending on healthcare have always been on the rise over the years. Event Processing (CEP) has been in use for many years for situational awareness and response generation. Computing technologies have played an important role in improvising several aspects of healthcare. Recently emergent technology paradigms of Big Data, Internet of Things (IoT) and Complex Event Processing (CEP) have the potential not only to deal with pain areas of healthcare domain but also to redefine healthcare offerings. This paper aims to lay the groundwork for a healthcare system which builds upon integration of Big Data, CEP and IoT.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
Advances in information and communication technologies have led to the emergence of Internet of Things
(IoT). In the modern health care environment, the usage of IoT technologies brings convenience to physicians and
patients since they are applied to various medical areas (such as real-time monitoring, patient information and healthcare
management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in
healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor
nodes
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
Advances in information and communication technologies have led to the emergence of Internet of Things
(IoT). In the modern health care environment, the usage of IoT technologies brings convenience to physicians and
patients since they are applied to various medical areas (such as real-time monitoring, patient information and healthcare
management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in
healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor
nodes
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Welcome to the age of cognitive computing: where intelligent machines have
moved from the realms of science fiction to the present day. This groundbreaking
technology is driving advanced discoveries and allowing improved decision-making –
resulting in better patient care
IT can do a revolution in many industries like health. Ksoft Technologies, cherppulassery does this through websites, mobile apps and computer software.
You can have a quick look into it's website and portfolio for more opportunities and needs - www.ksofttechnologies.com
Let's make it work !
Introduction to Health Informatics and Health Information Technology (Part 1)...Nawanan Theera-Ampornpunt
Presented at the Health Informatics and Health Information Technology Course, Doctor of Philosophy and Master of Science Programs in Data Science for Health Care (International Program), Faculty of Medicine Ramathibodi Hospital, Mahidol University on October 3, 2017
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
Standardization and wider use of Electronic Health records (EHR) creates opportunities for
better understanding patterns of illness and care within and across medical systems. In the healthcare
systems, hidden event signatures allow taking decision for patient’s diagnosis, prognosis, and
management. Temporal history of event codes embedded in patients' records, investigates frequently
occurring sequences of event codes across patients. There is a framework that enables the
representation, retrieval, and mining of high order latent event structure and relationships within
single and multiple event sequences. There is a wealth of hidden information present in the large
databases. Different data mining techniques can be used for retrieving data. A classifier approach for
detection of diabetes is presented in this paper and shows how Naive Bayes can be used for
classification purpose. In this system, medical data is categories into five categories namely low,
average, high and very high and critical, treatment is given as per the predicted category. The system
will predict the class label of unknown sample. Hence two basic functions namely classification
(training) and prediction (testing) will be performed. An algorithm and database used affects the
accuracy of the system. It can answer complex queries for diagnosing diabetes disease and thus assist
healthcare practitioners to make intelligent clinical decisions which traditional decision support
systems cannot.Over the last decade, so many information visualization techniques have been
developed to support the exploration of large data sets. There are various interactive visual data
mining tools available for visual data analysis. It is possible to perform clinical assessment for visual
interactive knowledge discovery in large electronic health record databases. In this paper, we
proposed that it is possible to develop a tool for data visualization for interactive knowledge
discovery.
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 7, 2020
Presented at the Navamindradhiraj University National Conference 2018 "Networking in the Smart City : Collaboration of Smart Health and Smart Community" on July 13, 2018
E-Symptom Analysis System to Improve Medical Diagnosis and Treatment Recommen...journal ijrtem
: A wealth of data in public health care systems has been collected and meanwhile there are plenty
of new technological improvements which have considerable influence on current data pool. Nevertheless,
important obstacles are challenging to utilize existing clinical data. Enhanced technological improvements lead
patients to search their symptoms and corresponding diagnosis on online resources. In this study, it is aimed to
develop a machine learning model to suit in different availability of users. Most of the current systems allow
people to choose related symptom in web interfaces or Q&A forums. In addition to these applications it is aimed
to implement a new technique which extracts the text-based symptoms and its related parameters such as, severity,
duration, location, cause, accompanied by any other indicators. This study is applicable for patient`s everyday
language statements besides medical expression of symptoms for corresponding symptoms. Extracted terms are
used as an input of the model and analyzed for matching diagnosis where an accuracy of 72.5% has been
accomplished.
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
Startupfest 2015: DR. JONATHAN KANEVSKY - "Future of" stageStartupfest
The future of medicine
The future of medicine is bright. In the era of big data, nanotechnology, and advanced robotics, medicine is becoming more advanced than ever. Dr. Kanevsky will outline the most interesting innovations in medicine and surgery that are changing the face of healthcare.
The Future Outlook of Artificial Intelligence in Cancer Prevention, Diagnosis...IJMCERJournal
ABSTRACT: Using Artificial Intelligence technics to prevent, diagnose and treat cancer is a promising
are(Liao, Ding, Jiang, Wang, Zhang & Zhang, 2018). As a matter of fact, the earlier a cancer is diagnosed, the
better the chances of survival. Or, if diagnosed in the late stages of the disease, the faster the patient is treated
with the right treatment, the better the chances of survival. Recently, the availability of a large and diverse
amount of data has made it possible to use intelligent systems to get more precise results for patients. Depending
on the type of cancer, facing metastasis is always a challenge for physicians, scientists and patients (La Porta &
Zapperi,2018). This paper is to illustratehow artificial intelligence algorithms have been improving and boosting
the research to save lives.
KEYWORDS: artificial intelligence, cancer, machine learning, artificial neural networks, prevention, diagnosis,
treatmen
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Welcome to the age of cognitive computing: where intelligent machines have
moved from the realms of science fiction to the present day. This groundbreaking
technology is driving advanced discoveries and allowing improved decision-making –
resulting in better patient care
IT can do a revolution in many industries like health. Ksoft Technologies, cherppulassery does this through websites, mobile apps and computer software.
You can have a quick look into it's website and portfolio for more opportunities and needs - www.ksofttechnologies.com
Let's make it work !
Introduction to Health Informatics and Health Information Technology (Part 1)...Nawanan Theera-Ampornpunt
Presented at the Health Informatics and Health Information Technology Course, Doctor of Philosophy and Master of Science Programs in Data Science for Health Care (International Program), Faculty of Medicine Ramathibodi Hospital, Mahidol University on October 3, 2017
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
Standardization and wider use of Electronic Health records (EHR) creates opportunities for
better understanding patterns of illness and care within and across medical systems. In the healthcare
systems, hidden event signatures allow taking decision for patient’s diagnosis, prognosis, and
management. Temporal history of event codes embedded in patients' records, investigates frequently
occurring sequences of event codes across patients. There is a framework that enables the
representation, retrieval, and mining of high order latent event structure and relationships within
single and multiple event sequences. There is a wealth of hidden information present in the large
databases. Different data mining techniques can be used for retrieving data. A classifier approach for
detection of diabetes is presented in this paper and shows how Naive Bayes can be used for
classification purpose. In this system, medical data is categories into five categories namely low,
average, high and very high and critical, treatment is given as per the predicted category. The system
will predict the class label of unknown sample. Hence two basic functions namely classification
(training) and prediction (testing) will be performed. An algorithm and database used affects the
accuracy of the system. It can answer complex queries for diagnosing diabetes disease and thus assist
healthcare practitioners to make intelligent clinical decisions which traditional decision support
systems cannot.Over the last decade, so many information visualization techniques have been
developed to support the exploration of large data sets. There are various interactive visual data
mining tools available for visual data analysis. It is possible to perform clinical assessment for visual
interactive knowledge discovery in large electronic health record databases. In this paper, we
proposed that it is possible to develop a tool for data visualization for interactive knowledge
discovery.
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 7, 2020
Presented at the Navamindradhiraj University National Conference 2018 "Networking in the Smart City : Collaboration of Smart Health and Smart Community" on July 13, 2018
E-Symptom Analysis System to Improve Medical Diagnosis and Treatment Recommen...journal ijrtem
: A wealth of data in public health care systems has been collected and meanwhile there are plenty
of new technological improvements which have considerable influence on current data pool. Nevertheless,
important obstacles are challenging to utilize existing clinical data. Enhanced technological improvements lead
patients to search their symptoms and corresponding diagnosis on online resources. In this study, it is aimed to
develop a machine learning model to suit in different availability of users. Most of the current systems allow
people to choose related symptom in web interfaces or Q&A forums. In addition to these applications it is aimed
to implement a new technique which extracts the text-based symptoms and its related parameters such as, severity,
duration, location, cause, accompanied by any other indicators. This study is applicable for patient`s everyday
language statements besides medical expression of symptoms for corresponding symptoms. Extracted terms are
used as an input of the model and analyzed for matching diagnosis where an accuracy of 72.5% has been
accomplished.
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
Startupfest 2015: DR. JONATHAN KANEVSKY - "Future of" stageStartupfest
The future of medicine
The future of medicine is bright. In the era of big data, nanotechnology, and advanced robotics, medicine is becoming more advanced than ever. Dr. Kanevsky will outline the most interesting innovations in medicine and surgery that are changing the face of healthcare.
The Future Outlook of Artificial Intelligence in Cancer Prevention, Diagnosis...IJMCERJournal
ABSTRACT: Using Artificial Intelligence technics to prevent, diagnose and treat cancer is a promising
are(Liao, Ding, Jiang, Wang, Zhang & Zhang, 2018). As a matter of fact, the earlier a cancer is diagnosed, the
better the chances of survival. Or, if diagnosed in the late stages of the disease, the faster the patient is treated
with the right treatment, the better the chances of survival. Recently, the availability of a large and diverse
amount of data has made it possible to use intelligent systems to get more precise results for patients. Depending
on the type of cancer, facing metastasis is always a challenge for physicians, scientists and patients (La Porta &
Zapperi,2018). This paper is to illustratehow artificial intelligence algorithms have been improving and boosting
the research to save lives.
KEYWORDS: artificial intelligence, cancer, machine learning, artificial neural networks, prevention, diagnosis,
treatmen
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
Overview of Health Informatics: survey of fundamentals of health information technology, Identify the forces behind health informatics, educational and career opportunities in health informatics.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about transformational advances in patient care, research, and healthcare management. United States is the focus due fact that many academic and research institutions in the country are at the forefront of healthcare data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect, process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more educated decisions, forecast health outcomes, manage population health, customize treatment, optimize workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data intelligence applications raises issues and concerns about data privacy, fairness, transparency, data quality, accountability, fair data access, regulatory compliance, and the balance between automation and human judgment. Emerging themes include AI and machine learning domination, stronger ethical and regulatory frameworks, edge and quantum computing, data democratization, sustainability applications, and developing human-machine collaboration. Data intelligence has an impact that goes beyond healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth. Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine healthcare excellence and extend their influence across sectors.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about
transformational advances in patient care, research, and healthcare management. United States is the
focus due fact that many academic and research institutions in the country are at the forefront of healthcare
data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of
Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and
emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect,
process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more
educated decisions, forecast health outcomes, manage population health, customize treatment, optimize
workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data
intelligence applications raises issues and concerns about data privacy, fairness, transparency, data
quality, accountability, fair data access, regulatory compliance, and the balance between automation and
human judgment. Emerging themes include AI and machine learning domination, stronger ethical and
regulatory frameworks, edge and quantum computing, data democratization, sustainability applications,
and developing human-machine collaboration. Data intelligence has an impact that goes beyond
healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth.
Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine
healthcare excellence and extend their influence across sectors.
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...ijsc
Data intelligence technologies have transformed the United States healthcare sector, bringing about
transformational advances in patient care, research, and healthcare management. United States is the
focus due fact that many academic and research institutions in the country are at the forefront of healthcare
data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of
Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and
emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect,
process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more
educated decisions, forecast health outcomes, manage population health, customize treatment, optimize
workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data
intelligence applications raises issues and concerns about data privacy, fairness, transparency, data
quality, accountability, fair data access, regulatory compliance, and the balance between automation and
human judgment. Emerging themes include AI and machine learning domination, stronger ethical and
regulatory frameworks, edge and quantum computing, data democratization, sustainability applications,
and developing human-machine collaboration. Data intelligence has an impact that goes beyond
healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth.
Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine
healthcare excellence and extend their influence across sectors.
Big Data in Healthcare
Hospital and healthcare providers can use big data to expand the scope of their projects and draw comparisons over larger populations of data. Because big data involves the use of automation and artificial intelligence, data can be processed in larger volumes and higher velocity to uncover valuable insights for Management.
Big data enables management to proactively identify issues with real-time access to the data so that decisions can be base more on hard evidence and facts, rather than emphasizing on guesswork and assumptions about customers, employees, and vendors. Applying analytics to big data creates many opportunities for healthcare businesses to gain greater insight, predict future outcomes and automate non-routine tasks.
Healthcare industries have gone through massive technology driven transformations over the past decade. This is a result of the significant advancement in digitized, disruptive, open sourced and pervasive healthcare information technologies and peripherals in application, that are continuously producing huge volumes of diversified data. In a recent literature review, Agrawal and Prabakaran1 suggested that big data are an integral part of “the next generation of technological developments” that reveal new insights from vast quantities of data being produced from various sectors, including health care. (Shah J Miah, Edwin Camilleria, and H. Quan Vub).
Healthcare requires a lot of analysis and less room for error, with big data and analytics procedure can be game changer. Healthcare busines requires to analyze, store, and continuously update patient’s data and these tasks cannot efficiently be achieved without the help of big data.
According to Pastorino, the use of big data in health care can provision the design of solutions that improve patient care and can generate value and new strategies to overcome dynamic challenges in healthcare organizations. This is attributed to big data in health care providing an opportunity to detect meaningful patterns, which in turn produce actionable knowledge for precision medicine and various healthcare decision-makers. (Shah J Miah, Edwin Camilleria, and H. Quan Vu)
Harmony Alliance stated that opportunities offered by big data “will only materialize when healthcare systems move beyond the mere collection of large amounts of data. Linkage of previously separated data sets and their analysis using appropriate big data analytics offer new ways to accelerate research and to identify the right treatment for individual patients. Access to large data sets that paint a more comprehensive picture of patients allows patient-relevant outcomes to be measured more accurately.”
Big data is becoming crucial in this time of Covid-19, where data need to be collected from different corner of the globe. Data are collected in a big amount and need to be processed in real time so the decision-makers can have enough information to work on. Today’s world is interconnected, and pa ...
POST EACH DISCUSSION SEPARATELYThe way patient data is harvested.docxLacieKlineeb
POST EACH DISCUSSION SEPARATELY
The way patient data is harvested and used is rapidly changing. Patient data itself has become quite complex.
In the future
, patient data will be combined with financial data, product or drug data, socioeconomic factors, social patterns, and social determinants of health. Cognitive behavior and artificial intelligence will be applied to the data to help prevent and depict rather than cure disease.
Evaluate the future of Healthcare information technology.
Include the following aspects in the discussion:
Find two articles related to the future of information systems (IS) in healthcare
Include telehealth, wearable technology, patient portals, and data utilization
Analyze potential benefits from advances
Discuss, from your own perspective, the advantages and disadvantages of having a system where the patient manages their own data
REPLY TO MY CLASSMATE’S DISCUSSION TO THE ABOVE QUESTIONS AND EXPLAIN WHY YOU AGREE. MINIMUM OF 150 WORDS EACH
Classmate’s Discussion 1
The technological advancements that have occurred in the field of healthcare have greatly changed the way people view and interact with the healthcare system. They have also led to the reduction of costs and the increasing efficiency of the system. We expect that the future of healthcare will continue to be influenced by information technology.
Due to the technological advancements that have occurred in the field of healthcare, physicians are now able to spend less time with their patients. This has allowed them to provide more effective and efficient care to their patients. In the future, we can expect that the increasing number of specialists who can delegate their work to other doctors will have a significant impact on the healthcare system. The increasing efficiency of doctors is expected to have a significant impact on the shortage of specialist physicians in the future. This issue could be solved using technology. Hopefully, the use of information technology can help boost the number of specialist physicians (Patric, 2022).
Electronic health records have revolutionized the way healthcare is done. Despite the progress that has been made in terms of keeping and tracking these records, they are still not widely used yet. This means that the kind of growth that was expected from the adoption of these records has not materialized. Although the adoption of electronic health records has been made in various parts of the world, it’s still not widely used in all areas. This means that the ability to keep track of one’s medical history is still very important (Patric, 2022).
The increasing importance of information technology in healthcare has led to the prediction that the cost of healthcare will eventually come down. Various factors such as better accessibility and efficiency will help make healthcare more affordable and more effective.
It’s widely believed that keeping one's health is much cheaper and easier than treating a.
Application of Big Data in Medical Science brings revolution in managing heal...IJEEE
Big Data can be combined with new technology to bring about positive conversion in the health care segment. A technology aimed at making Big Data analytics a certainty will act as a key element in transforming the way the health care industry operates today. The study and analysis of Big Data can be used for tracking and managing population health care effectively and efficiently. In ten years, eighty percent of the work people do in medicine will be replaced by technology. And medicine will not look anything like what it does today. Healthcare will change enormously as it becomes a data-driven industry. But the magnitude of the data, the speed at which it’s growing and the threat it could pose to individual privacy mean mastering "big data" is one of biomedicine's most pressing challenges. Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. This also plays a vital role in delivering preventive care. Health care will change a great deal as it becomes a data- driven industry. But the size of the data, the speed at which it’s growing and the threat it could cause to individual privacy mean mastering it is one of biomedicine's most critical challenges. In this research paper we will discuss problems faced by big data, obstacles in using big data in the health industry, how big Data analytics can take health care to a new level by enhancing the overall quality of patient care.
Please respond to each of the 3 posts with 3 APA sources no older thmaple8qvlisbey
Please respond to each of the 3 posts with 3 APA sources no older than 5 years old. APA format must be exceptional.
Reply 1
Professor,
How can big data impact prescription errors? Be specific and provide examples. Who should be on the team to implement this project and why? Support your work with the literature.
Reply 2
Ruth Niyasimi,
Big Data Risks and Rewards
Big data is defined as the process of collecting, analyzing, and leveraging consumer patient, physical, and clinical data that is too vast or complex to be understood by traditional means of data processing. In healthcare, data is generated from medical records, patient portals, government agencies, research studies, electronic health records, and medical devices. The data generated in healthcare is used to make decisions that will have an impact on patient health outcomes (Raghupathi & Raghupathi, 2014). Healthcare is a critical docket in our society since it is tasked with a duty to prevent, diagnose and treat illnesses and diseases affecting the community. In the past, health information was stored on paper but through advancements in technology, things have significantly changed as patient information is stored on Electronic health records (EHR).
The adoption of big data had significant impacts on customer services and other related issues. According to Raghupathi and Raghupathi (2014), for many years, healthcare has been generating huge volumes of data that was stored in hardcopy. This was a critical step toward improving the quality of healthcare delivery while reducing costs. This huge volume of information is crucial to healthcare because, through digitalization, it has become possible to detect diseases at an early stage and take necessary intervention measures. Secondly, big data enables the ability to enhance continuity, starting when a patient visits a hospital until the last stage of being discharged. For example, the lab tests taken from those patients and other specialized treatments are stored in a way that other departments can access this information in the future preventing duplicate redoing labs and imaging studies (Adibuzzaman et al., 2017). This cuts down costs while improving service delivery.
Although big data has had a tremendous impact on the healthcare systems, it has also created some problems. Firstly, the use of technology such as EHR has resulted in security issues and privacy threats. According to McGonigle and Mastrian (2017), technology has enabled the interoperability of healthcare data. Interoperability means sharing important health data across different organizations while ensuring it is presented understandably to the user. Unauthorized third parties can intersect this information and the Health Insurance Portability and Accountability Act (HIPPA) has shown little concern for patient data breach cases. Another problem is that big data is not static, it requires continuous system updates to ensure that it ...
A Case Analysis on Involvement of Big Data during Natural Disaster and Pandem...YogeshIJTSRD
Big data is an upcoming technology and requires utmost care for an efficient and smooth implementation of the technology. In case of healthcare the most challenging part of big data is the privacy, data security, handling large volume of medical imaging data and data leakage. It can be useful to this sector when big data is made structured, relevant, smart and accessible and the managers should give importance to the strategic and business value of big data technology rather than only concentrating at the technological aspect of the implementation. The use of big data in natural disasters and pandemics helps to understand and make better decision with fast processing of the data that are collected through various sources such as social media, sensors and other internet activities. This paper tries to focus on effective involvement of Big Data in natural disaster and pandemic and also identify the current and future use of Big Data in health care sector. The paper identifies the critical aspects which are used for Big data implementation and describe ways to handle the challenges related to it. Mr. Bibin Mathew | Dr. Swati John "A Case Analysis on Involvement of Big Data during Natural Disaster and Pandemics and its Uses in the Health Care Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45049.pdf Paper URL: https://www.ijtsrd.com/management/other/45049/a-case-analysis-on-involvement-of-big-data-during-natural-disaster-and-pandemics-and-its-uses-in-the-health-care-sector/mr-bibin-mathew
Data analytics are having a significant impact on the healthcare industry. As the world's population lives longer on average, current treatment options face substantial hurdles in Clinical Data Science.
The care business traditionally has generated massive amounts of inf.pdfanudamobileshopee
The care business traditionally has generated massive amounts of information, driven by record
keeping, compliance & regulative needs, and patient care [1]. whereas most knowledge is hold
on in text type, the present trend is toward fast conversion of those massive amounts of
information. Driven by obligatory needs and also the potential to enhance the standard of health
care delivery in the meantime reducing the prices, these huge quantities of information (known
as ‘big data’) hold the promise of supporting a good vary of medical and care functions, as well
as among others clinical call support, illness police work, and population health management [2,
3, 4, 5]. Reports say knowledge from the U.S. care system alone reached, in 2011, one hundred
fifty exabytes. At this rate of growth, huge knowledge for U.S. care can before long reach the
zettabyte (1021 gigabytes) scale and, shortly when, the yottabyte (1024 gigabytes) [6]. Kaiser
Permanente, the California-based health network, that has over nine million members, is
believed to possess between twenty six.5 and forty four petabytes of doubtless made knowledge
from EHRs, as well as pictures and annotations [6].
By definition, huge knowledge in care refers to electronic health knowledge sets therefore
massive and complicated that they\'re tough (or impossible) to manage with ancient computer
code and/or hardware; nor will they be simply managed with ancient or common knowledge
management tools and strategies [7]. huge knowledge in care is overwhelming not solely due to
its volume however additionally due to the range of information varieties and also the speed at
that it should be managed [7]. The totality of information associated with patient care and well-
being compose “big data” within the care business. It includes clinical knowledge from CPOE
and clinical call support systems (physician’s written notes and prescriptions, medical imaging,
laboratory, pharmacy, insurance, and alternative body knowledge); patient knowledge in
electronic patient records (EPRs); machine generated/sensor data, like from observance
important signs; social media posts, as well as Twitter feeds (so-called tweets) [8], blogs [9],
standing updates on Facebook and alternative platforms, and net pages; and fewer patient-
specific data, as well as emergency care knowledge, news feeds, and articles in medical journals.
For the massive knowledge person, there is, amongst this large quantity and array of information,
chance. By discovering associations and understanding patterns and trends inside the
information, huge knowledge analytics has the potential to enhance care, save lives and lower
prices. Thus, huge knowledge analytics applications in care cash in of the explosion in
knowledge to extract insights for creating higher enlightened selections [10, 11, 12], and as a
groundwork class square measure noted as, no surprise here, huge knowledge analytics in care
[13, 14, 15]. once huge knowledge is synthesized and an.
Digital technology is changing the relationship between patient and doctor, and healthcare providers must adopt new approaches to data and information.
Read our new article to gain insights of how the adoption of cloud affects the healthcare industry.
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
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Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
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Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems and Analytics
1. Big Data, CEP and IoT : Redefining Holistic
Healthcare Information Systems and Analytics
Tawseef Naqishbandi
Dept.of Computer Science and Engineering,
Islamic University of science and Technology,
Awantipora Kashmir
Imthyaz Sheriff .C
Dept of Computer Science and Engineering,
B. S. Abdur Rahman University
Vandular, Chennai
Sama Qazi
Dept. Electronics and Communication
Islamic University of Science and Technology
Awantipora, Jammu and Kahmir, India
Abstract— Healthcare industry has been a significant area
for innovative application of various technologies over
decades. Being an area of social relevance governmental
spending on healthcare have always been on the rise over
the years. Event Processing (CEP) has been in use for
many years for situational awareness and response
generation. Computing technologies have played an
important role in improvising several aspects of
healthcare. Recently emergent technology paradigms of
Big Data, Internet of Things (IoT) and Complex Event
Processing (CEP) have the potential not only to deal with
pain areas of healthcare domain but also to redefine
healthcare offerings. This paper aims to lay the
groundwork for a healthcare system which builds upon
integration of Big Data, CEP and IoT.
Keywords—Big Data; Internet of Thing; Complex Event
Processing; CEP; IoT; Body Sensor Networks; Healthcare
I. INTRODUCTION
The health care industry is extremely big incorporating
several sectors that are dedicated to providing health care
services and products. Some of these sectors depend
significantly on data storage, computing and communication
technologies. Healthcare Information Technology (HIT) [16]
is ―the application of information processing involving both
computer hardware and software that deals with the storage,
retrieval, sharing, and use of health care information, data, and
knowledge for communication and decision making‖. In
recent years a newer sector of Healthcare known as
‗Healthcare Informatics‘ has emerged and started to gain
significant popularity. It is a discipline at the intersection
of information science, computer science, and healthcare [17].
Healthcare finance and insurance is also another area which
relies significantly on computing and communication
technologies.
Healthcare information systems have come a long way from
being simple patient data management systems, to being
repositories of huge volume and variety (blood work reports,
diagnosis details, prescription details, scans, x-rays images
etc.). Most of this data goes unanalyzed due to its sheer
volume and also due to lack of meaningful correlative
analytics. Task of analysis all this data becomes even tougher
due increasing use of Personal Health Systems (PHS) and
Remote Patient Monitoring and Treatment (RMT), Body Area
Networks (BAN). Such monitoring devices keep pouring data
at a rapid rate and most of this may go unanalyzed.
Healthcare is an area of concern and importance for both
developed and developing nations owing to its social
relevance. Governments across the world are focusing on all
aspects of healthcare like policy changes, legal statures,
insurance, funding, and technology overhaul. Patient
Protection and Affordable Care Act also popularly known as
Obama care is ushering significant changes in US healthcare
industry. Hearth insurers are expected to see substantial
increase in their cost due to increased risk of covering more
people and they cannot legally deny insurance to individuals
based on prior health conditions. Affordable care ensures that
maximum number of people is covered. This poses as a
challenge to hospitals as they will witness increase in patients,
which means increasing amount of data to be analyzed.
This paper is organized as follows: Section II discuss about
Big Data and its role in Healthcare. Section III details about
IoT, its role in future world and how it contributes to
healthcare. Section IV provides an overview of CEP and how
it is significant in Healthcare information systems and
analytics. Section V proposes a holistic system for healthcare
informatics. Finally, Section VI gives the conclusion and the
step forward.
II. BIG DATA – CRUNCHING HEALTHCARE
DATA
The new era of health care will be driven by an explosion
in health-related data from a growing range of public and
private sources, and can be analysed by increasingly powerful
number-crunching computers. This new era is moving from a
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2. world where illnesses can be treated to one where it can be
predicted and can be prevented. Big Data technologies will
play a key role in turning this idea into reality. Big Data is a
term encompassing the use of techniques to capture, process,
analyze and visualize potentially large datasets in a reasonable
time frame not accessible to standard IT technologies [1]. It
refers to the ability to crunch vast collections of information,
analyze it instantly, and draw from it sometimes profoundly
surprising conclusions. According to Gartner analyst Doug
Laney [2] data growth challenges and opportunities as being
three-dimensional, i.e. increasing volume (amount of data),
velocity (speed of data in and out), and variety (range of data
types and sources). Big data technologies deal with petabytes
of records, files, transactional data either arriving as streams
or in batches. Rise of disruptive technologies like social and
mobile are contributing to increase in unstructured and semi
structured data.
Figure 1: Big Data Properties
Big data technologies have to deal with all these varieties of
data. Whereas volume, variety, velocity are the native
properties of big data system, it has also three acquired
properties of variability (indication of changing nature of
data), value(significance based on statistics, hypothesis etc,),
and veracity (trustworthiness of data, provenance etc.).
`Big data is an idle fit for dealing with the technology
challenges faced by the Health care industry. Increasing
public health records with all the new sources of health data
generated by wearable sensor devices, Wi-Fi enabled scales,
smart phones and low-cost diagnostic kit, could provide a far
more accurate picture of individual‘s health and the
treatments they receive. In terms of big data for health care
―Volume‖ refers to the rapidly expanding size of the sets of
data being generated in every area of activity in an healthcare
enterprise, from revenue, to patient data, to supply and
operations. ―Variety‖ includes the diversity of data collected.
In a hospital, for instance, data includes patient records
containing a variety of information like lab reports, scans, x-
rays, prescription details and so on. Beyond diagnosis and
treatment related data, all aspects of finance, patient
scheduling and workflow, insurance data and medical
outcomes etc are also available. Synthesizing and analyzing
such disparate elements is challenging on its own. With
newer sources of healthcare monitoring devices like personal
health monitors, Body Area Networks (BAN) there is
significant ―Velocity‖ of incoming healthcare data.
There used to be a time in healthcare industry where more
in-patient days translated to more revenue. But now patients
are increasingly demanding information about their healthcare
options so that they understand their choices and can
participate in decisions about their care. Patients are also an
important element in keeping healthcare costs down and
improving outcomes. Providing patients with accurate and up-
to-date information and guidance rather than just data will
help them make better decisions and better adherence to
treatment programs [4]. As a result of this focus on
meaningful information, all healthcare constituents are
impacted by big data, which supports analytics that predict
how these patients are likely to behave, encourage desirable
behavior and minimize less desirable behavior. Report on Big
Data Analytics [5] indicates that Big data solutions can help
stakeholders personalize care, engage patients, reduce
variability and costs, and improve quality of health delivery.
Big data analytics can also contribute to providing a rich
context to shape many areas of health care like analysis of
effects, side-effects of drugs, genome analysis etc.
As populations grow and people live longer, healthcare
costs are growing to unsustainable levels. Insights derived by
big data analytics can help remove inefficiencies from
system. For example, by analysing prescription drugs for
specific ailments across the country, Big data analytics can
help in saving lots of money if doctors switched from
branded to cheaper - but equally effective generic versions of
the drug. Big data analytics can aide hospitals in efficient
resources management by reducing emergency waiting times,
track patient movements, moderate X-ray dosage levels etc.
Access to huge amounts of healthcare data coupled with
insights provided by big data analytics, it will be possible to
develop predictive algorithms that can forecast which
demographics are likely to cost the most to treat in future, for
conditions like diabetes and asthma. This will enable
healthcare providers intervene earlier and redesign their
services to cope with the expected massive increase in
healthcare demand.There are also certain challenges particular
to using big data for healthcare:
Accuracy: Human tendency to understate negative factors,
such as smoking and failure to comply with treatment is of
concern. People also tend to overstate positive factors, such as
exercise. These biases need to be identified and corrected, or
passive techniques need to be used in order to acquire data
that does not have self-reported bias.
Privacy: People are reluctant to divulge personal information
because of concerns about privacy. Providing guarantees for
security and privacy, incentives will help to address these
concerns.
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3. Consistency: Standards need to be developed and
implemented to promote consistency, increase usefulness and
facilitate data usage amongst all the stakeholders involved in
various healthcare sectors.
Facility: Mechanisms need to be developed to make it easy
for patients to accurately self-report data. This includes
evolving passive mobile computing, wearable devices that
require no effort. Techniques are needed to get data from
healthy people to make the populations truly representative
and not biased by the ill in any kind of statistical analysis.
Fragmentation: Healthcare data is significantly fragmented.
There is also unwillingness for healthcare participants to share
data.
III. INTERNET OF THINGS – INTEGRATED
MONITORING OF HEALTH
The Internet of Things (IoT), sometimes referred to as the
Internet of Devices or Internet of Everything, is expected to
change the world as one sees and perceives it today. Internet
of Things (IoT) is a worldwide network of interconnected
objects and their virtual representations uniquely addressable
based on standard communication protocols. Identified by a
unique address, any object including computers, mobile
phones, RFID tagged devices, and especially Wireless Sensor
Networks (WSN) will be able to dynamically join the
network, collaborate, and cooperate efficiently to achieve
different tasks. IoT devices gather and share information
directly with each other and the cloud, making it possible to
collect record and analyze new data streams faster and more
accurately [11].
World population is on the raise. Especially in countries
like India and China with higher population growth, health
care has become one of the largest sectors, both in terms of
revenue and employment – and its growth is expected to
continue. Estimates project that the current US $40 billion
Indian health care industry will grow to US $280 billion by
2020. Such massive systems can effectively handled only by
embracing cutting edge technologies. IoT will be key enabler
for holistic healthcare environment. The emergence of the
IoT, in which devices connect directly to data and to each
other, is important in future of healthcare for two reasons:
1. The recent advances in embedded sensors and
connectivity technology are allowing devices to connect,
anywhere, anytime anyplace to collect record and analyze data
that was not accessible before. In healthcare, this means being
able to connect and collect patient data on the air over time
that can be used to help enable preventive care, allow prompt
diagnosis of acute complications and promote understanding
of how a meditation is helping improve a patient‘s parameters.
2. The ability of these smart devices to connect and
gather data on their own removes the limitations of human-
entered data—automatically obtaining the data that suits
doctor‘s need; at the time and in the way they need it. The
automation reduces the risk of error. Fewer errors can mean
increased efficiency, lower costs and improvements in quality
in just. In healthcare, where human error can literally be the
difference between life and death, IoT devices are a boon.
IoT-related healthcare issues today are based on the
essential definition of the IoT as a network of devices that
connect directly with each other to capture and share vital data
through a secure service layer (SSL) that connects to a central
command and control server in the cloud. Energy efficiency
[7],[10] of sensors will play a major role in the success of this
technology. A major concern about sensors is Energy
efficiency of these smart devices; various methodologies have
been discussed in literature for saving energy in sensor nodes
[7].
From the perspective of healthcare, IoT devices can
broadly fall in three categories as shown in Figure 2, 3 and 4.
Personal Monitoring Devices: Wearable blood pressure
monitors, thermal sensors, smart pacemakers, movement
monitor (posture, gait, step size, step height), EEG nodes,
ECG Nodes, pulmonary monitors, diagnostic aides at
hospitals.
Smart Home Devices: Home automation has always been a
interesting idea for deployment of sensors. With focus on
healthcare, IoT devices at home can monitoring living
conditions (temperatures, light etc) at home, keep track of
what a person eats (smart fridges). This devices can give
better insights for healthcare.
Environment Monitoring Sensors: These have been around
for quite some time for monitoring air quality, temperature,
humidity etc in cities. With newer and better sensors smart
cities are becoming a reality. Environmental data can help
understand spread of ailments like asthma, hypertensions etc
which may be attribute to environmental conditions to some
degree.
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4. Figure 2:Personal Monitoring Devices
Figure 3: Smart Home Devices
While still an emerging trend, machine-to-machine (M2M) --
and the overall Internet of Things phenomenon -- have
Figure 4: Environment Monitoring Sensors
captured the healthcare industry's attention. The two
technologies shared the No. 4 spot on Gartner's 2013 Top 10
Strategic Technology Trends list[12]. Another research firm,
Markets and Markets, recently projected the Internet of
Things and M2M communications market to grow from $44
billion in 2011 to $290 billion by 2017, representing an
estimated CAGR of 30 percent from 2012 to 2017. For
solution providers, the technologies represent a way to grow
their business, provided they take the right steps and forge the
right partnerships. Ability of personal health monitoring
devices to communicate instantly with doctors, hospitals,
emergency services will redefine the landscape of healthcare
industry.
Though a completely viable world of IoT is in near-distant
future, some of the ingredients like Wireless Sensor Network
(WSN), Body Areas Networks(BAN) are already existent and
deeply being explored. Wireless Sensor Network (WSN)
refers to a distributed network, consisting of dispersed and
autonomous sensing stations. Each sensing station—also
known as a sensor node—consists of a computing component,
communication component, a power source normally a
battery, and some sensors depending upon the application
area. Some smart sensors are equipped with an actuator [8]—
an electro-mechanical device used to control different
components of the system. Wireless Body Sensor Networks
(WBSNs) has offered a paradigm shift which can be used for
early detection of the different diseases so that health issues
can be detected at an early stage. These smart things can
collect and analyze the vital sign-related data of patients by
deploying different types of bio-medical sensors (for example:
body temperature, heartbeat, blood pressure,
electrocardiogram (ECG), electro encephalogram (EEG), etc.
sensors) for a long period of time, thus reducing the healthcare
costs. The bio-medical smart sensor nodes can either be
suitably placed on the body or implanted inside the body as
depicted in Figure.5. These bio-medical sensor nodes send the
sensed information to a coordinator (base station), located on
or near the body. The coordinator (base station) is responsible
for forwarding the collected information to the sink node. The
sink node will send the received data to the health care centre
or any other destination.
Figure.5.Body Area Network as part of IoT
Benefits IoT in Health Care include:-
Clinical care: Hospitalized patients whose
physiological status requires close attention can be constantly
monitored using IoT-driven monitoring. This type of health
care solution employs sensors to collect comprehensive
physiological information and uses gateways and the cloud to
analyze and store the information and then send the analyzed
data wirelessly to caregivers for further analysis and review. It
replaces the process of having a health professional come by
at regular intervals to check the patient‘s vital signs, instead
providing a continuous automated flow of information. In this
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Vol. 4 Issue 01,January-2015
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5. way, it simultaneously improves the quality of care through
constant attention and lowers the cost of care by eliminating
the need for a caregiver to actively engage in data collection
and analysis.
Ongoing monitoring: With Internet of Things
technology — such as wireless EKG machines , devices for
diabetics to track their data, and necklaces or belts that
transmit heart rate data — healthcare professionals can collect
and store real-time information about their patients, and even
be alerted when something is wrong and action is needed. The
Internet of Things can transform healthcare from a reaction-
based process into an ongoing one.
Scaled expertise: A challenge in healthcare today is
that expertise is often limited to an individual or location. For
example, a doctor who‘s an expert in a particular procedure
can only be in one place, and with one patient at a time. But
the Internet of Things is helping technology evolve to allow
for physicians to be able to perform procedures remotely.
Remote monitoring: There are people all over the
world whose health may suffer because they don‘t have ready
access to effective health monitoring. But small, powerful
wireless solutions connected through the IoT are now making
it possible for monitoring to come to these patients instead of
vice-versa. These solutions can be used to securely capture
patient health data from a variety of sensors, apply complex
algorithms to analyze the data and then share it through
wireless connectivity with medical professionals who can
make appropriate health recommendation .
Hospital prevention: Thanks to ongoing
monitoring, Internet of Things technology can also help
patients know when a trip to the hospital isn‘t needed.
The devices continually sending health data to doctors can
open up the communication and treatment channels so that
unnecessary hospital trips are prevented.
IV. COMPLEX EVENT PROCESSING
Complex Event Processing (CEP) technologies have been
used for situational awareness and decision making for quite
few years. It has regained focus owing to the rising demand
for on-the fly processing of events. Growing popularity of
Big Data and Internet of Things (IoT) coupled with the need
for real-time analytics have fueled the rapid growth of CEP
technologies and tools.
The idea of Complex Event Processing (CEP) was first
introduced and discussed in detail by Luckham [14]. It is in
recent years that CEP has evolved and gained prominence as
separate discipline by building upon different technology
paradigms like discrete event systems, active databases, Data
Stream Management Systems (DSMS), stream reasoning and
network management etc. Eckert and Bry [15], defined
Complex Event Processing as a technology which
encompasses methods, techniques, and tools for processing
events while they occur, i.e., in a continuous and timely
fashion. According to Luckham [13], CEP consists of
principles for processing clouds of events to extract
information, together with technologies to implement those
principles.
Several CEP engines and frameworks have been proposed
and/or developed over past decade and deployed over various
domains and problem areas like business process monitoring,
healthcare, smart cities, traffic analysis, fraud detection,
automation etc. Comprehensive CEP frameworks [18] will be
an idle fit for healthcare domain. CEP engines can analyze
events and related data which come from various sources
(health sensors, environment sensors etc.) in real-time and
provide insights for a better healthcare.
CEP can analyses event from personal sensors in
correlation with smart home sensor are provides insights
which can answers questions pertaining to linkage between
personal health, food consumed, quality of lifestyles etc. CEP
can also analyzed health changes in correlation with
environmental factors like humidity, air quality, temperature
etc by subscribing to events from environment monitoring
sensors.
Parts of the big data that is of no interest it can be filtered
with Complex Event Processing (CEP) as they get into the
system and selected data can be compressed by orders of
magnitude. One challenge is to define these filters in such a
way that they do not discard useful information. Complexities
of Big Data associated with the sheer volume can be handled
by suitable integration with Complex event processing.
V.HOLISTIC HEALTHCARE
Big Data, CEP and IoT together holds a great potential to
change the present scenario of healthcare, starting from drug
discovery to remote monitoring of patients, to faster
settlement of health insurance, to improved clinical
outcomes. The three native properties (volume, variety,
velocity) along with the acquired properties (variability, value
and veracity) define the key characteristics of big data which
are very much suited to healthcare issues. IoT is useful in
collecting real time data from patients anytime, anywhere,
any place, and easily relates to all the three native properties
of big data. Current practice of collecting patient's data at
bedside involves writing it down to a paper spreadsheet and
then the notes are typed in a data entering finally The data is
transmitted to a database server that organizes, indexes, and
make it accessible through a database interface and then data
is analyzed by doctors, pharmacists etc. It is now clear that
there is latency between data gathering, information
accessibility and this will grow more as there is increase in
volume of data in terms of health databases, clinical data,
health insurance data, repositories which can provide better
results for better future health care if analyzed properly with
the help of complex event processing and big data analytics.
This paper proposes a holistic health care approach for
performing complex event processing on the real time data
coming from wearable and non wearable sensors whose
volume, velocity; variety is huge in terms of storage and
processing. The elements and interactions are described
below and shown in Figure.6.
IoT Module: Contains the smart elements to extract real
time data, transform and load the health data. The data
generated from these smarter things is very sensitive and
large enough in terms of volume, variety and velocity. So,
instead of storing the data in databases for future use, better is
to analyze the data at that time only, with the help of
Complex Event Processing.
International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
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617
6. CEP Module: - CEP will extract real time data from IoT
devices as soon as it is generated, and that will be analyzed
by applying real time event analytics , and more over CEP
can extract the information from the Big data databases on a
need basis, which are large enough in terms of volume and
byapplying analytics to find the patterns and analyze the data
of the patients regarding the past and present health.
Big Data Analytics: - Big data analytics comes into play in
order to unlock the value of data, organizations need Inferring
knowledge from complex heterogeneous patient sources
Leveraging the patient/data correlations in longitudinal
records. Understanding unstructured clinical notes in the right
context. Efficiently handling large volumes of medical
imaging data and extracting potentially useful information
and biomarkers. Analyzing genomic data is a computationally
Figure .6. Holistic Healthcare System (Integration of CEP, Big Data, IoT)
intensive task and combining with standard clinical data adds
additional layers of complexity. Capturing the patient‘s
behavioral data through several smart sensors; their various
social interactions and communications. Take advantage of
the massive amounts of data and provide right intervention to
the right patient at the right time. Personalized care to the
patient Potentiallywill benefit all the components of a
healthcare system i.e., provider, payer, patient, and
management.
VI. CONCLUSION
Big Data Analytics ,Complex Event Processing and IoT
together have extreme potential in solving exsisting and
future problem of healthcare industry. Application of these
technologies is still in infancy in the healthcare domain. With
renewed focus on better healthcare, growth in population ,
increasing prices, healthcare industry has to embrace cutting
edge technoliges like for effective and efficeint functioning.
Such application of technology should happen in a integrative
manner such as to deliver a complete healthcare solution.
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618
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