Health Care Revolution Research Articles

A Comprehensive Review Study on: Optimized Data Mining, Machine Learning and Deep Learning Techniques for Breast Cancer Prediction in Big Data Context

In recent years, big data in health care is commonly used for the prediction of diseases. The most common cancer is breast cancer infections of metropolitan Indian women as well as in women worldwide with a broadly factor occurrence among nations and regions. According to WHO, among 14% of all cancer tumours in women breast cancer is well-known cancer in women in India also. Few researches have been done on breast cancer prediction on Big data. Big data is now triggering a revolution in healthcare, resulting in better and more optimized outcomes. Rapid technological advancements have increased data generation; EHR (Electronic Health Record) systems produce a massive amount of patient-level data. In the healthcare industry, applications of big data will help to improve outcomes. However, the traditional prediction models have less efficiency in terms of accuracy and error rate. This review article is about the comparative assessment of complex data mining, machine learning, deep learning models used for identifying breast cancer because accuracy rate of any particular algorithm depends on various factors such as implementation framework, datasets(small or large),types of dataset used(attribute based or image based)etc. Aim of this review article is to help to choose the appropriate breast cancer prediction techniques specifically in the Big data environment to produce effective and efficient result, Because “Early detection is the key to prevention-in case of any cancer”.

Thoracic surgery in United Arab Emirates.

The United Arab Emirates (UAE) has undergone a significant change in its population and economy in the last decades and in parallel its healthcare system has evolved rapidly to provide advanced, innovative and world-leading care. At the forefront of this revolution in healthcare is the development of a multidisciplinary multimodality thoracic service provision, offered at quaternary referral hospitals amalgamating academics, training, research and innovation. Previously, thoracic service care was limited to single providers at various public and private hospitals, usually performing lower complexity cases. Most complex thoracic cases were repatriated outside the UAE. This practice was replaced with the opening of Cleveland Clinic Abu Dhabi (CCAD), in 2015, which created a multidisciplinary thoracic program. This included the start of a mini-invasive surgical and lung transplantation program. Since that time other public and private hospitals have emerged providing care in a similar model. The impact of these programs has been a decreased transfer of patients abroad for treatment. Under the umbrella of the Emirates Thoracic Society (ETS) a platform for greater collaboration aimed at improving patient care, potential research and physician education has been created. Direct links have been established with world-leading Thoracic surgery and Respiratory Medicine Centers facilitating this development and offering support and guidance. This article charts these changes in thoracic care in the recent past, present, and delineates plans for the future in the UAE.

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities.

Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.

Big Data Health Care Innovations: Performance Dashboarding as a Process of Collective Sensemaking.

Big data is poised to revolutionize health care, and performance dashboards can be an important tool to manage big data innovations. Dashboards show the progress being made and provide critical management information about effectiveness and efficiency. However, performance dashboards are more than just a clear and straightforward representation of performance in the health care context. Instead, the development and maintenance of informative dashboards can be more productively viewed as an interactive and iterative process involving all stakeholders. We refer to this process as dashboarding and reflect on our learnings within a large European Union–funded project. Within this project, multiple big data applications in health care are being developed, piloted, and scaled up. In this paper, we discuss the ways in which we cope with the inherent sensitivities and tensions surrounding dashboarding in such a dynamic environment.International Registered Report Identifier (IRRID)RR2-10.2196/16779

Book Review: Phil Jones, The Arts Therapies: A Revolution in Healthcare

Book Review: Phil Jones, <i>The Arts Therapies: A Revolution in Healthcare</i>

Netiquette for social media engagement for oncology professionals.

Social media growth has revolutionized health care, facilitatinguser-friendly, rapidand global sharing of content. Within oncology, this allows for new frontiers in communication for cancer patients, caregiversand healthcare providers. As more physicians engage in online spaces, it is imperative that there are resources to assist in establishing a professional presence on social media. This article describeshow to create a social media identity, best practices for engaging both in patient and caregiver spaces and professional communities, and how to address antagonistic and inappropriatebehavior on social media with the goal of helping physicians develop an engaging, productiveand enjoyable experience online.

Smartphone and mHealth Use After Stroke: Results From a Pilot Survey.

Advances in smartphone technology have revolutionized health care providing innovative approaches to improve health and well-being. Emerging mobile health (mHealth) apps are becoming increasingly available and offer opportunities to support self-management needs in people with stroke; however, experiences with mHealth among this population are not well understood. The objective of this study is to conduct a cross-sectional survey on smartphone and mHealth app use for adults with stroke. A pilot survey of 50 adults with stroke was conducted. In all, 51% of respondents reported using their smartphone more frequently than before their stroke, and 91% reported having some degree of difficulty with its use. Respondents reported on specific challenges with app use and discussed desired content and features. Occupational therapists should familiarize themselves with valid, accessible, usable, and acceptable apps for people with stroke and consider implementing mHealth apps in self-management intervention plans to improve health, rehabilitation, and community integration outcomes in this population.

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

Digital Twin (DT) is bringing revolution to our lives by a digital representation of the physical system. DT is the creation of the joint usage of various technologies like Cyber-Physical System (CPS), Internet of Things (IoT), Big Data, Edge Computing (EC), Artificial Intelligence (AI), and Machine Learning (ML), etc. DTs are established to optimize a wide range of applications of industry, healthcare, smart cities, smart homes, etc. It is still in its early development stages. This paper fills the gaps by combining the extensive information on technologies utilized in the creation of DT in industry and healthcare. The paper focuses on studying the characteristics of DT, communication technologies and tools utilized in the creation of DT models, reference models, standards, and the researcher&#x2019;s recent work in smart manufacturing and healthcare. Challenges and open issues that need attention are also discussed.

Biotechnology – A Continual Revolution in Product Development and Healthcare – 1st 20 Years versus 2nd 20 Years and Beyond.

Biotechnology (the science and the business) has revolutionized product development and healthcare worldwide over the past 40 years with novel molecules, new companies, and major advances in disease mitigation. The revolution has continued and even accelerated as can be observed in comparing the last 20 years (2000-2019) versus first 20 years (1980-1999), and the recent years of 2020 and 2021 as well. This biotech review addresses the many product categories, indications, research, companies, business changes, and product sales, especially comparing these two time periods and demonstrating the substantial growth and impact of biotechnology and continuing the revolution through the last two years (2020-2021). Over 500 biotech products are available worldwide in 10 different categories of molecules for over 460 indications with over 160 biotech and pharma companies marketing the products. Biosimilar products (close duplicates of marketed products) have become commonplace in last 15 years. The top companies in biotech research and sales over these 40 years include Roche, Amgen, and Novo Nordisk, along with all the pharma companies becoming engaged. Twenty plus of the leading biotech companies have been acquired over these times. All of pharma is now dedicated to biotech molecule development independently and mostly in extensive collaboration with biotech companies. Clinical research has expanded with the top 90 biotech companies that spend at least $100 million in one year totaling over $50 billion in 2020, along with tens of billions from pharma companies for biotech research &amp; development. Over 600 molecules are being studied in phase three clinical trials. Worldwide biotech sales grew to $327 million in 2020 from $35 billion in 2001 with biotech over 25% of all drug sales. The biotech revolution is alive and well and continuing with novel molecules from exiting and more new biotech and pharma companies for further advances in healthcare. This treatise addresses the biotechnology revolution and its dramatic growth and expansion, covering the past 42 years from 1980 to 2021, where-in the author has been collecting data for the last 25 years and maintains 35 biotech databases, which are being reviewed in this publication, and are encompassed in his new database publication, Evens RP. Global Biotech Database e-Sourcebook 2021, Editor – Kenneth KI, Publisher - Tufts Center for the Study of Drug Development, Tufts University, Boston, MA 12/2021. The data sets, tables, and graphs all come from the database publication with data updates for 2021 as available. The definition of products in biotechnology in this publication encompasses nine product categories; recombinant proteins, monoclonal antibodies (also fragments and antibody-drug conjugates), peptides (recombinant and molecular engineered), vaccines (recombinant and molecular engineered), oligonucleotides (RNA inhibitory molecules and gene therapies), cell therapies, tissue therapies, liposomes, and polymers. Naturally occurring substances and extracts from nature or blood can be biologicals, but they are not biotech and are excluded, as well as diagnostic agents being excluded.

Mapping the Digital Healthcare Revolution

Mapping the Digital Healthcare Revolution

Smart Technologies to Mitigate and Manage The Pandemic: Today and Tomorrow

COVID 19 pandemic has affected the modern world in an unimaginable way. Outbreak of the coronavirus and months long total lockdown in major parts of the world has highlighted the lacunas in disaster management and unpreparedness of the world for such a type of pandemic and “unexpected” future pandemic. All the efforts put in combating this infectious virus and to deal with adverse effects of pandemic has proved that use of technology is the only and would be the only effective solution. Each and every sector including the health sector has no option other than adoption of newer technologies in innovative engineering ways. This paper tries to suggest use of various technologies including Artificial Intelligence (AI), cloud computing, big data, blockchain, 5G and smart applications, Internet of Things (IoT), drones, robotics. These and few more digital technologies have been playing a crucial role in effective management of pandemic. This focussed review and discussion is expected to create awareness and motivation to the health care sector for using all the major Industry 4.0 technologies leading to Medicine 4.0. This technological revolution in healthcare and allied sectors will help in effective management of pandemic.

The Health Care Sector's Experience of Blockchain: A Cross-disciplinary Investigation of Its Real Transformative Potential.

BackgroundAcademic literature highlights blockchain’s potential to transform health care, particularly by seamlessly and securely integrating existing data silos while enabling patients to exercise automated, fine-grained control over access to their electronic health records. However, no serious scholarly attempt has been made to assess how these technologies have in fact been applied to real-world health care contexts.ObjectiveThe primary aim of this paper is to assess whether blockchain’s theoretical potential to deliver transformative benefits to health care is likely to become a reality by undertaking a critical investigation of the health care sector’s actual experience of blockchain technologies to date.MethodsThis mixed methods study entailed a series of iterative, in-depth, theoretically oriented, desk-based investigations and 2 focus group investigations. It builds on the findings of a companion research study documenting real-world engagement with blockchain technologies in health care. Data were sourced from academic and gray literature from multiple disciplinary perspectives concerned with the configuration, design, and functionality of blockchain technologies. The analysis proceeded in 3 stages. First, it undertook a qualitative investigation of observed patterns of blockchain for health care engagement to identify the application domains, data-sharing problems, and the challenges encountered to date. Second, it critically compared these experiences with claims about blockchain’s potential benefits in health care. Third, it developed a theoretical account of challenges that arise in implementing blockchain in health care contexts, thus providing a firmer foundation for appraising its future prospects in health care.ResultsHealth care organizations have actively experimented with blockchain technologies since 2016 and have demonstrated proof of concept for several applications (use cases) primarily concerned with administrative data and to facilitate medical research by enabling algorithmic models to be trained on multiple disparately located sets of patient data in a secure, privacy-preserving manner. However, blockchain technology is yet to be implemented at scale in health care, remaining largely in its infancy. These early experiences have demonstrated blockchain’s potential to generate meaningful value to health care by facilitating data sharing between organizations in circumstances where computational trust can overcome a lack of social trust that might otherwise prevent valuable cooperation. Although there are genuine prospects of using blockchain to bring about positive transformations in health care, the successful development of blockchain for health care applications faces a number of very significant, multidimensional, and highly complex challenges. Early experience suggests that blockchain is unlikely to rapidly and radically revolutionize health care.ConclusionsThe successful development of blockchain for health care applications faces numerous significant, multidimensional, and complex challenges that will not be easily overcome, suggesting that blockchain technologies are unlikely to revolutionize health care in the near future.

Will Drones Revolutionize Health Care and Create Landmark Moments in History?

Will Drones Revolutionize Health Care and Create Landmark Moments in History?

Tracking the Presence of Software as a Medical Device in US Food and Drug Administration Databases: Retrospective Data Analysis.

Software as a medical device (SaMD) has gained the attention of medical device regulatory bodies as the prospects of standalone software for use in diagnositic and therapeutic settings have increased. However, to date, figures related to SaMD have not been made available by regulators, which limits the understanding of how prevalent these devices are and what actions should be taken to regulate them. The aim of this study is to empirically evaluate the market approvals and clearances related to SaMD and identify adverse incidents related to these devices. Using databases managed by the US medical device regulator, the US Food and Drug Administration (FDA), we identified the counts of SaMD registered with the FDA since 2016 through the use of product codes, mapped the path SaMD takes toward classification, and recorded adverse events. SaMD does not seem to be registered at a rate dissimilar to that of other medical devices; thus, adverse events for SaMD only comprise a small portion of the total reported number. Although SaMD has been identified in the literature as an area of development, our analysis suggests that this growth has been modest. These devices are overwhelmingly classified as moderate to high risk, and they take a very particular path to that classification. The digital revolution in health care is less pronounced when evidence related to SaMD is considered. In general, the addition of SaMD to the medical device market seems to mimic that of other medical devices.

Telemedicine via Continuous Remote Care: A Proactive, Patient-Centered Approach to Improve Clinical Outcomes.

The COVID-19 pandemic has revolutionized health care for patients and providers alike. Telemedicine has moved from the periphery of our health care system to center stage more rapidly than anyone could have envisioned. Currently, virtual care has quite effectively replicated the traditional health system’s care delivery model and reimbursement structure—a patient makes an appointment, then sees a physician (except with video or phone replacing in-office visits) who makes a care plan, and the patient and physician meet again at a later timepoint to assess progress. Replicating this episodic care paradigm virtually has been invaluable for delivering care swiftly during the COVID-19 pandemic; however, we can and should do more with the connectedness and convenience that telemedicine technology enables. Continuous remote care, with a data-driven, proactive outreach to patients, represents a decisive step forward in contrast to the currently available episodic, reactive, patient-initiated care. In the context of continuous remote care, patient biometric and symptom data (patient entered and connected data) are assimilated in real time by artificial intelligence–enabled clinical platforms to bring physicians' and other health care team members’ attention to those patients who need intervention, whether this is via medication adjustments, acute care management, or lifestyle coaching. In this paper, we discuss how an innovative continuous remote care approach has improved outcomes in another deadly pandemic—type 2 diabetes mellitus.

Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging.

Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine Learning (ML) has had several applications in the most diverse knowledge areas. It is conceived as a technology with huge potential to revolutionize health care. In this context, we developed ML models to evaluate their ability to distinguish an individual's sex from MPI assessment. We used 260 polar maps (140 men/120 women) to train ML algorithms from a database of patients referred to a university hospital for clinically indicated MPI from January 2016 to December 2018. We tested 07 different ML models, namely, Classification and Regression Tree (CART), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Adaptive Boosting (AB), Random Forests (RF) and, Gradient Boosting (GB). We used a cross-validation strategy. Our work demonstrated that ML algorithms could perform well in assessing the sex of patients undergoing myocardial scintigraphy exams. All the models had accuracy greater than 82%. However, only SVM achieved 90%. KNN, RF, AB, GB had, respectively, 88, 86, 85, 83%. Accuracy standard deviation was lower in KNN, AB, and RF (0.06). SVM and RF had had the best area under the receiver operating characteristic curve (0.93), followed by GB (0.92), KNN (0.91), AB, and NB (0.9). SVM and AB achieved the best precision. Our results bring some challenges regarding the autonomy of patients who wish to keep sex information confidential and certainly add greater complexity to the debate about what data should be considered sensitive to the light of the GDPR.

Implementation of e-health innovative technologies in North Lebanon hospitals.

E-health is considered the single most important revolution in health care since the advent of modern medicine. It is an emerging field of medical informatics, used in the organization and delivery of health services and information. It is expected to improve various aspects of health care. To achieve universal health coverage in Lebanon, and to explore e-health implementation in Northern Lebanese hospitals and factors influencing the adoption of recent specialized technology in e-health. We adopted an exploratory research method based on a semi-structured questionnaire. The main 14 hospitals in Northern Lebanon were chosen for face-to-face interviews to assess and measure behaviour and knowledge regarding e-health and its implementation. Data were analysed using SPSS software, followed by SWOT analysis. The hospitals surveyed had partly implemented and continuously tried to apply some e-health technologies, but there were no real medical records for patients. Various challenges were faced for full e-health technology implementation in Lebanon: primarily cost, followed by some personnel resistance, lack of legislation and common standards, and the necessity for continual training. The majority of hospitals are conscious of the importance of e-health technology and the urgent need to implement these new methods. After the legislative rules are issued, the financial aspects remain the major handicap to full implementation. A major role must be played by the academic institutions to provide appropriate education on e-health in their programmes for future graduates.

Confidentiality, privacy, and general practice: GPDPR and the brave new world of 'big data'.

Big data is big business. Health data is valuable for the public good — it can save and improve lives — but it can also be monetised for private profit. The UK’s NHS data has been valued at £10 billion. GP data — with details of everything from medications to diagnoses that include mental illness, abortions, sexually transmitted diseases, suicide attempts, and addictions — is arguably the most detailed, valuable, and sensitive of all. Health data provides the fodder for machine learning, a subfield of artificial intelligence, that some argue will revolutionise health care, making it more efficient and effective. But ultimately, data emerges from the stories and information that patients bring to healthcare professionals and how these are interpreted, recorded, and acted on. This means that we should give careful consideration in incorporating patient and public views in the decisions made about their data. We should also consider whether an excessive focus on digital data may blind us or distract us from the valuable ‘data’ that patients as whole human beings bring to the consultation — their values, feelings, relationships, unique life stories, and particular circumstances. Nor should it belittle the importance of relationship-based care. GPDPR, GP Data for Planning and Research — not to be confused with GDPR — General Data Protection Regulation (which is about protecting not sharing your data) — is set to happen on 1 September 2021, having been (briefly) paused from the earlier date of 1 July after criticisms from various institutions including the British Medical Association and Royal College of General Practitioners, as well as a threat of legal action. The GP data from 55 million people will be ‘pseudo-anonymised’, but can in fact be readily de-anonymised, according to data experts. Many GPs — the designated ‘data processors’ who carry the responsibility to …

The Effect of 24/7, Digital-First, NHS Primary Care on Acute Hospital Spending: Retrospective Observational Analysis.

BackgroundDigital health has the potential to revolutionize health care by improving accessibility, patient experience, outcomes, productivity, safety, and cost efficiency. In England, the NHS (National Health Service) Long Term Plan promised the right to access digital-first primary care by March 31, 2024. However, there are few global, fully digital-first providers and limited research into their effects on cost from a health system perspective.ObjectiveThe aim of this study was to evaluate the impact of highly accessible, digital-first primary care on acute hospital spending.MethodsA retrospective, observational analysis compared acute hospital spending on patients registered to a 24/7, digital-first model of NHS primary care with that on patients registered to all other practices in North West London Collaboration of Clinical Commissioning Groups. Acute hospital spending data per practice were obtained under a freedom of information request. Three versions of NHS techniques designed to fairly allocate funding according to need were used to standardize or “weight” the practice populations; hence, there are 3 results for each year. The weighting adjusted the populations for characteristics that impact health care spending, such as age, sex, and deprivation. The total spending was divided by the number of standardized or weighted patients to give the spending per weighted patient, which was used to compare the 2 groups in the NHS financial years (FY) 2018-2019 (FY18/19) and 2019-2020 (FY19/20). FY18/19 costs were adjusted for inflation, so they were comparable with the values of FY19/20.ResultsThe NHS spending on acute hospital care for 2.43 million and 2.54 million people (FY18/19 and FY19/20) across 358 practices and 49 primary care networks was £1.6 billion and £1.65 billion (a currency exchange rate of £1=US $1.38 is applicable), respectively. The spending on acute care per weighted patient for Babylon GP at Hand members was 12%, 31%, and 54% (£93, P=.047; £223, P<.001; and £389, P<.001) lower than the regional average in FY18/19 for the 3 weighting methodologies used. In FY19/20, it was 15%, 35%, and 51% (£114, P=.006; £246, P<.001; and £362, P<.001) lower. This amounted to lower costs for the Babylon GP at Hand population of £1.37, £4.40 million, and £11.6 million, respectively, in FY18/19; and £3.26 million, £9.54 million, and £18.8 million, respectively, in FY19/20.ConclusionsPatients with access to 24/7, digital-first primary care incurred significantly lower acute hospital costs.

Cloud based IoT Smart Healthcare System for Remote Patient Monitoring

INTRODUCTION: Covid-19 has exposed the necessitate for the rapid acceptance of increasingly pioneering digital health technologies, especially remote health monitoring. The digital revolution in Healthcare is dynamic ease of use of inexpensive concern solutions, enhancing patient care, reducing complications, improving effectiveness, and authorizing healthcare decision-makers with intelligence insight at the point of care.OBJECTIVES: The primary objective of this work is to depict the need to recognize wearable sensors as a prerequisite for supporting paradigms in monitoring patients in real-time and enabling access information from the cloud.METHODS: Internet of Things (IoT) is the association of substantial objects where information and communication tools connect various embedded devices to the internet for gathering and switching over data. The combinations of embedded devices with cloud servers recommend extensive pertinence of IoT to several areas of human life. This paper has proposed a method through cloud-based IoT healthcare sensors to formulate patient monitoring remotely. In combination with the implementation of various inbuilt capabilities, internet-enabled heterogeneous wearable sensors can be used for the collection of biomedical data to transmit patient data directly to cloud severe systems to monitor health remotely.RESULTS: Smart healthcare monitoring includes channels of communication, embedded internal and external sensors, IoT server, and cloud storage. The health parameters activities are done at various levels of refining named application layer, management layer, network layer and layer of device. Different data sensors have been collected by wireless media from nodes. It is saved as an unstructured dataset in the cloud. For security with username and password, a patient database is created. Authorized individuals have access to the cloud in order to monitor cloud sensor data in data log, analogue log, digital input and digital output.CONCLUSION: Patient physical parameters like heart beat respiration ,high temperature and stress are calculated via sensors and can be progressed by WIFI unit in the cloud. From this healthcare practitioner will analyze medical to have effective medication. For face-to-face consultation between doctors and patients, the video feature can be added in the future work.