How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

Cognitive Computing Creates Value In Healthcare and Shows Potential for Business Value

This research paper examines cognitive computing relative to how businesses in healthcare may use cognitive systems to analyze big data to create a competitive advantage. It explains the underlying technologies, such as machine learning and natural language processing, and gives an overview of the technology driving the world\u27s most popular cognitive computing system, IBM Watson. It examines case studies that show businesses applying cognitive systems to derive value from big data and discusses how this may be used to develop business value and provide analysis for strategic processing. It also touches on challenges of cognitive computing. The paper concludes with lessons learned and future research

Big data analytics in the healthcare industry: A systematic review and roadmap for practical implementation in Nigeria

Introduction: The introduction of digitization of healthcare data has posed both challenges and opportunities within the industry. Big Data Analytics (BDA) has emerged as a powerful tool, facilitating data-driven decision-making and revolutionizing patient care. Purpose: The research aimed to analyze diverse perspectives on big data in healthcare, assess BDA's application in the sector, examine contexts, synthesize findings, and propose an implementation roadmap and future research directions. Methodology: Using an SLR protocol by Nazir et al. (2019), sources like Google Scholar, IEEE, ScienceDirect, Springer, and Elsevier were searched with 18 queries. Inclusion criteria yielded 37 articles, with five more added through citation searches, totaling 42. Results: The study uncovers diverse healthcare viewpoints on big data's transformative potential, precision medicine, resource optimization, and challenges like security and interoperability. BDA empowers clinical choices, early disease detection, and personalized medicine. Future areas include ethics, interpretable AI, real-time BDA, multi-omics integration, AI-driven drug discovery, mental health, resource constraints, health disparities, secure data sharing, and human-AI collaboration. Conclusion: This study illuminates Big Data Analytics' transformative potential in healthcare, revealing diverse applications and emphasizing ethical complexities. Integrated data analysis is advocated for patient-centric services. Recommendation: Balancing BDA's power with privacy, guidelines, and regulations is vital. Implementing the Nigerian healthcare roadmap can optimize outcomes, address challenges, and enhance efficiency. Future research should focus on ethics, interpretable AI, real-time BDA, and mental health integration

Value co-creation and potential benefits through big data analytics: Health Benefit Analysis

Big data analytics in healthcare context is often studied from a technical point of view. In the field of strategic management, researchers have indicated a research gap in how big data analytics create business value. This study examines how big data and advanced analytics generate potential benefits and business value for the healthcare service provider, and value for the individual patients and population health. In addition, the effects of advanced analytics to the value co-creation practices and actors in healthcare ecosystem are studied. The theoretical framework used for the purpose is the big data analytics-enabled transformation model which is adapted to answer the research questions. The study is conducted as a single case study. The studied case is the Health Benefit Analysis (HBA) tool. The empirical data is collected in eight semi-structured interviews with participants of the tool development project. Using the HBA tool reveals several paths-to-value chains. The most evident path shows how using advanced analytics affects the personalized care practice by enabling a more interactive service process between the health professionals and patients. It denotes a business scope redefinition as patients are now being interpreted as essential actors in the value co-creation of their own health outcomes. The benefits that arise from the advanced analytics are of several dimensions; operational, managerial, strategic, and organizational. Using the HBA tool generates strategic business value for the healthcare service provider as a differentiator that contributes to gaining competitive advantage compared to other service providers not using this innovation. Value emerges for the individual patient as improved patient experience and better health outcomes. Population health gains most value from the reduced health inequalities. The evolving value co-creation practices set requirements for the healthcare ecosystem actors as they need to conform to new practices with patients and other professionals from other sectors and levels of the ecosystem. The healthcare work and service culture need to develop and adapt to new tools, related processes, and a more diversified professional base, including health analysts and other new professionals. To conclude, it can be claimed that advanced analytics of healthcare big data contributes to the shift to value-based healthcare.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Big data analytics and internet of things for personalised healthcare: opportunities and challenges

With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future

Improving Patient Care with Machine Learning: A Game-Changer for Healthcare

Machine learning has revolutionized the field of healthcare by offering tremendous potential to improve patient care across various domains. This research study aimed to explore the impact of machine learning in healthcare and identify key findings in several areas.Machine learning algorithms demonstrated the ability to detect diseases at an early stage and facilitate accurate diagnoses by analyzing extensive medical data, including patient records, lab results, imaging scans, and genetic information. This capability holds the potential to improve patient outcomes and increase survival rates.The study highlighted that machine learning can generate personalized treatment plans by analyzing individual patient data, considering factors such as medical history, genetic information, and treatment outcomes. This personalized approach enhances treatment effectiveness, reduces adverse events, and contributes to improved patient outcomes.Predictive analytics utilizing machine learning techniques showed promise in patient monitoring by leveraging real-time data such as vital signs, physiological information, and electronic health records. By providing early warnings, healthcare providers can proactively intervene, preventing adverse events and enhancing patient safety.Machine learning played a significant role in precision medicine and drug discovery. By analyzing vast biomedical datasets, including genomics, proteomics, and clinical trial information, machine learning algorithms identified novel drug targets, predicted drug efficacy and toxicity, and optimized treatment regimens. This accelerated drug discovery process holds the potential to provide more effective and personalized treatment options.The study also emphasized the value of machine learning in pharmacovigilance and adverse event detection. By analyzing the FDA Adverse Event Reporting System (FAERS) big data, machine learning algorithms uncovered hidden associations between drugs, medical products, and adverse events, aiding in early detection and monitoring of drug-related safety issues. This finding contributes to improved patient safety and reduced occurrences of adverse events.The research demonstrated the remarkable potential of machine learning in medical imaging analysis. Deep learning algorithms trained on large datasets were able to detect abnormalities in various medical images, facilitating faster and more accurate diagnoses. This technology reduces human error and ultimately leads to improved patient outcomes.While machine learning offers immense benefits, ethical considerations such as patient privacy, algorithm bias, and transparency must be addressed for responsible implementation. Healthcare professionals should remain central to decision-making processes, utilizing machine learning as a tool to enhance their expertise rather than replace it. This study showcases the transformative potential of machine learning in revolutionizing healthcare and improving patient care