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The Theme - Biomedical Research

Harvard_3171
(Harvard University - Joyce Yang)

 

 "Biomedical Research in A New Health ICT Framework"

 

  <Draft>

 

Overview

In the 21st century, groundbreaking research and discovery in the Biomedical Research are more interdisciplinary than ever. Biomedical Research represents the (basic and applied) research activities in the areas of Medicine, Public Health, Pharmacology, Biology, Biochemistry, Chemistry, Physics, Mathematics, Statistics, Engineering, New Materials, Information and Communication Technology (ICT), and Health-related topics. These scientists work to understand the biological principles that govern the function of the human body, to discover the mechanisms of disease, and to find innovative ways to treat or cure disease by developing advanced diagnostic tools or new therapeutic strategies for physicians - especially new smart devices that could help transform the detection, prevention, and management of disease. The increased longevity of humans over the past century can be significantly attributed to advances resulting from Biomedical Sciences Research. 

We’re at the cusp of a major revolution in understanding the workings of the human body. According to Google Ventures, the following top eight life sciences technologies are the most promising and will transform medicine: Artificial Intelligence, Understanding the Brain, Reinventing Antibiotics, Battling Cancer, Genetic Repair, Understanding the Microbiome, Organ Generation, and Stem Cells. For example, stem cell research has the potential to revolutionize the way we treat many conditions, including degenerative diseases for which few effective treatments currently exist. Stem cell research is rapidly advancing towards potential therapeutic applications such as tissue and organ replacement, disease modelling and drug testing. Dr. Aaron Ciechanover, Nobel Prize in Chemistry 2004, characterizes 21st century medicine with four P’s: it’s personalized, predictive, preventive – and it should be participatory.

New Media and Fog Computing

The rise of new media has increased communication between people all over the world and the Internet. It allows people to on-demand (cloud computing) access to content anytime, anywhere, on any digital device, as well as interactive user feedback, and creative participation. New media allows the real-time generation of new, unregulated content, including (at least for now) Internet, blogs, websites, computer multimedia (e.g., medical audio or speech, real-time or recorded video, high resolution still image, and so forth), pictures, and other user-generated media. The physical world is becoming a type of information system. Fog computing will change the information technology industry in the next decade. It enables key applications in wireless 5G, the Internet of Things (IoT), and big data. Fog computing and networking present a new architecture vision where distributed edge and user devices collaborate with each other and with the clouds to carry out computing, control, networking, and data management tasks. The IoT may more likely be supported by fog computing, in which computing, storage, control and networking power may exist anywhere along the architecture, either in data centers, the cloud, edge devices such as gateways or routers, edge equipment itself such as a machine, or in sensors. Fog computing distributes the services of computation, communication, control and storage closer to the edge, access and users.

Internet of Things (IoT)

The vision of “Internet of Things (IoT)” with more than 50 to 200 billion connected devices (containing embedded sensors and actuators, etc.), linked through wired and wireless networks by year 2020, will see profound changes in the way people, businesses and the society interact. However, one of the central-most challenges facing IoT (still very immature, and a long way to go) is the enablement of seamless interoperability between each connection (i.e., lack of interoperability at the application level). Merely connecting "things" gives you very little or almost no benefit. The vast majority of "things" and data that we might be collecting may have no relevance to the decision we want to make. A business case is needed to justify the investment. It’s about getting the right data to the right person at the right time to make the right decision. Once we started collecting right data for decision making, applications like ERPs (Enterprise Resource Plannings) and analytics applications were re-written or enhanced to use this data. This is now Internet of Everything (IoE) - data, people, things and processes. In the IoT, hundreds of incompatible protocols co-exist today. This makes the integration of data and services from various devices extremely complex and costly. It is clearly time to consider how to expand the IoT beyond product silos into Web-scale open ecosystems based on open standards, including those for identification, discovery, and service interoperability across platforms from different vendors.

Web of Things (WoT)

The enablement or implementation of IoT/IoE is done using Web of Things (WoT). The Web of Things (WoT) is a term used to describe approaches, software architectural styles and programming patterns that allow real-world objects to be part of the World Wide Web. Similarly to what the Web (Application Layer) is to the Internet (Network Layer), the Web of Things provides an Application Layer that simplifies the creation of Internet of Things applications. Rather than re-inventing completely new standards, the Web of Things reuses existing and well-known Web standards used in the programmable Web, semantic Web, the real-time Web and the social Web.

WoT focuses on software standards and frameworks such as REST, HTTP and URIs to create applications and services that combine and interact with a variety of network devices. The Web will enable a transition from costly monolithic software to open markets of apps. "The Web of Things (WoT) vision", going beyond the Internet of Things and where real world objects and cloud services interact through the Web, will produce large volumes of data related to the physical world, and intelligent solutions are required to enable connectivity, inter-networking, and relevance between the physical world and the corresponding digital world resources. In the Web of Things, any device can be accessed using standard Web protocols. Connecting heterogeneous devices to the Web makes the integration across systems and applications much simpler. The use of Web technologies is expected to dramatically reduce the cost for implementing and deploying IoT services. Correspondingly, WoT brings into focus a wide variety of challenges and opportunities while paving a way to a variety of exciting applications for individuals to industries. The reality of a hyper-connected world is here today.

Life Sciences and ICT

Life sciences and ICT are coming together to revolutionize scientific and medical discovery; comprising: acquisition, transmission, processing, storage and retrieval of biomedical and health information. Telemedicine, predictive diagnostics, wearable sensors and a host of new apps will transform how people manage their health. With today’s high-throughput sequencing technology, it’s much easier to generate genomic data than to transform it into information or knowledge that can improve human health. We are at the beginning of the genomics revolution. The promise of genomics is to revolutionize treatment of disease, to personalize treatment. The unprecedented abundance of medically relevant data (e.g. molecular, cellular, organismal, ecological, behavioral, clinical), from detailed information about genes and genetic diseases and the relative efficacy of drugs in diverse patient populations, to three-dimensional imaging of living cells giving researchers a more detailed and accurate spatial visualization of the interplay of cells and their components, is driving the use of quantitative methods in medicine. For example, recent advances have made 3D imaging (e.g., enabling 3D images of living organisms to be obtained with greater speed and precision) a valuable tool for many applications, such as cell biology, developmental biology, neuroscience and cancer research. These new approaches will improve our understanding of finding better diagnostics, treatments and therapies for diseases. 

Artificial Intelligence (AI) and Machine Learning (ML)

We are currently struggling to find the right information either about lifestyle or therapeutic decisions. This could change with Artificial Intelligence (AI) and Machine Learning (ML). AI and ML represent the next big wave in supercomputing. Like a human, a ML application learns by experience and/or instruction. By applying the advanced ML capabilities, patients and healthcare providers benefit from more rapid and thorough analysis to translate DNA insights, understand a person’s genetic profile and gather relevant information from medical literature to personalize treatment options for patients. Healthcare providers will also share knowledge they glean from treating patients. This is key. In the era of Electronic Health Records (EHR), it is possible to examine the decision outcomes made by doctors. By enabling researchers at the institutions to mine a much larger store of data, they can more easily spot patterns and identify best practices. When it comes to effectiveness of ML, more data almost always yields better results—and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and ML in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators. 

Machine Learning (ML) is the future of pharma. The current drug discovery process – too lengthy and very expensive. It can take up to 15 years to translate a drug discovery idea from initial inception to a market ready product. Industry is currently said to spend well over $1 billion per drug. That’s partly because all the drugs that didn’t make it have to be paid for. ML in particular, present the pharmaceutical industry with a real opportunity to do R&D differently, so that it can operate more efficiently and substantially improve success at the early stages of drug development. The drug discovery process and the researchers that drive the pipelines can be greatly aided by the latest innovations in ML technology. The average biomedical researcher is dealing with a huge amount of new information every day. It’s estimated that the bioscience industry is getting 10,000 new publications uploaded on a daily basis – from across the globe and among a huge variety of biomedical databases and journals. So it’s impossible for researchers to know, let alone process, all of the scientific knowledge out there relating to their area of investigation. What’s more, without the ability to correlate, assimilate and connect all this data, it’s impossible for new usable knowledge – which can be used to develop new drug hypotheses – to be created. ML have a vital role to play in augmenting the work of drug development researchers so that an informed, first analysis of the mass of scientific data can be conducted in order to form essential new knowledge. The technology will also help in terms of the industry’s selection of patients for clinical trials and enable companies to identify any issues with compounds much earlier when it comes to efficacy and safety. So the industry has much to gain by adopting ML approaches. It can be used to good effect to build a strong, sustainable pipeline of new medicines. 

The list below is by no means complete, but provides a useful lay-of-the-land of some of ML’s impact in the healthcare industry: Scaled Up/Crowdsourced Medical Data Collection, Disease Identification/Diagnosis, Diagnosis in Medical Imaging, Personalized Treatment/Behavioral Modification, Treatment Queries and Suggestions, Drug Discovery/Manufacturing, Clinical Trial Research, Radiology and Radiotherapy, Smart Electronic Health Records, Epidemic Outbreak Prediction, Robotic Surgery, and Automatic Treatment or Recommendation.

Wireless 5G

Mobile revolution has changed everything. The next generation of wireless networks, the ‘fifth generation’ or 5G, will change the way we communicate, the way we do business, the way we do everything! The impact of 5G will extend well beyond telecommunications: by connecting people, machines and things on a massive scale. The 5G wireless technology will provide the backbone for IoT (e.g., Health IoT) that greatly improves data transfer speeds and processing power over its predecessors. This combination of speed and computing power will enable new applications for mobile technologies, especially in health care. 5G networks open up new avenues for the delivery of health care. Instead of bringing patients to a doctor for treatment, 5G networks can connect patients and doctors from across the globe. Digital imaging can be sent anywhere in the world for analysis, expanding access for patients who live far away from health care providers. The point of care will move rapidly into the home, With ubiquitous mobile broadband-enabled internet access, connectivity and networking are becoming completely independent of location. 

The rapid evolution of wireless technologies coupled with advances in related fields such as biosensor design, low power battery operated systems, diagnosing and reporting for intelligent information management, genome sequencing, and advances in analytic software, etc. has opened up many new applications for wireless systems in medicine (uHealth – ubiquitous Health). With the inclusion of Electronic Health Care, Point-of-Care technologies, E-Health and M-Health protocols, and personalized healthcare/medicine, the medical informatics area is entering into another era of massive amount of information. The medical and health care information databases would lead to new knowledge bases, discoveries in medical research, engineering oriented developments and clinical translational research and practices. Data sharing is reaping huge rewards in the fight against cancer too. At the individual level, health tracker apps on our mobile devices are sending data to health care providers to improve patient care and provide early-warning signs in at-risk patients. Early detection and monitoring is critical to mounting effective cancer treatments (and speed is critical because cancer treatment is a race against fast-replicating cells.). By combining implantable cancer detectors (using new methods in molecular imaging and micro-electromechanical systems (MEMS) technologies) with wireless data transmission technologies, new tools and emerging technologies for continuous monitoring during and after cancer treatment to signal remission and relapse or even trigger micro-scale drug delivery systems for automatic therapeutic interventions are on the horizon. 

A Digital Revolution in Health Care

The convergence of several trends -- wider adoption of electronic health records, advances in mobile technology, and payment reform -- is accelerating the pace of change in how healthcare is delivered. A digital revolution in health care is speeding up. Telemedicine, predictive diagnostics, wearable sensors and a host of new apps will transform how people manage their health. The age of digital health/medicine is here. Social media and mobile devices have swiftly become more ubiquitous in the healthcare industry and integrated into daily life (for example, our incessant need for instantaneous medical diagnoses via the web). And digital health tools like smartphones certainly do make it easier. As patients continue to gain access and share healthcare information (such as sleep patterns, heart rate, activity levels, blood oxygen, glucose levels, and even stress, etc.) through various forms of online media (such as via a smartphone, smartband, or glucose monitor, etc.), healthcare organizations have started to use social media (i.e., Internet-based applications) to better connect with patients and their community on a wide range of healthcare issues. This initiative could appeal to anyone with an interest in a healthier lifestyle or, more specifically, to patients who suffer from chronic illnesses like heart disease, diabetes, stroke, hypertension (high blood pressure). 

Chronic diseases are long-term medical conditions that are generally progressive and are a significant cause of illness and death. These patients need closer health status monitoring and the study of their biometric data could allow physicians to foresee their crises. A number of technologies can reduce overall costs for the prevention or management of chronic illnesses. These include devices that constantly monitor health indicators, devices that auto-administer therapies, or devices that track real-time health data when a patient self-administers a therapy. Because they have increased access to high-speed Internet and smartphones, many patients have started to use mobile applications (apps) to manage various health needs. For example, having an EKG-accurate (Electrocardiogram) monitor strapped to (a mass amount of) wearers (or patients) throughout the day could be hugely beneficial to the study of heart disease. Accurate EKG data generated throughout a normal day and having the data collected sent automatically to scientists and doctors, combined with other metrics, could help researchers understand more about heart performance. Software could be used to warn users of a heart attack or stroke days in advance. The future versions of Apple Watch heart rate monitor (and other similar watches) could include better technology to aid in this goal. The “Internet of Things and Beyond” will make health monitoring, diagnostics and treatment more personalized (i.e., personalized and precision medicine), timely and convenient, while also lowering costs.

Big Data and High-Performance Analytics

High-performance analytics, high-speed connections and affordable data storage have made large data-sharing projects possible in health care too. New medical breakthroughs as well as the effective management of health care in the future requires the integration of data and methods across the different realms of fundamental research, development of therapeutics (e.g., nanotechnology-based cancer therapeutics), health care practice, and massive high performance computing infrastructure. The large volume of data coming from all the different health-monitoring devices and constituting the ‘individualome’ requires large-capacity hardware infrastructures for storage and processing. Such resources can be implemented locally at the data centers associated with hospitals or deployed on secured cloud computing or virtual private server computing environments. In particular, given the sensitivity of the information, only secured and HIPAA-approved architectures should be considered.

In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. With these combined data sources from hundreds of studies and dozens of companies, researchers – from large academic institutions, commercial organizations, or small research labs in remote corners of the world – are finding deeper insights than ever before, getting answers faster, reducing duplication of effort and improving efficiency.

The Goal

The goal will be to care for more people in more affordable and effective ways, and to recognize that health and care management needs (a patient-centric business model) to occur wherever the patient is, not just in hospitals or physician offices. Overall, telemedicine is expected to grow rapidly.

 

<updated by hhw: 8/27/17>

 

 

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