This folder will be kept up to date as we move forward. analysts expect that by 2020, almost half a billion smart wearable devices will have been sold. For today’s health-conscious consumer, a Fitbit, Garmin or Jawbone activity tracker is a must-have accessory. Despite the fast-growing market, only 10% of those consumers are using the product daily. This is an opportunity for innovative life science companies to tap into the market and create value-added services for consumers.To shine a light on personal health management for users, life sciences companies are working to provide solutions that combine treatment and technology by leveraging existing products and introducing medical-grade wearables.Life science companies need to consider these factors as they deliver medical-grade wearables to patients:The potential for medical-grade wearablesMedical-grade wearables offer a range of potential applications from real-time disease diagnosis and monitoring, to delivering insights on patient experiences, to improving product quality and innovation. Developing medical-grade wearables that will appeal to consumers, while providing health benefits, begins with identifying and defining unmet patient needs. The next step to develop a wearable that effectively meets a plethora of patient needs – whether it’s a drug-device combination associated with a medical app or as part of a broader patient-engagement initiative.Philips has this sort of vision-a broader patient-engagement initiative The data Part of the development process for creating a medical-grade wearable is to examine how clinical trial data integrate with real-world evidence, to determine the patient transition from controlled testing to everyday use. With the understanding of how technologies can be implemented as treatment options for conditions like diabetes and obesity, device creators can integrate feedback and validate the outcomes. The insights gathered from trial data can be used to improve product characteristics, design, and implementation.Tier 1 is at this stage? The regulations The development of regulations around security and confidentiality have become obstacles to the creation of medical-grade wearables. Currently, the regulatory path is in flux as the global agencies, like the FDA, develop risk-based frameworks for health information technologies, which will include wearables. However, regulations are evolving, as both medical and wearable technologies are changing at a rapid pace, particularly if these devices are providing healthcare professionals with data in real time. Positioning health wearables as clinical-grade medical devices can ensure data integrity and compliance. However, regardless of the device’s classification, data collection and supply must always be accomplished in a way that ensures security of the data and informed consent from the patient. The resulting data output from medical-grade wearables can be directed through secure cloud‑based environments which can address privacy, security or confidentiality concerns, like anonymity. The customer For the health wearables market to have a successful and profitable future, life science manufacturers need to create and deliver customer-centric products. Creating and extending pharmaceutical solutions to encompass a more holistic approach to healthcare will ultimately deliver sustainable value to patients and healthcare systems. Building mutually-beneficial relationships between patients, physicians, providers and producers allows life science companies to improve therapeutic outcomes while reducing the cost of care for the patient and the healthcare system.Will big pharma make wearables? Beyond the hype Wearables have the potential to enable a more holistic approach to healthcare that has the potential to revolutionise the health sciences industries. By embracing such a holistic approach to patient care, sciences like genomics, personalised medicine and molecular biology can integrate with emerging technologies powered by the Internet of Things (IoT), creating what could become the next wave of life-saving drugs and devices. To enable this transformation, life science companies need to leverage the power of these devices when utilised in a disease-related context. This will not be simple or easy, given the magnitude of legal, regulatory and technical issues, but the opportunity to positively impact healthcare outcomes around the globe has never been greater Philips has Asia as a KEY geography for them, most will not have any idea of that.
Artificial Intelligence and the Move Towards Preventive Healthcare December 13, 2017 by Editorial Team Leave a CommentIn this special guest feature, Waqaas Al-Siddiq, Founder and CEO of Biotricity, discusses how AI’s ability to crunch Big Data will play a key role in the healthcare industry’s shift toward preventative care. A physicians’ ability to find the relevant data they need to make a diagnosis will be augmented by new AI enhanced technologies. Waqaas, the founder of Biotricity, is a serial entrepreneur, a former investment advisor and an expert in wireless communication technology. Academically, he was distinguished for his various innovative designs in digital, analog, embedded, and micro-electro-mechanical products. His work was published in various conferences such as IEEE and the National Communication Council. Waqaas has a dual Bachelor’s degree in Computer Engineering and Economics, a Master’s in Computer Engineering from Rochester Institute of Technology, and a Master’s in Business Administration from Henley Business School. He is completing his Doctorate in Business Administration at Henley, with a focus on Transformative Innovations and Billion Dollar Markets.In October 2000, Google co-founder Larry Page made a luminary prediction: “Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing.” Fast forward seventeen years later, and artificial intelligence (AI) and Big Data are the new buzzwords in healthcare. According to a new Market Study report, the healthcare artificial intelligence segment is projected to see a staggering 40 percent compound annual growth rate (CAGR) between 2017 and 2024, resulting in a $10 billion market focused on medical imaging, diagnostics, robotic personal AI assistants, drug discovery, and genomics. When Larry Page compared artificial intelligence to the “ultimate search engine,” he was essentially speaking of AI’s ability to crunch massive amounts of data. AI’s deep learning algorithms are designed to detect features in huge, disparate datasets that are not discernible to entire teams of data scientists. Second, these deep learning algorithms can be trained to provide specific information, or in Page’s words, “[AI] would understand exactly what you wanted, and it would give you the right thing.”Clinical AI implementation promises a healthcare system that is preventive rather than reactionary. Today, patients are often diagnosed with a chronic condition, such as cancer or diabetes, when it’s too late to reverse the progression of the condition. Treatment plans with late stage diseases are expensive and debilitative. Patients are poorly equipped with feedback and insights into their own health conditions, and so are less proactive about making healthy lifestyle choices and adhering to physician advice. Consequently, preventing the start of chronic disease and managing the disease post-diagnosis has become the focus of preemptive measures in healthcare. Here, AI offers a promising solution. A preventive healthcare system will capitalize on AI’s ability to collect, compile, and analyze data to facilitate three progressive and ultimately integrated stages of learning. First, AI will enable a broad scope of learning that will aid in more effective and efficient disease diagnostics based on historical data. The insights gleaned from this massive survey of Big Data will be utilized by physicians to further train AI. Second, AI will harness historical data and augment it with real-time patient data to provide feedback to patients. Finally, as AI begins to learn how patients react differently based on real-time data, it will create personalized and predictive feedback for each patient. Broad Learning for Effective and Efficient Diagnostics AI is distinguished by its analysis capabilities and its deep learning algorithms. These capabilities can be deployed to traverse massive amounts of data and detect a few variables across hundreds of thousands of data points that are specific to certain conditions and diseases. In the context of broad learning, AI holds the potential to aid in the diagnostic process and identify problems before they become serious.Researchers from Sutter Health and the Georgia Institute of Technology are using deep learning to analyze electronic health records to predict heart failure before it happens. Initial results have empirically demonstrated that this AI application can accurately predict heart failure one to two years early. Philadelphia’s Thomas Jefferson University Hospital has researchers training AI to identify tuberculosis on chest X-rays, an initiative which may help screening and evaluation efforts in TB-prevalent areas with limited access to radiologists.By leveraging public historical data sets licensed from research groups such as the Mayo Clinic or the American Heart Association, with patient-specific data such as medical history, individual symptoms, and prescribed medications, AI will enable physicians to identify a specific condition while ruling out others. Then, they’ll be able to recommend the best course of treatment based on the individual patient. Augmenting Broad Learning with Real-Time Patient Data Once AI’s broad learning can identify and assist in the diagnosis of a specific condition, it can leverage historic data to develop treatments plans that are interactive, driving patient engagement. Doc.ai is using Blockchain technology to collect masses of medical data globally and generate insights from that information. Then, through machine learning, the data collected will be analyzed and processed to provide personalized feedback to users about their own medical issues. Studies have shown that ongoing feedback is a key factor in driving patient engagement. A 2012 trial found that when remote patient monitoring devices were given to patients with chronic conditions, the number of emergency room visits, hospital admissions, and one-year mortality rates decreased. The devices used in this study provided ongoing feedback for patients by reminding them when tests were due, offering educational videos, and creating a graphic chart detailing their recent clinical results.It is just as easy to envision a heart disease patient equipped with a medical-grade wearable device that provides real-time metrics detailing the effectiveness of an exercise regime or medication based on the prior week’s metrics. This demonstrable, measurable feedback could encourage the patient to adhere better to a treatment plan or to consult with physicians between appointments to improve regimens and future results.For AI to be truly “intelligent,” it needs to become more effective with experience, and this experience cannot occur with information pulled from historic datasets alone. AI requires copious amounts of data for optimization, and medical-grade remote monitoring technologies that continuously stream patient data are the ideal mechanism for this purpose.Philips has this vision This is because these devices provide constant connectivity (through expanded broadband) combined with the capability to collect clinically accurate, medically verifiable data. Specific Learning for Personalized and Predictive Feedback Perhaps the most valued quality of AI is its ability to dynamically learn and improve over time. As AI collects individual patient data, and begins to learn how patients react differently to feedback, it can begin tailoring feedback so that it’s personalized and predictive. Such feedback is the foundation upon which a preventive healthcare system is built. Medtronic’s new IBM Watson-powered Sugar.IQ diabetes app uses real-time continuous glucose monitoring and insulin information from Medtronic pumps and glucose sensors to provide diabetes patients with personalized insights. The AI-based app is designed to learn from a patient’s own information input; its Glycemic Assist feature enables users to inquire about how specific foods or therapy-related actions and events impact their personal glucose levels. By following trends, Sugar.IQ can then help users discover the impact that these items have on their glucose levels. Patient inputs also enable Sugar.IQ to learn and issue blood glucose level predictions by assessing the patient’s current situation and the risk of glucose levels falling outside safe thresholds. Medical-grade wearables with AI could create predictions based on a patient’s daily biometrics. If a heart disease patient is prone to developing a rapid heartbeat after X minutes of walking, then the medical device would make a prediction and alert the patient to avoid exceeding the recommended minutes of walking. AI could also exercise predictive capabilities by learning what kinds of feedback instigate adherence for a patient and then applying that feedback to improve the patient’s disease management, almost like a personal health coach. When patients can follow their own progress, and see how certain choices have a direct impact on their health, they are more likely to adhere to treatment plants, engage in their healthcare, and change their behavior. The Future of AI in Healthcare Ultimately, the effectiveness of AI in healthcare will be directly predicated on its access to Big Data—both to historical data sets and EHRs as well as to real-time, continuous, patient-specific data from remote monitoring technologies. Training AI to reach its maximum potential is an interactive process in which physicians and patients are key players. AI applications must become fully integrated into existing healthcare systems, and must function within a “residency program” of sorts, in which they perform analytics on real-time patient data while being overseen by a physician. In this way, the algorithms will learn simultaneously from the data and from the physician’s oversight to hone their capabilities. AI’s ability to learn from experience and offer personalized and predictive feedback to patients and physicians is its greatest value proposition for preventive healthcare systems which improve diagnostics while catalyzing patient adherence through engagement. The integration of both broad and specific AI learning applications, the latter implemented in remote patient monitoring devices, represents the tantalizing future of preventive healthcare that beckons on the horizon.The Tier 1 device + The European b2b are both aimed here?What have I learned?Philips efforts here are NOT, lets just make a wearable, its a whole, well integrated platform from edge device to the cloud.100s or 1000s of man yrs of effort to put it all together.It does explain time going by.Security of this info? A lot of work will go into this, especially in the USA.For a company like Philips it is NOT just the west, it is a global effort.
Great post, very informative. I think a lot of people will find this very useful.Keep post in coming future as well!!! crimsontopreviewer
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