Covid-19 overshadows all our lives. But just because we are battling the same storm does not mean we are all in the same boat. The people likely to be most exposed to coronavirus, and to suffer most from the resulting interventions, are disproportionally those already disadvantaged in society (the #ethnic-breakdown-of-deaths-by-age-and-sex">startling disparity between white and BAME outcomes is one tragic example). This inequality predates the pandemic. Even in the NHS — the world’s most mature system of universal healthcare coverage — morbidity and mortality are strongly correlated with where a person lives and how much they earn. If these dark clouds have a silver lining, it is that the pandemic has also highlighted the most effective tools to repair this imbalance: chief among them is digital innovation. Below are some key lessons that healthcare professionals, politicians and scientists should consider as they tackle discrimination in the digital age, and examples of what can be achieved in a variety of care settings when innovation (digital and otherwise) is applied with care. Mobile tools for fairer access to consultations It seems increasingly common to hear that the last ten months have seen ‘ten years of innovation’. But while services like video consultations have certainly become more widespread, start-ups like DrDoctor and Zesty had already developed a variety of remote care tools, from appointment management apps and text updates, to self-monitoring questionnaires and paperless referrals. Solutions like these are convenient: improving patient choice, shortening waiting times, and significantly reducing overhead costs. But there is a risk, says Lloyd Price, Co-Founder of Zesty, that “technology always starts by benefitting the most affluent, educated, and digitally mature people in society” because “the focus tends to be on cutting edge platforms. Innovation can’t just be for the early adopters at the ‘top of the pyramid’; it needs to be for everyone. ” By taking advantage of near-ubiquitous technology like mobile phones, we might finally see all sections of society able to effectively manage their own care. Shift workers could flexibly report on chronic conditions; those living with financial insecurity could consult a GP without incurring holiday or travel costs; and homeless people could receive timely diagnoses without needing to rely on a discriminatory paper-based approach. Universal benefit does not require universal uptake. Those unwilling or unable to engage online still benefit by ‘delegating access’ to their carers or when automated booking systems free up clinic phone lines. Tom Whicher, Founder and CEO of DrDoctor, believes this combined approach is vital. “Digital transformation can help everyone by bridging gaps in health inequality. But the way to do it is not to force digital products on everybody and alienate them from their own health journey. New tools need to work with, rather than replace, existing behaviour. ” Integrated analytics to improve planning of local services Effective case management, while a perk for patients, is vital for healthcare organisations. This was the challenge faced by boroughs in North West London when they were hit hard by the first wave of the pandemic. Facing a critical incident, North West London Collaboration of Clinical Commissioning Groups (the local body responsible for planning and procuring clinical services) enlisted the support of analytics company TrueCue to help them combine, sort, and understand the vast array of information they were dealing with. In only four weeks, TrueCue and NWL launched a reporting platform to integrate data from almost 20 source systems and display it in near-real-time using a variety of ‘dashboards’. Clinicians and managers are now using these reports to spot trends earlier, identify high risk groups, and plan interventions. James Don-Carolis, Managing Director at TrueCue, emphasises the importance not only of combining inputs, but also sharing outputs widely. “Integrating disparate data sources is vital for holistic, evidence-based decision making. But you only see maximum value if the information can be accessed by teams in multiple care settings, including hospital staff, central leadership teams, and GPs. This collaboration is the only way of managing caseloads without losing a patient-centric view. ” An online portal to help ageing patients access care Other organisations like North of England Commissioning Support Unit(NECS) have made the opposite transition: taking existing innovations and re-purposing them to deal with Covid-19. In 2019, NECS — one of a handful of units providing business intelligence, consulting, and back-office support to other parts of the NHS — launched 'Capacity Tracker', an online portal to address the significant delays that exist when moving patients from hospital to a care setting. There was historically no way of searching care homes and matching the requirements of individual patients, but Capacity Tracker allows social care teams to find and fill vacancies more easily, saving time and anxiety, and freeing up critical hospital beds. As well as working with more than 15,000 care homes, it is now being used across other settings such as community rehabilitation and drug and alcohol services. Stephen Childs, Managing Director at NECS, believes this growth in uptake “is an example of how local innovation can be rapidly adopted at a national level without the high initial overheads of a top-down approach”. The UKGovernment has used it to support the national pandemic response (providing real-time insight on PPE, workforce requirements and flu immunisations) and it been adopted for the same reason by a primary health network in Australia. Diversity works both ways Just as technical innovations can support under-served populations, increasing diversity also improves technical innovation. One example is in genomics, where recent advances have opened vast new possibilities for the diagnosis and management of rare diseases. The UK is a world leader, and NHS Trusts, private organisations, and Genomics England have begun partnering with other countries to share their expertise. However, most genomic studies focus on participants of white European ancestry, and this ethnicity bias is impeding the equitable sharing of clinical benefits and the advance of scientific knowledge. Dr Saghira Malik Sharif, Principal Genetic Counsellor for Prenatal Genetics at Leeds Teaching Hospital, believes that the way to increase participation is through broader education. “The key issue,” she says, “is to develop researchers with cultural — as well as clinical — competencies. If we can identify the practical barriers to engagement, and address people’s concerns through effective communication, we will build a system able to embrace advances and improve health outcomes for all patients. ” Education and engagement need to be driven by, as well as for, the groups in question. Katherine Mathieson, CEO of The British Science Association, has extensive experience working with minority communities. “I often hear calls for more ‘representation’, but that’s not the problem,” she says (indeed, the BAME population is actually #by-ethnicity">over-represented among the NHSworkforce). “Instead, we need to ask different questions; to be led by local communities as well as launching centrally funded ‘outreach’ programmes. ” Be slow to be fast So why not start immediately? First, because the health and care sectors are complex: strictly evidence-based, highly regulated, and often decentralised. The NHS, however coordinated it may be, is an ecosystem of hundreds of organisations. While transformations have historically suffered from rigid centralised planning and mass rollout, some preparation is needed. Secondly, we are already in the midst of excessive change and the fatigue it brings with it. Even healthcare data, despite its value, can be hard to manageand even harder to build public trust in. Without firm foundations, any rush to innovate may lead to more problems than solutions. There must be a period of reflection, where pockets of innovation (like accessibility and inclusive prescribing) can be expanded, and new ideas sifted. This is one aim of the ‘NHS Reset’ campaign being championed by NHS Confederation, The Health Foundation, and England’s Academic Health Science Networks. As Richard Stubbs, CEO of the Yorkshire and Humber AHSN, and one of the Reset campaign leads explains, “We have seen rapid and fundamental changes in the way the health system functions. Now we have a time-limited opportunity to evaluate these changes and sustain the ones we expect to deliver value in the future before we slip back into previous ways of delivering care. ” Cause for optimism? Despite the clear and present challenges to health equality, there is cause for hope. Mobile approaches are widening access, reducing costs, and improving user experience. Clinical commissioning is being enhanced through real-time system integration and information sharing. Across health and care, innovators are working quickly, building locally before scaling nationally, and investing in essential education and community engagement. If we are to see the dawn of truly universal healthcare coverage, leaders in the NHS and globally will need to take time to balance the trade-offs, learn from the past, and share those lessons with each other. “I'm more optimistic than I've ever been," says Hassan Chaudhury, Digital Health and Care Lead at Healthcare UK in the Department for International Trade. “There is so much digital innovation in the UK, from NHS organisations to start-ups to well established private companies. If we harness it in the right way, we can revolutionise patient care forever. ” Source: https://integratedcarejournal.com/newsdit-article/3dfc360fc019a6ae1e9a4ad5108759ac/

The Big Unlock refers to the massive opportunity unfolding in healthcare from the digitization of patient medical records and the emerging tsunami of data from a wide range of medical and non-medical sources, especially unstructured data such as text and speech. Collectively, these data will enable the unlocking of insights for improving the quality of care, reducing costs, and creating new sources of value for healthcare providers, consumers, and technology firms. In this book, Paddy Padmanabhan offers a framework for understanding the competitive landscape of digital health solutions and a set of key principles for success in the digital health marketplace. Custodians, Enablers, Arbitrageurs and Innovators Custodians : these are the big electronic health record (EHR) vendors like Epic and Cerner who have the data and the workflow. Enablers : big technology firms such as Google, GE Healthcare, Microsoft, Salesforce and IBM’s Watson Health business have built technology stacks that integrate multiple emerging and traditional data sources, including EHR systems, and have incorporated some proprietary data sources as well, such as images in the case of GE Healthcare. Arbitrageurs : are mostly technology agnostic consulting firms such as Accenture and Deloitte, as well as India-heritage firms such as Wipro and Infosys, that rely on information and labor-arbitrage models to build digital experiences from scratch using the preferred technology tools that exist in health systems. Innovators : are typically startups that have come up with a whole new way of addressing an existing problem with technology-enabled healthcare experiences, or for filling a gap in the current healthcare experience with technology. Along with a shift towards value-based care, a digital transformation is under way in health care. However, health care enterprises are having a hard time keeping up with advances in information technology. Organizations that could once spend months or years developing a strategy to deliver solutions now must implement changes on a near real-time basis. Complicating matters is the emergence of new data sources, new technology architectures and models, and new methods to analyze an avalanche of data. This book provides a framework for understanding the competitive landscape for digital health and advanced analytics solutions that are harnessing data to unlock insights. It reveals a set of key principles, or universal themes, for success in the digital health marketplace. Whether you’re a health care information technology specialist, a digital health startup or technology firm with a strategic focus on health care, a venture capitalist, or just interested in the industry structure and the emerging technology landscape in health care, you’ll learn how to grow revenue and profits while creating a sustainable competitive advantage. Take a key step in navigating the exciting transformation of health care, and harness the power of data and analytics with The Big Unlock. About the Author Paddy Padmanabhan is the founder and CEO of Damo Consulting, a health care growth strategy firm based in Chicago. He has worked extensively with CIOs and CXOs in the healthcare sector as a consultant and trusted advisor to large global organizations and technology startups. Paddy is a regular columnist for CIO magazine and publishes opinion pieces on healthcare IT and digital health strategies. He has written for other technology publications and has been featured in Becker’s Hospital Review and Modern Healthcare. Paddy is a frequent speaker at leading industry conferences and leads panel discussions on technology markets, emerging technologies and digital health. He is actively involved in industry organizations such as HIMSS. Paddy earned a BS in chemical engineering from the Indian Institute of Technology and an MBA in marketing and finance from the Indian Institute of Management. He is an alum of the CMI executive program of the University of Chicago’s Booth School of Business. He has worked in health care information technology for two decades, serving in leadership roles at global tech firms Wipro and Accenture, as well as with Silicon Valley startups that saw successful exits. Source : https://www.amazon.co.uk/Big-Unlock-Harnessing-Businesses-Value-Based/dp/1480854603/ref=sr_1_1?ie=UTF8&qid=1526716012&sr=8-1&keywords=the+big+unlock

Please email lloyd@healthcare.digital for Press and Media Enquiries 1) CNBC - Shaking Up The Healthcare Sector 2) Meeting the Prime Minister's health advisors at 10 Downing Street 3) The Economist - Will patients shop around online for a Doctor? 4) StartUp Health Insider 5) Sky News Live 6) World Health Summit 7) Hot Topics - Scaling a Health Tech company 8) HealthTechX Europe 2017 Conference 9) Millennial 2020 London 2017 10) The Times newspaper 11) CB Insights - Health On-Demand: 42 Mobile Startups Disrupting Healthcare https://www.cbinsights.com/research/mobile-healthcare-startups-list/ 12) Daily Telegraph – “Apps Which Are Changing Healthcare” http://www.telegraph.co.uk/business/open-economy/new-technologies-transforming-healthcare/

In 2016, the Estonian government was looking for new and innovative ways to secure the health records for its 1.3 million residents. It turned to blockchain technology. It may or may not revolutionize the internet, depending on who you ask. But without doubt, the incorruptible distributed ledger technology known as blockchain has proved its potential for applications where data integrity is critical. Away from the frenetic world of cryptocurrency, developers and investors are searching high and low for new areas they can unleash blockchain’s power. Finance, logistics and electronic copyrights top the list as the most talked about. The same goes for healthcare in Estonia, specifically with regard to securing patients’ electronic health records. Still, practical cases have proven hard to identify. Estonia, home to one of the world’s most e-savvy governments, has become the first country to dabble in using blockchain for healthcare on a national scale. In 2016, the Estonian E-Health Foundation launched a development project aimed at safeguarding patient health records using blockchain technology in archiving related activity logs. Estonia has become the first country to use #blockchain for healthcare on a national scale. “We are using blockchain as an additional layer of security to help us ensure the integrity of health records. Privacy and integrity of healthcare information are a top priority for the government and we are happy to work with innovative technologies like the blockchainto make sure our records are kept safe,” said Artur Novek, the foundation’s Implementation Manager and Architect. It’s not the health records that are secured using blockchain, but the log files that record all the data processing activities performed on those records. Securing such highly private health information from prying eyes is only part of the goal. It is just as important to mitigate every risk that life-critical personal data could become compromised by an unwitting or malicious hacker or a fraudulent insider. Digital watchdog An archive or ledger with a backbone built on blockchain technology can record and timestamp each instance of access or each change to a patient’s electronic records. Its cryptographic hash functions create an unchangeable audit trail that can be monitored. It also guarantees that the most recent version of the record is always used. In Estonia’s case, a private digital ledger has been integrated into the ledger at the E-Health Foundation to record and track patient medical data. One of the goals was to have real-time awareness of the integrity of the stored data, so that administrators could see any breaches and act immediately to limit damage. Mostly though, it’s about barring would-be electronic intruders and keeping alterations from happening in the first place. The project in Estonia is still a work in progress, but I believe that other nations that use electronic health records could soon follow suit. Source : https://nortal.com/us/blog/blockchain-healthcare-estonia/

Twenty years from now, we predict that the health care system we know today will look completely different. We are already beginning to see the early stages of this transformation. Health care consumers are starting to demand greater transparency, accessibility, and personalization…and that trend is likely to continue. Consumers will want automated, actionable health insights that come from smart artificial intelligence (AI) applied to interoperable data that is seamless and integrated across all platforms and applications. Many consumers will shop for modular and personalized health coverage and will receive care (mostly) where they are. A wide range of companies—from inside and outside of the health care sector—are making strategic investments that could be the foundation for a future of health that is defined by radically interoperable data, open and secure platforms, and consumer-driven care. These organizations illustrate early innovations that can help personalize health care, enable consumers to make more informed decisions about their health, and leverage AI and other emerging technologies to harness and share data. Consider some of the announcements that companies have made in just the past few months: In late November, Amazon Web Services announced the formation of Comprehend Medical, which uses natural language processing to mine unstructured medical text from electronic medical records (EMRs) and other patient data.1 The company says the data could eventually help consumers make more informed decisions about their own health and improve patient recruitment for clinical trials. During the Exponential Medicine 2018 conference last fall, executives from Human Longevity, Inc. outlined their Health Nucleus program, which maps out a participant’s entire genome. For about 15 percent of participants, the program identified actionable responses that could increase their life expectancy from one to nine years.2 Apple, Inc.* is moving toward a device-enabled future in which consumer devices not only play a greater role in health care, but also put patients in control of their health information. The company’s Apple Watch Series 4 wearable device, for example, includes an EKG sensor that can alert the wearer to irregular heart rhythms.3 Apple’s HealthKit platform provides a central repository of each user’s health and medical data, and the CareKit framework for apps helps users understand and manage their medical conditions. Apple’s ResearchKit framework could help medical researchers gather data. DeepMind, an AI research firm Google acquired in 2014, recently said it is using Google’s scale and experience to create an AI-powered app that can assist nurses and doctors. This app could wind up being similar to Google Maps, but instead of helping drivers get from Point A to Point B, it could help clinicians navigate clinical pathways. Microsoft Healthcare says it intends to combine cloud computing, machine learning, natural language processing, omics data, and AI to tackle cancer.4The company’s researchers expect the technology to help oncologists sift through mountains of biologic research data to determine effective, individualized treatment. During a December conference, the CEO of Bind Benefits explained the concept of “on-demand” health coverage, which his company is making available to self-insured employers. Copayments are priced on a sliding scale based on quality outcomes and the setting where care is delivered. These examples point to a broader shift in life sciences and health care that is only beginning to form and signal a new future of health. I expect the pace of innovation in this sector is going to accelerate in 2019. Jump ahead 20 years…How will innovation play out? We don’t expect to have eliminated disease entirely by 2040, but by using actionable health insights driven by interoperable data and smart AI, we should be able to identify illness early and intervene much more quickly. This can pave the way for a future focused more on well-being rather than treatment. Largely replacing the traditional industry segments we have now (health systems and clinicians, health plans, biopharmaceutical companies, and medical device manufacturers) we expect new roles, functions, and players will emerge. We expect the future of health will be made of three broad segments. Within these segments, we envision 10 sector-agnostic archetypes: Data and platforms can generate the insights needed for personalized, always-on decision-making in the new health ecosystem. These archetypes can serve as the backbone for the health care ecosystem of tomorrow. 1. Data conveners: Data-gathering organizations will have an economic model built around1. aggregating and storing individual, population, institutional, and environmental data. These data can be used to drive the future of health. 2. Science and insights engines: Some organizations will likely have an economic model driven by their ability to derive insights and define the algorithms that power the future of health. These organizations will likely conduct research, develop analytical tools, and generate data insights that go far beyond human capabilities in care delivery. 3. Data and platform infrastructure builders: This new world of health will need infrastructure and platforms that can serve highly empowered and engaged individuals in real time (someone will need to lay the pipes). Well-being and care delivery represent new virtual and physical communities that can provide consumer-centric delivery of products, care, wellness, and well-being. 4. Health product developers: The economic model of these organizations is driven by their ability to enable well-being and care delivery. While there will continue to be organizations that develop products, those products won’t likely be limited to pharmaceuticals and medical devices. They can also include software, applications, and wellness products. 5. Consumer-centric health (virtual home and community): Along with companies that develop health products, other organizations can provide the structure that supports virtual communities. 6. Specialty care operators: Two decades from now, we will likely still have disease, which means we will still need specialty care providers and highly specialized facilities where patients can receive care. 7. Localized health hubs: While there will be some specialty care, most health care will likely be delivered in localized health hubs. Localized health hubs can serve as centers for education, prevention, and treatment in a retail setting. Additionally, local hubs can connect consumers to virtual, home, and auxiliary wellness providers. Care enablement includes the connectors and facilitators that can make the new health engine run. 8. Connectors and intermediaries: These are the logistics providers that will run the just-in-time supply chain, facilitate device and medication procurement operations, and get the product to the consumer. 9. Individualized financiers: Unlike the health insurers of today, these organizations will create the financial products that individuals can use to navigate their care. These organizations will likely offer tailored modular and catastrophic care-coverage packages. They can drive reductions in care costs by leveraging advanced risk models, consumer incentives, and market power. 10. Regulators: While we will still have regulators, we probably won’t view them as governmental traffic cops. They will set the standards for business transactions. The regulators of the future can influence policy in an effort to catalyze the future of health and drive innovation while promoting consumer and public safety. Tomorrow’s consumers can expect the exponential changes I’ve outlined above—and they’ll vote with their feet and their wallets. Legacy stakeholders should consider whether to disrupt themselves or isolate and protect their offerings to retain some of their existing market shares. We anticipate that successful companies will identify and compete in one or a few of the new business archetypes above, taking into consideration their existing capabilities, core missions and beliefs, and expectations for the future. Doug Beaudoin, vice chairman, US Life Sciences & Health Care leader, Deloitte LLP Source : https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/health-care-current-january15-2019.html

Chatbots is a popular term in the customer service industry; and one I’m becoming increasingly familiar with as I complete my online Christmas shopping, and book my last-minute break in the sun. We’re beginning to recognise chatbots as a service that’s always friendly, helpful, and most importantly, available 24/7. It’s not hard to see why this innovation triggers excitement in the NHS. Chatbots can ease staffing issues, triage patients to the correct services, and source intelligent information for patients to their smart devices whenever they need it. For the non-developer community, let’s go back to basics quickly. A chatbot refers to a computer programme that can read and respond in human language. It’s able to answer queries and do basic tasks as directed. Chatbots are programmed to understand specific questions and give an instant solution. AI and machine learning are the underlying concepts that support these chatbots and make them possible. The chatbot opportunity in healthcare Chatbots are gradually being adopted into the healthcare industry and are generally in the early phases of implementation. Market research estimates that the global chatbot market will reach $1.23 billion by 2025. One example where chatbots could have a huge impact on the NHS is reducing the burden on incorrectly used services in the NHS, through effective triage and providing intelligent information at times of need to patients. Last year at 46 hospital trusts, more than 50% of patients left A&E without receiving any medical treatment at all. At four trusts that figure reached a staggering 90%. Patients need information at hand, and advice on where to access the right services for what they need, whenever they need it. The Microsoft Healthcare Bot service can help the NHS build and deploy an AI-powered, compliant, conversational healthcare experience at scale. The service combines built-in medical intelligence with natural language capabilities, extensibility tools, and compliance constructs, allowing the NHS to give patients access to trusted and relevant healthcare services and information. Give it a try here. Conversation-as-a-service The best examples of the NHS utilising chatbot technology come from the centre itself. NHS England has undertaken a digital transformation for the funding of patient care. The current approach is problematic as there’s a high reliance on subjective decisioning for the funding submission. There’s currently a 75% rejection rate for funding of a continuous care package, which is governed by statutory rules published by the Department of Health and Social Services. Microsoft partner Df2020 has prototyped an alternative solution based on scripted chatbots. This is designed for consistent, timely, and more accurate decisions, underpinned by a full audit trail for transparency and traceability. This Conversation-as-a-Service approach has challenged conventional thinking within NHS England as has replaced form-filling that is dependent upon subjective decisions. Df2020’s innovation is being considered as part of the wider strategy including a local versus national solution, and a joined-up solution with Social Services. “The complexity of our processes means that we can’t be certain that patients get high quality and reliable responses from us. We’re making lots of traditional changes including improving training and seeking to simplify procedures, but we have concerns that this won’t be enough on its own. That’s why we’ve started to explore how AI can help. Our work with Df2020 has helped us understand what’s possible. It’s building our confidence and expertise to test the technology in our environment.” Chatbots supporting mental health Code4Health is an initiative supported by NHS England and NHS Digital to enable the best use of digital tools and technology. One project showcases the importance of Chatbots in mental health. The demand for access to mental health services is higher than the capacity to service it, so effective triage is vital. In one month over 1.2 million people accessed NHS mental health services. Currently clinicians triage using questionnaires when a person presents to an NHS service. The Mental Health Chatbot, based on Microsoft Azure, replicates existing practice and meets NICE guidelines. Clinicians can gather information at scale, in a way that it sensitive to patient needs and confidentiality, in an out-of-clinic setting that can be preferred by many experiencing mental health problems. “A person concerned about their mental health no longer needs to attend an NHS site to share clinically relevant data. We estimate that the chatbot will save two hours of a patient’s time, and one hour of a clinician’s, for every instance of triage.” This is just the beginning of the rise of chatbots. If done well, they will seamlessly integrate into our lives to help us self-manage our care and improve our experience within the NHS. Source : https://cloudblogs.microsoft.com/industry-blog/en-gb/health/2018/12/17/chatbot-opportunity-healthcare/

Four years ago, when we launched our first bio fund, the idea that software would make an impact in drug discovery sounded like science fiction; the idea it could transform the patient experience in healthcare a fool’s errand. Fast forward to 2020, and it’s not just accepted but broadly embraced, igniting an explosion of new startups and big investments from established incumbents. Tech, biotech, and our healthcare system are merging—into what we call simply “bio”. And whether for pharma, hospitals, or investors, bio is now officially the hot new thing. That’s where they’ve got it wrong. Bio is not the “next new thing”—it’s becoming everything. Software is now affecting not just how we do not just one thing—cloning DNA, or engineering genes—but how we do it all across the board, blurring lines, breaking down traditional silos, changing our processes and business models. In other words, technology today is enhancing all our existing tools and data, affecting every decision we make, from research to development to deployment—and how we access, pay for, and experience healthcare. And it’s not just software. What is technology really? It’s principles and process. It’s a shift to an engineering mindset for relentless iteration and constant improvement; modular components that can be remixed and reused, and improvements that accrue and compound over time. Tech gives us tools beyond just software—continuous data streams to describe our health, circuits to program cells, scalpels to edit DNA, and the ability to create programmable, living medicines. Our focus is not just on the groundbreaking outputs of this shift, from novel gene and cell therapies to digital therapeutics and virtual care models, but on the underlying approach and drivers that created those breakthroughs. This is why it doesn’t work to simply tack on AI, or just insert tech into an established company. In order to re-program entire systems and re-imagine new approaches to massive challenges, whether those are biological, or man-made, you need to rethink the process from the ground up. We are now approaching a new ability to rethink bio’s biggest problems, from intractable diseases and massive inefficiencies or disparities in an overburdened healthcare system, to what we eat, what we wear, what we build, even how we heal our planet. And we will do this by using our most advanced technological tools, as well as the engineering principles that brought them to us. This revolution is being driven by a new breed of founder who is multi-disciplinary and not scared to challenge the status quo or conventional wisdom. Whether they think of themselves as protein engineers or cell engineers, data scientists or software engineers, they are using technology and engineering processes to build products instead of chasing the lucky strike of discovery. They are creating the new generation of companies at the intersection of tech + biotech + healthcare that will attack the most audacious goals in healthcare: using AI to discover new drugs; using data to provide a real-world view into how patients are progressing through their healthcare journeys; programming our cells to fight disease; improving how we care for our sickest patients; even re-engineering the entire process for how we develop, test, and get drugs to patients quickly and at lower cost. Because of who they are, this new breed of companies also requires a multidisciplinary approach to find, attract and vet these opportunities. This has been our DNA from the beginning, at the intersection of engineering and biology, of tech and medicine. MDs, PhDs, computer scientists, data scientists, biological engineers—all working side by side. This is how the next generation of bio and healthcare companies will be built. And this is why we are excited to announce a new $750M bio fund: Bio Fund III. Source: https://a16z.com/2020/02/04/bio-fund-iii-announcement/

During sleep onset, a window of opportunity arises in the form of Hypnagogia, a semi-lucid sleep state where we begin dreaming before we fall fully unconscious. To access this state, we developed Dormio, the first interactive interface for sleep, designed for use across levels of consciousness. Here we present evidence for a first use case, directing dream content to augment human creativity. The system enables future HCI research into Hypnagogia, extending interactive technology across levels of consciousness. Introduction: Sleep is a forgotten country of the mind. A vast majority of our technologies are built for our waking state, even though a third of our lives are spent asleep. Current technological interfaces miss an opportunity to access the unique, imaginative, elastic cognition ongoing during dreams and semi-lucid states. In turn, each of us misses an opportunity to use interfaces to influence our own processes of memory consolidation, creative insight generation, gist extraction, and emotion regulation that are so deeply sleep-dependent. In this project, we explore ways to augment human creativity by extending, influencing, and capturing dreams in Stage 1 sleep. It is currently impossible to force ourselves to be creative because so much creative idea association and creative incubation happens in the absence of executive control and directed attention. Sleep offers an opportunity for prompting creative thought in the absence of directed attention, if only dreams can be controlled. Source: https://www.media.mit.edu/projects/sleep-creativity/overview/ Source: https://dam-prod.media.mit.edu/x/2018/04/20/alt_chi_submitted_version.pdf

When is software a medical device? The MDR sets out clear rules on this, which are broadly similar to the previous position but provide helpful clarification. The key test relates to the purpose of the software. The MDR states: Example: software used for diagnosis, or predicting disease Example: medical records software used as a storage or retrieval system Example: apps which monitor fitness levels, diet and wellbeing Software in its own right, when specifically intended by the manufacturer to be used for one or more medical purposes, is a medical device. Software for a general purpose, even if used in a health care setting, is not a medical device. Software for lifestyle or well-being purposes is not a medical device. Many companies offering medical software will be familiar with the existing rules, but it is important to know that whilst the MDR principles are broadly similar to the old rules, the MDR introduces new requirements on classification of devices. These new classification requirements will affect many companies offering online health care tools, diagnosis and prediction tools, and software and AI. Digital Health in the United Kingdom: The New Regulatory Environment Under the Medical Device Regulation The MDR and other recent developments in the health care regulatory landscape pose new opportunities and challenges for medical software companies and investors in digital health in the United Kingdom. Although the regulatory landscape is changing, there is still no clear framework for reimbursement or tariffs for digital health tools and AI in the United Kingdom. This article sets out key considerations for digital heath developers and investors as they navigate this dynamic environment. In Depth Investment in artificial intelligence (AI) and digital health technologies has increased exponentially over the last few years. In the United Kingdom, the excitement and interest in this space has been supported by NHS policies, including proposals in the NHS Long Term Plan, which set out ambitious aims for the acceleration and adoption of digital health and AI, particularly in primary care, outpatients and wearable devices. Although these developments are encouraging to developers, there is still no clear framework for reimbursement or tariffs for digital health tools and AI. At the same time, the plethora of new technologies has led to increased calls for regulation and oversight, particularly around data quality and evaluation. Many of these concerns may be addressed by the new Medical Device Regulation (MDR) and other regulatory developments. In fact, there is some risk that while regulatory landscape is moving quickly, the pricing environment is still a way behind. In May 2020, the new MDR will change the law and process of certification for medical software. The new law includes significant changes for digital health technologies which are medical devices. In March 2019, the National Institute for Health and Care Excellence (NICE) also published a new evidence standards framework for digital health technologies. The Care Quality Commission (CQC) already regulates online provision of health care, and there are calls for wider and greater regulation. The government has also published a code on the use of data in AI. Digital Health Technologies and the MDR The new MDR will mean a significant change to the regulatory framework for medical devices in the European Union. As with the previous law, the MDR regulates devices through a classification system. The new regime introduces new rules for medical software that falls within the definition of device. This will mean significant changes for companies that develop or offer medical software solutions, especially if their current certification has been “up-classed” under the MDR. Key Takeaways for Investors in Digital Health Tools Companies and investors in digital health should: Check whether their digital health tool is a medical device and caught by the MDR. Review whether their proposed or current certification has changed under the new MDR. Assess the new requirements on certification and general safety in the MDR as those apply to digital health tools. Ensure that development programmes have taken into account the likely time period for certification, which may be lengthy due to shortages in Notified Bodies. Build in the cost of any increased certification requirements and costs of compliance. Take into account the CQC guidance for digital and online health care, and check whether the health tool and service will require CQC registration. If the digital health tool is to be marketed and sold to NHS organisations, understand and take into account the NICE evidence standards framework for digital health technologies. Whilst these are stated to be complementary to the new MDR, the framework has different assessments and evidence requirements. Have a clear strategy about access to market and reimbursement of technologies. Consider the government’s Code of Conduct for data driven health and care technology. Understand the implications of a “no deal” Brexit, especially if the technology is marketed across Europe. Medical Devices Regulation 2017: FAQ The MDR sets out some specific criteria around the classification of medical software, which is likely to mean that many devices which currently qualify as class I under the existing regime (MDD) may move up a class under the MDR. The new rules are as follows: Software intended to provide information used to take decisions with diagnosis or therapeutic purposes are Class IIa. Software, where these decisions have an impact that may cause a serious deterioration of a person’s state of health or a surgical intervention, are Class IIb. Software, where these decisions have an impact that may cause death or an irreversible deterioration in a person’s health, are Class III. All other software is Class I. Whilst some software may remain as Class I, many devices are likely to move up a class level. This will mean more onerous responsibilities and increased rigour (and time) in relation to the certification of the device. When do the new rules come into force for medical software? The MDR came into force on 25 May 2017 but does not apply fully until May 2020. The MDR includes transition provisions for existing devices. The general principle is that certificates issued by Notified Bodies under the MDD will remain valid, but for varying periods depending on the certification. Many digital health tools are currently self-certified as Class I devices. If medical software continues to be a Class I device under the MDR, there is likely to be little practical difference for manufacturers, although manufacturers will need to comply with the increased obligations under the MDR. However, for current Class I devices which are “up-classed” under the MDR, the position is a little less clear. There are no express provisions in the MDR which deal with up-classing, and the express transitional provisions protect certificates “issues by Notified Bodies” rather than to self-certified devices. The prudent interpretation is that self-certification does not survive an up-class. This means that manufacturers of up-classed medical software will need to comply with the MDR and obtain certification from the MDR from May 2020. What is the position of Notified Bodies and the timing of certification? Notified Bodies are the organisations responsible for the issue of certificates for medical devices under both the MDD and the MDR. There are currently serious concerns about the workloads of Notified Bodies and the impact this may have on the timing of certification. Some estimates suggest that the workload of Notified Bodies has increased seven-fold as a result of MDR changes. This issue is exacerbated because certain Notified Bodies have also announced their intention not to pursue designation under the MDR, and no new organisations have applied to be Notified Bodies under the MDR. What are the new data, safety and evaluation requirements under MDR? Manufacturers must, of course, be mindful of data protection laws that apply to the development, trialling and use of medical devices. The MDR sets out greater rigour about the evaluation of software and the datasets used to verify and validate medical devices. Manufacturers will need to be able to show how their device has been tested to demonstrate conformity, in particular with regards to safety. For software, this includes detailed information about test design, study protocols, methods of data analysis in addition to data summaries and test conclusions, in particular about software verification and validation (describing the software design and development process and evidence of the validation of the software, as used in the finished device). For clinical evaluations, the manufacturer also must ensure that the data is evaluated and relevant to the risks and purpose of the device in question. In addition to the general safety requirements that apply to all medical devices, the MDR sets out some specific requirements for medical software. These new requirements include instructions for use of the software, which must contain minimum requirements for hardware and IT security measures and protections against unauthorised access. When does software qualify as an accessory? In some cases, software may also be an accessory (and therefore must comply with the certification rules for accessories). To qualify as an accessory, the software would have either (i) to enable the medical device to function, or (ii) to specifically and directly assist the medical functionality of the device. This consideration is especially relevant to the interoperability of software used as components or in conjunction with medical devices. There is no guidance on how these new rules in the MDR will work in practice, and it may sometimes be difficult to assess whether software is a device itself or an accessory. It is also possible that where different medical devices are offered on the market as a single system, multiple certifications may be required. How will a “no-deal” Brexit affect digital health regulation? Under the terms of the current proposed withdrawal agreement, the European Union and the United Kingdom will continue to operate under the existing recognition of certifications across Member States. However, if there is no deal, those transitional mutual recognition provisions will not apply. This is relevant to UK digital health providers that rely on their UK certification across Europe. From the date of Brexit, a UK manufacturer of a medical device will need to ensure that its device is certified by an EU Notified Body before it is placed on the EU market. Source : https://www.natlawreview.com/article/digital-health-united-kingdom-new-regulatory-environment-under-medical-device

Dr Matthew Fenech. The development of AI tools for healthcare took a significant step forward this week, with the publication of the AI-specific extensions to the CONSORT(Consolidated Standards Of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) statements. These are intended to set new reporting standards for clinical studies on AI tools. As one of the experts who contributed to the development of these guidelines, I wanted to share some thoughts on what they mean and why they matter. What are CONSORT and SPIRIT? The CONSORT and SPIRIT statements, first published in 2010 and 2013 respectively, are efforts from the clinical research community to come together and agree on what basic facts about clinical trials should be made known. CONSORT focuses on reporting the results of clinical trials, while SPIRIT focuses on publishing the clinical trial protocol before the trial is conducted. Both take the form of a checklist: anyone reporting the results of a trial, or publishing its protocol, can go through the relevant checklist to make sure they've considered and shared all the information that the rest of the community needs to judge if it’s a well-conducted, rigorous study. Following a hugely collaborative process across the AI community and industry, the new guidelines – which were published on September 9, 2020 – will play an important role in ensuring standardization of clinical AI research, which will in turn help to increase trust in AI as it becomes more widely used in healthcare settings. I was privileged to be one of the experts recruited to participate in the Delphi process, which was used to arrive at a consensus on the items that need to be included in the extended checklists. The group also included other members of academia, industry, and fellow healthcare professionals to give a comprehensive view of the issue from those that are working to deliver AI-powered solutions to users. I also reviewed pre-final versions of the guidelines, providing comments and editing for clarity. Why AI needs its own guidelines Clinical research is obviously hugely important, but to have maximal impact, high reporting standards and transparency are required. Doctors and patients will not base decisions on the results of research they don’t trust. The CONSORT-AI and SPIRIT-AI extensions do an excellent job of bringing the new field of AI into the orbit of established clinical research, while acknowledging the unique and nuanced challenges it presents. For instance, many forms of AI are dependent on huge datasets. Researchers and the community at large need to know how this data is sourced and used in clinical research to trust the results. There are always strong incentives for researchers to report only positive results about their clinical trials using AI. Selective reporting could lead to negative patient outcomes and bad policy decisions. The CONSORT-AI and SPIRIT-AI statements encourage teams to conduct rigorous, replicable, and trusted research on the use of AI tools in a clinical setting. This should be the foundation on which the mainstream acceptance of AI in health is built. Moreover, with the changes in medical device regulation in the EU and the UK in the coming years, robust clinical evidence won’t just be a ‘nice-to-have’, but increasingly a regulatory requirement for medical AI tools. Therefore, these guidelines elevate clinical AI research to the status of ‘normal’ clinical research, by demanding the same high standards of reporting and transparency. An important milestone for health AI While it may take some time for the full impact of these guidelines to be felt in frontline practices, the clinical AI community should be hugely excited about this development. It could signal a watershed moment for the field: acceptance of clinical AI research as part of mainstream clinical research. For some others in the healthcare community, this may seem very far from their day-to-day work, but I hope they will appreciate the efforts from many of us working on the use of AI in healthcare to be genuinely scientifically robust in our approach. Those of us who work in AI for health need to be honest with ourselves – the time for hype is over. We should be focused on interventions that can demonstrably improve users’ health outcomes and healthcare experiences, and interventions that support the effectiveness of healthcare professionals. The only way we can know which those interventions are - and, in doing so, counter the quite understandable skepticism from those in the medical community who worry about these technologies being ‘unproven’ or ‘untested’ – is if we conduct good, strong clinical research. By standardizing the way we conduct clinical research in AI, we can compare different tools, helping clinicians and patients decide which is the best tool for them to use in a particular situation. Of course, to make the most of these new guidelines the community will need to use them in the right way. What’s more, and as with any guidelines, regulation or standards developed for fast-moving technologies like AI, these guidelines will need to be regularly reviewed and updated to ensure that they remain accurate and relevant to the ever-increasing capability and applications of AI. Open collaboration is critical At Ada, we’ve always had a focus on high medical quality and robust clinical assessment. These are both essential to develop safe products that are acceptable to patients and healthcare professionals. We are also demonstrably committed to cross-industry collaboration to advance the AI sector as a whole. Alongside our modest contribution to the newly updated reporting guidelines, Ada is leading the development of a framework for standardizing the measurement of symptom assessment performance as part of a larger WHO/ITU initiative on AI for Health. I’m also part of a group of experts convened by the World Economic Forum that is working to develop guidelines for the use of conversational agents in healthcare. My focus as Medical Safety Lead at Ada is on applying the latest thinking on how we develop safe digital health products rapidly and scalably, bringing together the best of software development and healthcare. If robustly developed, rigorously and continuously reviewed, and thoughtfully applied, AI tools have immense potential to deliver better health outcomes for users and healthcare professionals alike. Guidelines like the CONSORT and SPIRIT statements are powerful tools to help guide this process, and I hope that the rest of the field will join us in embracing them so that user trust in digital health products can continue to grow. Source: https://ada.com/editorial/what-do-the-new-consort-and-spirit-guidelines-mean-for-health-ai/

Following Current Health’s partnership with CGM leader, Dexcom, our CMO, Dr Adam Wolfberg joined endocrinologist, Dr Egils Bogdanovics to host a webinar detailing the benefits of our newly combined platform. If you are interested in learning how continuous glucose data can be used in remote monitoring of patients with diabetes, here are the key discussion points from this webinar: Lack of clinical insight into patient recovery can result in hospital readmission Recent research indicates 1 in 5 unplanned hospital readmissions within 30-days involve a patient who has diabetes either as a primary or coexisting condition. This challenge exists due to added complexity glucose instability adds during post-discharge care. Furthermore, when these patients get home, there hasn’t historically been an easy way to monitor at-home glucose readings, medication adherence, insulin intake, diet and exercise, making it hard to identify and solve preventable problems. Diabetes and CHF present further healthcare challenges at home It is reported that the prevalence of heart failure in patients with type 2 diabetes is four times greater compared with the general population. While both conditions are individually associated with considerable morbidity and mortality, the combination worsens adverse outcomes, quality of life and cost of treatment. In fact, the risk of hospitalization for heart failure patients with diabetes is 50% greater compared to patients with a single diagnosis. One of the key contributors to this is poor diabetic control, therefore, to address this challenge, it’s essential that health providers deliver acute care at home to help maintain safe glucose levels. A continuous and multi-vital sign monitoring solution can significantly improve post-discharge care With the addition of Dexcom’s G6 continuous glucose monitor to Current Health’s platform, healthcare teams now have access to patients’ blood glucose levels alongside their core vitals data, weight, food logs and self-reported symptom information – all of which are captured from the patient when they are at home. With this integrated approach, clinicians gain the broadest possible picture of patient health allowing them to track patients’ recovery without having to wait until their follow up appointment, which can often be many weeks following discharge from hospital. Moreover, with continuous vitals and blood glucose capture, clinicians can identify health trends far sooner compared to spot-check based solutions. This permits proactive adjustments to be made to a patient’s post-acute care regimen which is highly beneficial when monitoring patients where changes to health status can happen rapidly. Multi-parameter capture is also particularly beneficial when monitoring patients with multiple chronic conditions. For example, when monitoring patients with CHF and diabetes, teams can track CHF exacerbation symptoms such as weight increase, shortness of breath and hypertension in addition to their blood glucose levels. With real-time insight into patient health, clinicians can spot deterioration early and intervene to avoid serious health issues By spotting the early signs of health deterioration such as pulse and respiratory rate changes and derangement in glucose, serious hospitalization events such as diabetic ketoacidosis or hypoglycemia can be avoided. Interventions delivered by clinicians to stabilise their patient’s condition can include changing medications, adjusting insulin doses or engaging with the patient through video calls to make lifestyle and diet recommendations. By intervening in this way, re-hospitalization is avoided and clinical outcomes are improved. Source: https://currenthealth.com/the-role-of-continuous-glucose-data-in-remote-patient-monitoring

agilon health is pleased to announce it has entered into an agreement under which a Babylon Health-associated practice will purchase the Fresno and Madera operations of First Choice Medical Group, inclusive of its associated lines of business (Medi-Cal and Medicare Advantage). Babylon is a globally-leading technology company with the ambitious mission to put accessible and affordable health services in the hands of every person on Earth. Babylon works with governments, health providers and insurers across the globe, and supports healthcare facilities from small local practices to large hospitals; providing digital healthcare tools designed to empower people with knowledge about their health. Babylon covers 20 million people across the globe, and has delivered more than 8 million virtual consultations and AI interactions. Babylon has partnered with 170 impactful worldwide partners, including the NHS in the UK, Telus Health, Mount Sinai Health Partners, the Bill & Melinda Gates Foundation and the Government of Rwanda, to fulfill its vision of accessible and affordable healthcare, for all. Both organizations are enthusiastic about the opportunity to simultaneously broaden Babylon’s footprint while enabling agilon health to focus on its physician partnership model for Medicare Advantage members outside of California. This will enable a seamless transition of continued high caliber support for our existing Central California patients and provider networks with the additional benefit of a unique organization that focuses on advanced technology solutions. We believe this transition serves the well-being of our patients and best interests of all impacted stakeholders. To ensure a smooth transition for patients and providers, there will be an estimated operational transition period of up to six months following the effective date, during which agilon health will continue to provide market facing and MSO functions, including claims processing and utilization management support. Subsequently, Babylon will perform back-office functions currently performed by agilon. Source: https://www.firstchoicemg.com